arXiv subjects

Teng Li

Publications and source records attributed to Teng Li.

At least 91 records · Page 5Linked to original sources

arXiv2017

Peak-Piloted Deep Network for Facial Expression Recognition

Objective functions for training of deep networks for face-related recognition tasks, such as facial expression recognition (FER), usually consider each sample independently. In this work, we present a novel peak-piloted deep network (PPDN) that uses a sample with peak expression (easy sample) to supervise the intermediate feature responses for a sample of non-peak expression (hard sample) of the same type and from the same subject. The expression evolving process from non-peak expression to peak expression can thus be implicitly embedded in the network to achieve the invariance to expression intensities. A special purpose back-propagation procedure, peak gradient suppression (PGS), is proposed for network training. It drives the intermediate-layer feature responses of non-peak expression samples towards those of the corresponding peak expression samples, while avoiding the inverse. This avoids degrading the recognition capability for samples of peak expression due to interference from their non-peak expression counterparts. Extensive comparisons on two popular FER datasets, Oulu-CASIA and CK+, demonstrate the superiority of the PPDN over state-ofthe-art FER methods, as well as the advantages of both the network structure and the optimization strategy. Moreover, it is shown that PPDN is a general architecture, extensible to other tasks by proper definition of peak and non-peak samples. This is validated by experiments that show state-of-the-art performance on pose-invariant face recognition, using the Multi-PIE dataset.

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arXiv2016

A Review on Mechanics and Mechanical Properties of 2D Materials - Graphene and Beyond

Since the first successful synthesis of graphene just over a decade ago, a variety of two-dimensional (2D) materials (e.g., transition metal-dichalcogenides, hexagonal boron-nitride, etc.) have been discovered. Among the many unique and attractive properties of 2D materials, mechanical properties play important roles in manufacturing, integration and performance for their potential applications. Mechanics is indispensable in the study of mechanical properties, both experimentally and theoretically. The coupling between the mechanical and other physical properties (thermal, electronic, optical) is also of great interest in exploring novel applications, where mechanics has to be combined with condensed matter physics to establish a scalable theoretical framework. Moreover, mechanical interactions between 2D materials and various substrate materials are essential for integrated device applications of 2D materials, for which the mechanics of interfaces (adhesion and friction) has to be developed for the 2D materials. Here we review recent theoretical and experimental works related to mechanics and mechanical properties of 2D materials. While graphene is the most studied 2D material to date, we expect continual growth of interest in the mechanics of other 2D materials beyond graphene.

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arXiv2016

Strain-induced programmable half-metal and spin-gapless semiconductor in an edge-doped boron nitride nanoribbon

The search for half-metals and spin-gapless semiconductors has attracted extensive attention in material design for spintronics. Existing progress in such a search often requires peculiar atomistic lattice configuration and also lacks active control of the resulting electronic properties. Here we reveal that a boron-nitride nanoribbon with a carbon-doped edge can be made a half-metal or a spin-gapless semiconductor in a programmable fashion. The mechanical strain serves as the on/off switches for functions of half-metal and spin-gapless semiconductor to occur. Our findings shed light on how the edge doping combined with strain engineering can affect electronic properties of two-dimensional materials

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arXiv2015

Reordering GPU Kernel Launches to Enable Efficient Concurrent Execution

Contemporary GPUs allow concurrent execution of small computational kernels in order to prevent idling of GPU resources. Despite the potential concurrency between independent kernels, the order in which kernels are issued to the GPU will significantly influence the application performance. A technique for deriving suitable kernel launch orders is therefore presented, with the aim of reducing the total execution time. Experimental results indicate that the proposed method yields solutions that are well above the 90 percentile mark in the design space of all possible permutations of the kernel launch sequences.

