arXiv subjects

Dawei Li

Publications and source records attributed to Dawei Li.

At least 91 records · Page 5Linked to original sources

arXiv2023

Selective surface modification and layer thinning of MoS2 via ultraviolet light irradiation in ionic solution

The electrical and optoelectronic properties of transition-metal dichalcogenides (TMDs), such as MoS2, are highly dependent on carrier doping and layer thickness. The ability to selectively control these two critical characteristics is of great importance to develop TMD-based multifunctional device applications, which remains challenging. Here, we report a strategy for controllable surface modification and layer thinning of MoS2 via ultraviolet (UV) light irradiation in a silver ionic solution environment. The results show that by adjusting UV irradiation time, nanostructured silver ultrathin films (~2.9 nm) are uniformly deposited on monolayer MoS2 and can lead to controllable p-type doping effect, while the thickness of MoS2 from few-layer to bulk crystals could be thinned down to the atomic monolayer limit. Both silver nanostructure deposition and layer thinning process have been evidenced to initiate from the edges of MoS2, and independent of the edge type, thus revealing a unique UV light-assisted defect-induced surface modification and layer thinning mechanism. Overall, this study provides a new methodology for selective control of doping and layer thickness in TMDs, paving the way for developing novel 2D nanoelectronics and integrated optoelectronics.

cond-mat.mtrl-sci↗

arXiv2022

Domain Wall Enabled Steep Slope Switching in MoS$_2$ Transistors Towards Hysteresis-Free Operation

The device concept of ferroelectric-based negative capacitance (NC) transistors offers a promising route for achieving energy-efficient logic applications that can outperform the conventional semiconductor technology, while viable operation mechanisms remain a central topic of debate. In this work, we report steep slope switching in MoS$_2$ transistors back-gated by single-layer polycrystalline PbZr$_{0.35}$Ti$_{0.65}$O$_3$. The devices exhibit current on/off ratios up to 8$\times$10$^6$ within an ultra-low gate voltage window of $V_g$ = $\pm$0.5 V and subthreshold swing (SS) as low as 9.7 mV decade$^{-1}$ at room temperature, transcending the 60 mV decade$^{-1}$ Boltzmann limit without involving additional dielectric layers. Theoretical modeling reveals the dominant role of the metastable polar states within domain walls in enabling the NC mode, which is corroborated by the relation between SS and domain wall density. Our findings shed light on a hysteresis-free mechanism for NC operation, providing a simple yet effective material strategy for developing low-power 2D nanoelectronics.

physics.app-ph↗

arXiv2022

Fine-grained Contrastive Learning for Definition Generation

Recently, pre-trained transformer-based models have achieved great success in the task of definition generation (DG). However, previous encoder-decoder models lack effective representation learning to contain full semantic components of the given word, which leads to generating under-specific definitions. To address this problem, we propose a novel contrastive learning method, encouraging the model to capture more detailed semantic representations from the definition sequence encoding. According to both automatic and manual evaluation, the experimental results on three mainstream benchmarks demonstrate that the proposed method could generate more specific and high-quality definitions compared with several state-of-the-art models.

cs.CL↗

arXiv2022

Robust Coordinated Longitudinal Control of MAV Based on Energy State

Fixed-wing Miniature Air Vehicle (MAV) is not only coupled with longitudinal motion, but also more susceptible to wind disturbance due to its lighter weight, which brings more challenges to its altitude and airspeed controller design. Therefore, in this paper, an improved longitudinal control strategy based on energy state, is proposed to address the above-mentioned issues. The control strategy utilizes the Linear Extended State Observer (LESO) to observe the energy states and the disturbance of the MAV, and then designs a Multiple-Input Multiple-Output (MIMO) controller based on a more coordinated Total Energy Control (TEC) strategy to control the airspeed and altitude of the MAV. The performance of this control strategy has been successfully verified in a Model-in-the-Loop (MIL) simulation with Simulink, and a comparative test with the classical TEC algorithm is carried out.

eess.SY↗

arXiv2022

Tunable photochemical deposition of silver nanostructures on layered ferroelectric CuInP$_2$S6

