arXiv subjects

Peng Zhou

Publications and source records attributed to Peng Zhou.

At least 163 records · Page 9Linked to original sources

arXiv2020

Ultrafast non-volatile flash memory based on van der Waals heterostructures

Flash memory has become a ubiquitous solid-state memory device, it is widely used in portable digital devices, computers, and enterprise applications. The development of the information age has put forward higher requirements for memory speed and retention performance. Here, we demonstrate an ultrafast non-volatile memory based on MoS2/h-BN/multi-layer graphene (MLG) van der Waals heterostructures, which has an atomic-level flat interface and achieves ultrafast writing/erasing speed (~20 ns), surpassing the reported state-of-the-art flash memory (~100 ns). The ultrafast flash memory could lay the foundation for the next-generation of high-speed non-volatile memory.

physics.app-ph↗

arXiv2020

Inclusive GAN: Improving Data and Minority Coverage in Generative Models

Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images. Yet the equitable allocation of their modeling capacity among subgroups has received less attention, which could lead to potential biases against underrepresented minorities if left uncontrolled. In this work, we first formalize the problem of minority inclusion as one of data coverage, and then propose to improve data coverage by harmonizing adversarial training with reconstructive generation. The experiments show that our method outperforms the existing state-of-the-art methods in terms of data coverage on both seen and unseen data. We develop an extension that allows explicit control over the minority subgroups that the model should ensure to include, and validate its effectiveness at little compromise from the overall performance on the entire dataset. Code, models, and supplemental videos are available at GitHub.

cs.CV↗

arXiv2020

Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition

Recognizing the expressions of partially occluded faces is a challenging computer vision problem. Previous expression recognition methods, either overlooked this issue or resolved it using extreme assumptions. Motivated by the fact that the human visual system is adept at ignoring the occlusion and focus on non-occluded facial areas, we propose a landmark-guided attention branch to find and discard corrupted features from occluded regions so that they are not used for recognition. An attention map is first generated to indicate if a specific facial part is occluded and guide our model to attend to non-occluded regions. To further improve robustness, we propose a facial region branch to partition the feature maps into non-overlapping facial blocks and task each block to predict the expression independently. This results in more diverse and discriminative features, enabling the expression recognition system to recover even though the face is partially occluded. Depending on the synergistic effects of the two branches, our occlusion-adaptive deep network significantly outperforms state-of-the-art methods on two challenging in-the-wild benchmark datasets and three real-world occluded expression datasets.

cs.CV↗

arXiv2020

FastBERT: a Self-distilling BERT with Adaptive Inference Time

Pre-trained language models like BERT have proven to be highly performant. However, they are often computationally expensive in many practical scenarios, for such heavy models can hardly be readily implemented with limited resources. To improve their efficiency with an assured model performance, we propose a novel speed-tunable FastBERT with adaptive inference time. The speed at inference can be flexibly adjusted under varying demands, while redundant calculation of samples is avoided. Moreover, this model adopts a unique self-distillation mechanism at fine-tuning, further enabling a greater computational efficacy with minimal loss in performance. Our model achieves promising results in twelve English and Chinese datasets. It is able to speed up by a wide range from 1 to 12 times than BERT if given different speedup thresholds to make a speed-performance tradeoff.

cs.CL↗

arXiv2020

DeepStrip: High Resolution Boundary Refinement

In this paper, we target refining the boundaries in high resolution images given low resolution masks. For memory and computation efficiency, we propose to convert the regions of interest into strip images and compute a boundary prediction in the strip domain. To detect the target boundary, we present a framework with two prediction layers. First, all potential boundaries are predicted as an initial prediction and then a selection layer is used to pick the target boundary and smooth the result. To encourage accurate prediction, a loss which measures the boundary distance in the strip domain is introduced. In addition, we enforce a matching consistency and C0 continuity regularization to the network to reduce false alarms. Extensive experiments on both public and a newly created high resolution dataset strongly validate our approach.

cs.CV↗

arXiv2020

Reservoir Computing with Planar Nanomagnet Arrays

Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments. This work proposes a novel hardware implementation of a reservoir computer using a planar nanomagnet array. A small nanomagnet reservoir is demonstrated via micromagnetic simulations to be able to identify simple waveforms with 100% accuracy. Planar nanomagnet reservoirs are a promising new solution to the growing need for dedicated neuromorphic hardware.

cs.NE↗

arXiv2020

Quasi-parallel X-ray microbeam obtained using a parabolic monocapillary X-ray lens with an embedded square-shaped lead occluder

A parabolic monocapillary X-ray lens (PMXRL) is designed to effectively constrain a laboratory point X-ray source into a parallel beam. A square-shaped lead occluder (SSLO) is used to block direct X-rays in the PMXRL. To design the PMXRL, we use Python to simulate the conic parameter (p = 0.001 mm) of the lens and then use a drawing machine to draw a corresponding lens (p = 0.000939 mm) with a total length of 60.8 mm. We place the SSLO at the lens inlet for optical testing. The results show that the controlled outgoing beam has a divergence of less than 0.4 mrad in the range of 15-45 mm of the lens outlet, which achieves excellent optical performance in X-ray imaging methodology. The design details are reported in this paper.

physics.optics↗

arXiv2020

Log D-modules and Index theorems

We study log D-modules on smooth log pairs and construct a comparison theorem of log de Rham complexes. The proof uses Sabbah's generalized b-functions. As applications, we deduce a log index theorem and a Riemann-Roch type formula for perverse sheaves on smooth quasi-projective varieties. The log index theorem naturally generalizes the Dubson-Kashiwara index theorem on smooth projective varieties.