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arXiv2015

Efficient Resource Sharing Through GPU Virtualization on Accelerated High Performance Computing Systems

The High Performance Computing (HPC) field is witnessing a widespread adoption of Graphics Processing Units (GPUs) as co-processors for conventional homogeneous clusters. The adoption of prevalent Single- Program Multiple-Data (SPMD) programming paradigm for GPU-based parallel processing brings in the challenge of resource underutilization, with the asymmetrical processor/co-processor distribution. In other words, under SPMD, balanced CPU/GPU distribution is required to ensure full resource utilization. In this paper, we propose a GPU resource virtualization approach to allow underutilized microprocessors to effi- ciently share the GPUs. We propose an efficient GPU sharing scenario achieved through GPU virtualization and analyze the performance potentials through execution models. We further present the implementation details of the virtualization infrastructure, followed by the experimental analyses. The results demonstrate considerable performance gains with GPU virtualization. Furthermore, the proposed solution enables full utilization of asymmetrical resources, through efficient GPU sharing among microprocessors, while incurring low overhead due to the added virtualization layer.

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arXiv2015

Programmable Extreme Pseudomagnetic Fields in Graphene by a Uniaxial Stretch

Many of the properties of graphene are tied to its lattice structure, allowing for tuning of charge carrier dynamics through mechanical strain. The graphene electro-mechanical coupling yields very large pseudomagnetic fields for small strain fields, up to hundreds of Tesla, which offer new scientific opportunities unattainable with ordinary laboratory magnets. Significant challenges exist in investigation of pseudomagnetic fields, limited by the non-planar graphene geometries in existing demonstrations and the lack of a viable approach to controlling the distribution and intensity of the pseudomagnetic field. Here we reveal a facile and effective mechanism to achieve programmable extreme pseudomagnetic fields with uniform distributions in a planar graphene sheet over a large area by a simple uniaxial stretch. We achieve this by patterning the planar graphene geometry and graphene-based hetero-structures with a shape function to engineer a desired strain gradient. Our method is geometrical, opening up new fertile opportunities of strain engineering of electronic properties of 2D materials in general.

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arXiv2015

Mechanical Control of Graphene on Engineered Pyramidal Strain Arrays

Strain can tune desirable electronic behavior in graphene, but there has been limited progress in controlling strain in graphene devices. In this paper, we study the mechanical response of graphene on substrates patterned with arrays of mesoscale pyramids. Using atomic force microscopy, we demonstrate that the morphology of graphene can be controlled from conformal to suspended depending on the arrangement of pyramids and the aspect ratio of the array. Non-uniform strains in graphene suspended across pyramids are revealed by Raman spectroscopy and supported by atomistic modeling, which also indicates strong pseudomagnetic fields in the graphene. Our results suggest that incorporating mesoscale pyramids in graphene devices is a viable route to achieving strain-engineering of graphene.

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arXiv2015

Pseudomagnetic Fields in a Locally Strained Graphene Drumhead

Recent experiments reveal that a scanning tunneling microscopy (STM) probe tip can generate a highly localized strain field in a graphene drumhead, which in turn leads to pseudomagnetic fields in the graphene that can spatially confine graphene charge carriers in a way similar to a lithographically defined quantum dot (QD). While these experimental findings are intriguing, their further implementation in nanoelectronic devices hinges upon the knowledge of key underpinning parameters, which still remain elusive. In this paper, we first summarize the experimental measurements of the deformation of graphene membranes due to interactions with the STM probe tip and a back gate electrode. We then carry out systematic coarse grained, (CG), simulations to offer a mechanistic interpretation of STM tip-induced straining of the graphene drumhead. Our findings reveal the effect of (i) the position of the STM probe tip relative to the graphene drumhead center, (ii) the sizes of both the STM probe tip and graphene drumhead, as well as (iii) the applied back-gate voltage, on the induced strain field and corresponding pseudomagnetic field. These results can offer quantitative guidance for future design and implementation of reversible and on-demand formation of graphene QDs in nanoelectronics.