2D layered ferroelectric materials such as CuInP$_2$S6 (CIPS) are promising candidates for novel and high-performance photocatalysts, owning to their ultrathin layer thickness, strong interlayer coupling, and intrinsic spontaneous polarization, while how to control the photocatalytic activity in layered CIPS remains unexplored. In this work, we report for the first time the photocatalytic activity of ferroelectric CIPS for the chemical deposition of silver nanostructures (AgNSs). The results show that the shape and spatial distribution of AgNSs on CIPS are tunable by controlling layer thickness, environmental temperature, and light wavelength. The ferroelectric polarization in CIPS plays a critical role in tunable AgNS photodeposition, as evidenced by layer thickness and temperature dependence experiments. We further reveal that AgNS photodeposition process starts from the active site creation, selective nanoparticle nucleation/aggregation, to the continuous film formation. Moreover, AgNS/CIPS heterostructures prepared by photodeposition exhibit excellent resistance switching behavior and good surface enhancement Raman Scattering activity. Our findings provide new insight into the photocatalytic activity of layered ferroelectrics and offer a new material platform for advanced functional device applications in smart memristors and enhanced chemical sensors.

cond-mat.mtrl-sci↗

arXiv2022

C3KG: A Chinese Commonsense Conversation Knowledge Graph

Existing commonsense knowledge bases often organize tuples in an isolated manner, which is deficient for commonsense conversational models to plan the next steps. To fill the gap, we curate a large-scale multi-turn human-written conversation corpus, and create the first Chinese commonsense conversation knowledge graph which incorporates both social commonsense knowledge and dialog flow information. To show the potential of our graph, we develop a graph-conversation matching approach, and benchmark two graph-grounded conversational tasks.

cs.CL↗

arXiv2021

Giant Transport Anisotropy in ReS$_2$ Revealed via Nanoscale Conducting Path Control

The low in-plane symmetry in layered 1T'-ReS$_2$ results in strong band anisotropy, while its manifestation in the electronic properties is challenging to resolve due to the lack of effective approaches for controlling the local current path. In this work, we reveal the giant transport anisotropy in monolayer to four-layer ReS$_2$ by creating directional conducting paths via nanoscale ferroelectric control. By reversing the polarization of a ferroelectric polymer top layer, we induce conductivity switching ratio of >1.5x10$^8$ in the ReS$_2$ channel at 300 K. Characterizing the domain-defined conducting nanowires in an insulating background shows that the conductivity ratio between the directions along and perpendicular to the Re-chain can exceed 5.5x10$^4$. Theoretical modeling points to the band origin of the transport anomaly, and further reveals the emergence of a flat band in few-layer ReS$_2$. Our work paves the path for implementing the highly anisotropic 2D materials for designing novel collective phenomena and electron lensing applications.

cond-mat.mes-hall↗

arXiv2021

On the Benefit of Width for Neural Networks: Disappearance of Bad Basins

Wide networks are often believed to have a nice optimization landscape, but what rigorous results can we prove? To understand the benefit of width, it is important to identify the difference between wide and narrow networks. In this work, we prove that from narrow to wide networks, there is a phase transition from having sub-optimal basins to no sub-optimal basins. Specifically, we prove two results: on the positive side, for any continuous activation functions, the loss surface of a class of wide networks has no sub-optimal basins, where "basin" is defined as the set-wise strict local minimum; on the negative side, for a large class of networks with width below a threshold, we construct strict local minima that are not global. These two results together show the phase transition from narrow to wide networks.

cs.LG↗

arXiv2021

Mechanical Properties of Gradient Copper Nano-Gyroid Cellular Structures: A Molecular Dynamics Study

Advanced manufacturing (AM) technologies, such as nanoscale additive manufacturing process, enable the fabrication of nanoscale architected materials which has received great attention due to their prominent properties. However, few studies delve into the functional gradient cellular architecture on nanoscale. This work studied the gradient nano-Gyroid architected material made of copper (Cu) by molecular dynamic (MD) simulations. The result reveals that, unlike homogeneous architecture, gradient Gyroid not only shows novel layer-by-layer deformation behavior, but also processes significantly better energy absorption ability. Moreover, this deformation behavior and energy absorption are predictable and designable, which demonstrates its highly programmable potential.

