Model-Free Sure Screening via Maximum Correlation

We consider the problem of screening features in an ultrahigh-dimensional setting. Using maximum correlation, we develop a novel procedure called MC-SIS for feature screening, and show that MC-SIS possesses the sure screen property without imposing model or distributional assumptions on the response and predictor variables. Therefore, MC-SIS is a model-free sure independence screening method as in contrast with some other existing model-based sure independence screening methods in the literature. Simulation examples and a real data application are used to demonstrate the performance of MC-SIS as well as to compare MC-SIS with other existing sure screening methods. The results show that MC-SIS outperforms those methods when their model assumptions are violated, and it remains competitive when the model assumptions hold.Comment: 38 pages, 5 table

Holomorphic Curves into Algebraic Varieties Intersecting Divisors in Subgeneral Position

Recently, there are many developments on the second main theorem for holomorphic curves into algebraic varieties intersecting divisors in general position or subgeneral position. In this paper, we refine the concept of subgeneral position by introducing the notion of the index of subgeneral position. With this new notion we give some surprising improvement of the previous known second main theorem type results. Moreover, via the analogue between Nevanlinna theory and Diophantine approximation, the corresponding Schmidt's subspace type theorems are also established in the final section.Comment: to appear in Math. An

Computation Load Balancing Real-Time Model Predictive Control in Urban Traffic Networks

Owing to the rapid growth number of vehicles, urban traffic congestion has become more and more severe in the last decades. As an effective approach, Model Predictive Control (MPC) has been applied to urban traffic signal control system. However, the potentially high online computation burden may limit its further application for real scenarios. In this paper, a new approach based on online active set strategy is proposed to improve the real-time performance of MPC-based traffic controller by reducing the online computing time. This approach divides one control cycle into several sequential sampling intervals. In each interval, online active set method is applied to solve quadratic programming (QP) of traffic signal control model, by searching the optimal solution starting at the optimal solution of previous interval in the feasible region. The most appealing property of this approach lies in that it can distribute the computational complexity into several sample intervals, instead of imposing heavy computation burden at each end of control cycle. The simulation experiments show that this breakthrough approach can obviously reduce the online computational complexity, and increase the applicability of the MPC in real-life traffic networks

Sample-Efficient Policy Learning based on Completely Behavior Cloning

Direct policy search is one of the most important algorithm of reinforcement learning. However, learning from scratch needs a large amount of experience data and can be easily prone to poor local optima. In addition to that, a partially trained policy tends to perform dangerous action to agent and environment. In order to overcome these challenges, this paper proposed a policy initialization algorithm called Policy Learning based on Completely Behavior Cloning (PLCBC). PLCBC first transforms the Model Predictive Control (MPC) controller into a piecewise affine (PWA) function using multi-parametric programming, and uses a neural network to express this function. By this way, PLCBC can completely clone the MPC controller without any performance loss, and is totally training-free. The experiments show that this initialization strategy can help agent learn at the high reward state region, and converge faster and better

Compact Model of Nanowire Tunneling FETs Including Phonon-Assisted Tunneling and Quantum Capacitance

A physics-based compact model for silicon gate-all-around (GAA) nanowire tunneling FETs (NW-tFETs) with good accuracy has been developed by considering Phonon-Assisted Tunneling (PAT) and transition from Quantum Capacitance Limit (QCL) to Classical Limit (CL) during the device-size scaling. The impact of PAT results in the broadening of a single electron-energy level to an energy band with density-of-states (DOS) distribution of Lorentzian shape. As a consequence, the tunneling probability at the edge of tunneling window no longer changes abruptly from zero to having a finite value. By adjusting the parameters in the Lorentzian function, an accurate fitting to the measured transfer characteristics in the subthreshold region is made possible. Besides, with an analytical formula to calculate the channel potential, the model is able to cover naturally the transition from QCL to CL regime when the device size is scaled. Furthermore, on-voltage is defined to facilitate the modeling and fitting processes. Comparisons with the experimental data demonstrate the model accuracy across all device operation regions and the flexibility in model parameter extraction is also shown

Structured Pruning for Efficient ConvNets via Incremental Regularization

Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance degrade. Despite its effectiveness, existing regularization-based parameter pruning methods usually drive weights towards zero with large and constant regularization factors, which neglects the fragility of the expressiveness of CNNs, and thus calls for a more gentle regularization scheme so that the networks can adapt during pruning. To achieve this, we propose a new and novel regularization-based pruning method, named IncReg, to incrementally assign different regularization factors to different weights based on their relative importance. Empirical analysis on CIFAR-10 dataset verifies the merits of IncReg. Further extensive experiments with popular CNNs on CIFAR-10 and ImageNet datasets show that IncReg achieves comparable to even better results compared with state-of-the-arts. Our source codes and trained models are available here: https://github.com/mingsun-tse/caffe_increg.Comment: IJCNN 201