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arXiv2015

Crowded Scene Analysis: A Survey

Automated scene analysis has been a topic of great interest in computer vision and cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded scene analysis has attracted much attention. However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. In the past few years, an increasing number of works on crowded scene analysis have been reported, covering different aspects including crowd motion pattern learning, crowd behavior and activity analysis, and anomaly detection in crowds. This paper surveys the state-of-the-art techniques on this topic. We first provide the background knowledge and the available features related to crowded scenes. Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis. We also outline the available datasets for performance evaluation. Finally, some research problems and promising future directions are presented with discussions.

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arXiv2014

Reversible mechanical and electrical properties of ripped graphene

We examine the mechanical properties of graphene devices stretched on flexible elastomer substrates. Using atomic force microscopy, transport measurements, and mechanics simulations, we show that micro-rips form in the graphene during the initial application of tensile strain; however subsequent applications of the same tensile strain elastically open and close the existing rips. Correspondingly, while the initial tensile strain degrades the devices' transport properties, subsequent strain-relaxation cycles affect transport only moderately, and in a largely reversible fashion, yielding robust electrical transport even after partial mechanical failure.

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arXiv2014

Effects of surface compliance and relaxation on the frictional properties of lamellar materials

We describe the results of atomic-level stick-slip friction measurements performed on chemically-modified graphite, using atomic force microscopy (AFM). Through detailed molecular dynamics simulations, coarse-grained simulations, and theoretical arguments, we report on complex indentation profiles during AFM scans involving local reversible exfoliation of the top layer of graphene from the underlying graphite sample and its effect on the measured friction force during retraction of the scanning tip. In particular, we report nearly constant lateral stick-slip magnitudes at decreasing loads, which cannot be explained within the standard framework based on continuum mechanics models for the contact area. We explain this anomalous behavior by introducing the effect of local compliance of the topmost graphene layer, which varies when interaction with the AFM tip is enhanced. Such behavior is not observed for non-lamellar materials. We extend our discussion toward the more general understanding of the effects of the top layer relaxation on the friction force under pushing and pulling loads. Our results may provide a more comprehensive understanding of the effectively negative coefficient of friction recently observed on chemically-modified graphite.

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arXiv2011

Graphene morphology regulated by nanowires patterned in parallel on a substrate surface

The graphene morphology regulated by nanowires patterned in parallel on a substrate surface is quantitatively determined using energy minimization. The regulated graphene morphology is shown to be governed by the nanowire diameter, the nanowire spacing and the interfacial bonding energies between the graphene and the underlying nanowires and substrate. We demonstrate two representative regulated graphene morphologies and determine critical values of the nanowire spacing, nanowire diameter and interfacial bonding energies at which graphene switches between the two representative morphologies. Interestingly, we identify a rule-of-thumb formula that correlates the critical nanowire spacing, the critical interfacial bonding energies and the nanowire diameter in quite well agreement with the full-scale simulation results. Results from the present study offer guidelines in nano-structural design to achieve desired graphene morphology via regulation with a resolution approaching the atomic feature size of graphene.

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arXiv2011

Carbon Nanotube Initiated Formation of Carbon Nanoscrolls

The unique topology and exceptional properties of carbon nanoscrolls (CNSs) have inspired unconventional nano-device concepts, yet the fabrication of CNSs remains rather challenging. Using molecular dynamics simulations, we demonstrate the spontaneous formation of a CNS from graphene on a substrate, initiated by a carbon nanotube (CNT). The rolling of graphene into a CNS is modulated by the CNT size, the carbon-carbon interlayer adhesion, and the graphene-substrate interaction. A phase diagram emerging from the simulations can offer quantitative guideline toward a feasible and robust physical approach to fabricating CNSs.

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arXiv2011

A Molecular Mechanics Study of Morphologic Interaction between Graphene and Si Nanowires on a SiO2 Substrate

In this paper, we study the morphologic interaction between graphene and Si nanowires on a SiO2 substrate, using molecular mechanics simulations. Two cases are considered: 1) a graphene nanoribbon intercalated by a single Si nanowire on a SiO2 substrate and 2) a blanket graphene flake intercalated by an array of Si nanowires evenly patterned in parallel on a SiO2 substrate. Various graphene morphologies emerge from the simulation results of these two cases, which are shown to depend on both geometric parameters (e.g., graphene nanoribbon width, nanowire diameter, and nanowire spacing) and material properties (e.g., graphene-nanowire and graphene-substrate bonding strength). While the quantitative results at the atomistic resolution in this study can be further used to determine the change of electronic properties of graphene under morphologic regulation, the qualitative understandings from this study can be extended to help exploring graphene morphology in other material systems.