physics.app-ph↗

arXiv2021

Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss

Cyber attacks pose crucial threats to computer system security, and put digital treasuries at excessive risks. This leads to an urgent call for an effective intrusion detection system that can identify the intrusion attacks with high accuracy. It is challenging to classify the intrusion events due to the wide variety of attacks. Furthermore, in a normal network environment, a majority of the connections are initiated by benign behaviors. The class imbalance issue in intrusion detection forces the classifier to be biased toward the majority/benign class, thus leave many attack incidents undetected. Spurred by the success of deep neural networks in computer vision and natural language processing, in this paper, we design a new system named DeepIDEA that takes full advantage of deep learning to enable intrusion detection and classification. To achieve high detection accuracy on imbalanced data, we design a novel attack-sharing loss function that can effectively move the decision boundary towards the attack classes and eliminates the bias towards the majority/benign class. By using this loss function, DeepIDEA respects the fact that the intrusion mis-classification should receive higher penalty than the attack mis-classification. Extensive experimental results on three benchmark datasets demonstrate the high detection accuracy of DeepIDEA. In particular, compared with eight state-of-the-art approaches, DeepIDEA always provides the best class-balanced accuracy.

cs.CR↗

arXiv2021

A prognostic dynamic model applicable to infectious diseases providing easily visualized guides -- A case study of COVID-19 in the UK

A reasonable prediction of infectious diseases transmission process under different disease control strategies is an important reference point for policy makers. Here we established a dynamic transmission model via Python and realized comprehensive regulation of disease control measures. We classified government interventions into three categories and introduced three parameters as descriptions for the key points in disease control, these being intraregional growth rate, interregional communication rate, and detection rate of infectors. Our simulation predicts the infection by COVID-19 in the UK would be out of control in 73 days without any interventions; at the same time, herd immunity acquisition will begin from the epicentre. After we introduced government interventions, single intervention is effective in disease control but at huge expense while combined interventions would be more efficient, among which, enhancing detection number is crucial in control strategy of COVID-19. In addition, we calculated requirements for the most effective vaccination strategy based on infection number in real situation. Our model was programmed with iterative algorithms, and visualized via cellular automata, it can be applied to similar epidemics in other regions if the basic parameters are inputted, and is able to synthetically mimick the effect of multiple factors in infectious disease control.

q-bio.PE↗

arXiv2021

On a Faster $R$-Linear Convergence Rate of the Barzilai-Borwein Method

The Barzilai-Borwein (BB) method has demonstrated great empirical success in nonlinear optimization. However, the convergence speed of BB method is not well understood, as the known convergence rate of BB method for quadratic problems is much worse than the steepest descent (SD) method. Therefore, there is a large discrepancy between theory and practice. To shrink this gap, we prove that the BB method converges $R$-linearly at a rate of $1-1/κ$, where $κ$ is the condition number, for strongly convex quadratic problems. In addition, an example with the theoretical rate of convergence is constructed, indicating the tightness of our bound.

math.OC↗

arXiv2020

Sub-Optimal Local Minima Exist for Neural Networks with Almost All Non-Linear Activations

Does over-parameterization eliminate sub-optimal local minima for neural networks? An affirmative answer was given by a classical result in [59] for 1-hidden-layer wide neural networks. A few recent works have extended the setting to multi-layer neural networks, but none of them has proved every local minimum is global. Why is this result never extended to deep networks? In this paper, we show that the task is impossible because the original result for 1-hidden-layer network in [59] can not hold. More specifically, we prove that for any multi-layer network with generic input data and non-linear activation functions, sub-optimal local minima can exist, no matter how wide the network is (as long as the last hidden layer has at least two neurons). While the result of [59] assumes sigmoid activation, our counter-example covers a large set of activation functions (dense in the set of continuous functions), indicating that the limitation is not due to the specific activation. Our result indicates that "no bad local-min" may be unable to explain the benefit of over-parameterization for training neural nets.

cs.LG↗

arXiv2020

The Global Landscape of Neural Networks: An Overview

One of the major concerns for neural network training is that the non-convexity of the associated loss functions may cause bad landscape. The recent success of neural networks suggests that their loss landscape is not too bad, but what specific results do we know about the landscape? In this article, we review recent findings and results on the global landscape of neural networks. First, we point out that wide neural nets may have sub-optimal local minima under certain assumptions. Second, we discuss a few rigorous results on the geometric properties of wide networks such as "no bad basin", and some modifications that eliminate sub-optimal local minima and/or decreasing paths to infinity. Third, we discuss visualization and empirical explorations of the landscape for practical neural nets. Finally, we briefly discuss some convergence results and their relation to landscape results.