Unitary-Coupled Restricted Boltzmann Machine Ansatz for Quantum Simulations

Neural-Network Quantum State (NQS) has attracted significant interests as a powerful wave-function ansatz to model quantum phenomena. In particular, a variant of NQS based on the restricted Boltzmann machine (RBM) has been adapted to model the ground state of spin lattices and the electronic structures of small molecules in quantum devices. Despite these progresses, significant challenges remain with the RBM-NQS based quantum simulations. In this work, we present a state-preparation protocol to generate a specific set of complex-valued RBM-NQS, that we name the unitary-coupled RBM-NQS, in quantum circuits. This is a crucial advancement as all prior works deal exclusively with real-valued RBM-NQS for quantum algorithms. With this novel scheme, we achieve (1) modeling complex-valued wave functions, (2) using as few as one ancilla qubit to simulate $M$ hidden spins in an RBM architecture, and (3) avoiding post-selections to improve scalability.Comment: 21 pages, 6 figure

Deficiency of the Bulk Spin Hall Effect Model for Spin-Orbit Torques in Magnetic Insulator/Heavy Metal Heterostructures

Electrical currents in a magnetic insulator/heavy metal heterostructure can induce two simultaneous effects, namely, spin Hall magnetoresistance (SMR) on the heavy metal side and spin-orbit torques (SOTs) on the magnetic insulator side. Within the framework of the pure spin current model based on the bulk spin Hall effect (SHE), the ratio of the spin Hall-induced anomalous Hall effect (SH-AHE) to SMR should be equal to the ratio of the field-like torque (FLT) to damping-like torque (DLT). We perform a quantitative study of SMR, SH-AHE, and SOTs in a series of thulium iron garnet/platinum or Tm3Fe5O12/Pt heterostructures with different Tm3Fe5O12 thicknesses, where Tm3Fe5O12 is a ferrimagnetic insulator with perpendicular magnetic anisotropy. We find the ratio between measured effective fields of FLT and DLT is at least 2 times larger than the ratio of the SH-AHE to SMR. In addition, the bulk SHE model grossly underestimates the spin torque efficiency of FLT. Our results reveal deficiencies of the bulk SHE model and also address the importance of interfacial effects such as the Rashba and magnetic proximity effects in magnetic insulator/heavy metal heterostructures

Strong Rashba-Edelstein Effect-Induced Spin-Orbit Torques in Monolayer Transition-Metal Dichalcogenide/Ferromagnet Bilayers

The electronic and optoelectronic properties of two dimensional materials have been extensively explored in graphene and layered transition metal dichalcogenides (TMDs). Spintronics in these two-dimensional materials could provide novel opportunities for future electronics, for example, efficient generation of spin current, which should enable the efficient manipulation of magnetic elements. So far, the quantitative determination of charge current induced spin current and spin-orbit torques (SOTs) on the magnetic layer adjacent to two-dimensional materials is still lacking. Here, we report a large SOT generated by current-induced spin accumulation through the Rashba-Edelstein effect in the composites of monolayer TMD (MoS$_2$ or WSe$_2$)/CoFeB bilayer. The effective spin conductivity corresponding to the SOT turns out to be almost temperature-independent. Our results suggest that the charge-spin conversion in the chemical vapor deposition-grown large-scale monolayer TMDs could potentially lead to high energy efficiency for magnetization reversal and convenient device integration for future spintronics based on two-dimensional materials.Comment: accepted versio

Deep Learning for Optoelectronic Properties of Organic Semiconductors

Atomistic modeling of energetic disorder in organic semiconductors (OSCs) and its effects on the optoelectronic properties of OSCs requires a large number of excited-state electronic-structure calculations, a computationally daunting task for many OSC applications. In this work, we advocate the use of deep learning to address this challenge and demonstrate that state-of-the-art deep neural networks (DNNs) are capable of predicting the electronic properties of OSCs at an accuracy comparable with the quantum chemistry methods used for generating training data. We extensively investigate the performances of four recent DNNs (deep tensor neural network, SchNet, message passing neural network, and multilevel graph convolutional neural network) in predicting various electronic properties of an important class of OSCs, i.e., oligothiophenes (OTs), including their HOMO and LUMO energies, excited-state energies and associated transition dipole moments. We find that SchNet shows the best performance for OTs of different sizes (from bithiophene to sexithiophene), achieving average prediction errors in the range of 20-80meV compared to the results from (time-dependent) density functional theory. We show that SchNet also consistently outperforms shallow feed-forward neural networks, especially in difficult cases with large molecules or limited training data. We further show that SchNet could predict the transition dipole moment accurately, a task previously known to be difficult for feed-forward neural networks, and we ascribe the relatively large errors in transition dipole prediction seen for some OT configurations to the charge-transfer character of their excited states. Finally, we demonstrate the effectiveness of SchNet by modeling the UV-Vis absorption spectra of OTs in dichloromethane and a good agreement is observed between the calculated and experimental spectra.Comment: comments are welcom