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arXiv2011

Determining Graphene Adhesion via Substrate-regulated Morphology of Graphene

Understanding the adhesion between graphene and other materials is crucial for achieving more reliable graphene-based applications in electronic devices and nanocomposites. The ultra-thin profile of graphene, however, poses significant challenge to direct measurement of its adhesion property using conventional approaches. We show that there is a strong correlation between the morphology of graphene on a compliant substrate with patterned surface and the graphene-substrate adhesion. We establish an analytic model to quantitatively determine such a strong correlation. Results show that, depending on the graphene-substrate adhesion, number of graphene layers and substrate stiffness, graphene exhibits two distinct types of morphology: I) graphene remains bonded to the substrate and corrugates to an amplitude up to that of the substrate surface patterns; II) graphene debonds from the substrate and remains flat on top of the substrate surface patterns. The sharp transition between these two types of graphene morphology occurs at a critical adhesion between the graphene and the compliant substrate material. These results potentially open up a feasible pathway to measuring the adhesion property of graphene.

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arXiv2011

Extrinsic morphology of graphene

Graphene is intrinsically non-flat and corrugates randomly. Since the corrugating physics of atomically-thin graphene is strongly tied to its electronics properties, randomly corrugating morphology of graphene poses significant challenge to its application in nanoelectronic devices for which precise (digital) control is the key. Recent studies revealed that the morphology of substrate-supported graphene is regulated by the graphene-substrate interaction, thus is distinct from the random intrinsic morphology of freestanding graphene. The regulated extrinsic morphology of graphene sheds light on new pathways to fine tune the properties of graphene. To guide further research to explore these fertile opportunities, this paper reviews recent progress on modeling and experimental studies of the extrinsic morphology of graphene under a wide range of external regulation, including two dimensional and one dimensional substrate surface features and one dimensional and zero dimensional nanoscale scaffolds (e.g., nanowires and nanoparticles).

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arXiv2011

Ultrafast nano-oscillators based on interlayer-bridged carbon nanoscrolls

We demonstrate a viable approach to fabricating ultrafast axial nano-oscillators based on carbon nanoscrolls (CNSs) using molecular dynamics simulations. Initiated by a single-walled carbon nanotube (CNT), a monolayer graphene can continuously scroll into a CNS with the CNT housed inside. The CNT inside the CNS can oscillate along axial direction at a natural frequency of 10s gigahertz (GHz). We demonstrate an effective strategy to reduce the dissipation of the CNS-based nano-oscillator by covalently bridging the carbon layers in the CNS. We further demonstrate that, such a CNS-based nano-oscillator can be excited and driven by an external AC electric field, and oscillate at more than 100 GHz. The CNS-based nano-oscillators not only offer a feasible pathway toward ultrafast nano-devices, but also hold promise to enable nano-scale energy transduction, harnessing and storage (e.g., from electric to mechanical).

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arXiv2010

Substrate-regulated morphology of graphene

We delineate a general theoretical framework to determine the substrate-regulated graphene morphology through energy minimization. We then apply such a framework to study the graphene morphology on a substrate with periodic surface grooves. Depending on the substrate surface roughness and the graphene-substrate interfacial bonding energy, the equilibrium morphology of graphene ranges from 1) closely conforming to the substrate, to 2) remaining flat on the substrate. Interestingly, in certain cases, the graphene morphology snaps between the above two limiting states. Our quantitative results envision a promising strategy to precisely control the graphene morphology over large areas. The rich features of the substrate-regulated graphene morphology (e.g., the snap-through instability) can potentially lead to new design concepts of functional graphene device components.

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