cs.LG↗

arXiv2020

Polar Coupling Enabled Nonlinear Optical Filtering at MoS$_2$/Ferroelectric Heterointerfaces

Complex oxide heterointerfaces and van der Waals heterostructures present two versatile but intrinsically different platforms for exploring emergent quantum phenomena and designing new functionalities. The rich opportunity offered by the synergy between these two classes of materials, however, is yet to be charted. Here, we report an unconventional nonlinear optical filtering effect resulting from the interfacial polar alignment between monolayer MoS$_2$ and a neighboring ferroelectric oxide thin film. The second harmonic generation response at the heterointerface is either substantially enhanced or almost entirely quenched by an underlying ferroelectric domain wall depending on its chirality, and can be further tailored by the polar domains. Unlike the extensively studied coupling mechanisms driven by charge, spin, and lattice, the interfacial tailoring effect is solely mediated by the polar symmetry, as well explained via our density functional theory calculations, pointing to a new material strategy for the functional design of nanoscale reconfigurable optical applications.

cond-mat.mes-hall↗

arXiv2020

An Improved Quadrature Voltage-Controlled Oscillator with Through-Silicon-Via Inductor in Three-dimensional Integrated Circuits

Low-power quadrature voltage-controlled oscillator (QVCO) design utilizing transformer-feedback and current-reuse techniques with increased frequency range is proposed in this paper. With increasing demand for QVCOs in on-chip applications, the conventional spiral inductor based approaches for QVCOs has become a major bottleneck due to their large size. To address this concern, we propose to replace the conventional spiral inductor based approaches with through-silicon-via (TSV) inductor based approach in three-dimensional integrated circuits (3D ICs). In addition, the proposed QVCO circuit can provide higher frequency range of operation compared with conventional designs. Experimental results show by replacing conventional spiral transformers with TSV transformers, up to 3.9x reduction in metal resource consumption. The proposed QVCOs achieves a phase noise of -114 $dBc/Hz$@1 $MHz$ and -111.2 $dBc/Hz$@1 $MHz$ at the carrier of 2.5 $GHz$ for toroidal TSV transformed based-QVCO and vertical spiral transformer based-QVCO respectively. The power consumption is only 1.5 $mW$ and 1.7 $mW$ for toroidal TSV transformed based-QVCO and vertical spiral transformer based-QVCO respectively, under the supply voltage of 0.7 $V$.

eess.SY↗

arXiv2020

Class-incremental Learning via Deep Model Consolidation

Deep neural networks (DNNs) often suffer from "catastrophic forgetting" during incremental learning (IL) --- an abrupt degradation of performance on the original set of classes when the training objective is adapted to a newly added set of classes. Existing IL approaches tend to produce a model that is biased towards either the old classes or new classes, unless with the help of exemplars of the old data. To address this issue, we propose a class-incremental learning paradigm called Deep Model Consolidation (DMC), which works well even when the original training data is not available. The idea is to first train a separate model only for the new classes, and then combine the two individual models trained on data of two distinct set of classes (old classes and new classes) via a novel double distillation training objective. The two existing models are consolidated by exploiting publicly available unlabeled auxiliary data. This overcomes the potential difficulties due to the unavailability of original training data. Compared to the state-of-the-art techniques, DMC demonstrates significantly better performance in image classification (CIFAR-100 and CUB-200) and object detection (PASCAL VOC 2007) in the single-headed IL setting.

cs.CV↗

arXiv2019

Rapid Identification of X-ray Diffraction Spectra Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretic data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) spectra of metal-organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental spectra and shuffled, then merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. The optimized model showed the highest identification accuracy of 96.7% for the Top 5 ranking among a dataset of 1012 MOFs. Neighborhood components analysis (NCA) on the experimental XRD spectra shows that the spectra from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD spectra. This CNN model trained by the data-augmentation technique would not only open numerous potential applications for identifying XRD spectra for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR.

physics.data-an↗