math.AG↗

arXiv2019

High-Performance Logic and Memory Devices Based on a Dual-Gated MoS2 Architecture

In this work, we demonstrate a dual-gated (DG) MoS2 field effect transistors (FETs) in which the degraded switching performance of multilayer MoS2 can be compensated by the DG structure. It produces large current density (>100 μA/μm for a monolayer), steep subthreshold swing (SS) (~100 mV/dec for 5 nm thickness), and high on/off current ratio (greater than 107 for 10 nm thickness). Such DG structure not only improves electrostatic control but also provides an extra degree of freedom for manipulating the threshold voltage (VTH) and SS by separately tuning the top and back gate voltages, which are demonstrated in a logic inverter. Dynamic random access memory (DRAM) has a short retention time because of large OFF-state current in the Si MOSFET. Based on our DG MoS2-FETs, and a DRAM unit cell with a long retention time of 1260 ms are realized. A large-scale isolated MoS2 DG-FETs based on CVD-synthesized continuous films is also demonstrated, which shows potential applications for future wafer-scale digital and low-power electronics.

physics.app-ph↗

arXiv2019

A Dual-gate MoS2 Photodetector Based on Interface Coupling Effect

Two-dimensional (2D) transition metal dichalcogenides (TMDs) based photodetectors have shown great potential for the next generation optoelectronics. However, most of the reported MoS2 photodetectors function under the photogating effect originated from the charge-trap mechanism, which is difficult for quantitative control. Such devices generally suffer from a poor compromise between response speed and responsivity (R) and large dark current. Here, a dual-gated (DG) MoS2 phototransistor operating based on the interface coupling effect (ICE) is demonstrated. By simultaneously applying a negative top-gate voltage (VTG) and positive back-gate voltage (VBG) to the MoS2 channel, the photo-generated holes can be effectively trapped in the depleted region under TG. An ultrahigh R of ~1E5 A/W and detectivity (D*) of ~1E14 Jones have been achieved in several devices with different thickness under Pin of 53 uW/cm2 at VTG=-5 V. Moreover, the response time of the DG phototransistor can also be modulated based on the ICE. Based on these systematic measurements of MoS2 DG phototransistors, the results show that the ICE plays an important role in the modulation of photoelectric performances. Our results also pave the way for the future optoelectrical application of 2D TMDs materials and prompt for further investigation in the DG structured phototransistors.

physics.app-ph↗

arXiv2019

MoS$_2$ Dual-gate Transistors with Electrostatically Doped Contacts

Two-dimensional (2D) transition metal dichalcogenides (TMDs) such as molybdenum disulfide (MoS2) have been intensively investigated because of their exclusive physical properties for advanced electronics and optoelectronics. In the present work, we study the MoS2 transistor based on a novel tri-gate device architecture, with dual-gate (Dual-G) in the channel and the buried side-gate (Side-G) for the source/drain regions. All gates can be independently controlled without interference. For a MoS2 sheet with a thickness of 3.6 nm, the Schottky barrier (SB) and non-overlapped channel region can be effectively tuned by electrostatically doping the source/drain regions with Side-G. Thus, the extrinsic resistance can be effectively lowered, and a boost of the ON-state current can be achieved. Meanwhile, the channel control remains efficient under the Dual-G mode, with an ON-OFF current ratio of 3E7 and subthreshold swing of 83 mV/decade. The corresponding band diagram is also discussed to illustrate the device operation mechanism. This novel device structure opens up a new way toward fabrication of high-performance devices based on 2D-TMDs.

physics.app-ph↗

arXiv2019

Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning

Visual aesthetic assessment has been an active research field for decades. Although latest methods have achieved promising performance on benchmark datasets, they typically rely on a large number of manual annotations including both aesthetic labels and related image attributes. In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective. Our motivation is that a suitable feature representation for image aesthetic assessment should be able to distinguish different expert-designed image manipulations, which have close relationships with negative aesthetic effects. To this end, we design two novel pretext tasks to identify the types and parameters of editing operations applied to synthetic instances. The features from our pretext tasks are then adapted for a one-layer linear classifier to evaluate the performance in terms of binary aesthetic classification. We conduct extensive quantitative experiments on three benchmark datasets and demonstrate that our approach can faithfully extract aesthetics-aware features and outperform alternative pretext schemes. Moreover, we achieve comparable results to state-of-the-art supervised methods that use 10 million labels from ImageNet.

cs.CV↗

arXiv2019

K-BERT: Enabling Language Representation with Knowledge Graph

Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge. However, too much knowledge incorporation may divert the sentence from its correct meaning, which is called knowledge noise (KN) issue. To overcome KN, K-BERT introduces soft-position and visible matrix to limit the impact of knowledge. K-BERT can easily inject domain knowledge into the models by equipped with a KG without pre-training by-self because it is capable of loading model parameters from the pre-trained BERT. Our investigation reveals promising results in twelve NLP tasks. Especially in domain-specific tasks (including finance, law, and medicine), K-BERT significantly outperforms BERT, which demonstrates that K-BERT is an excellent choice for solving the knowledge-driven problems that require experts.

cs.CL↗

arXiv2019

Generate, Segment and Refine: Towards Generic Manipulation Segmentation

Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of fake news and misinformation is growing. Current state of the art methods for detecting these manipulated images suffers from the lack of training data due to the laborious labeling process. We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Strong experimental results validate our proposal.

cs.CV↗

arXiv2019

Sionnx: Automatic Unit Test Generator for ONNX Conformance

Open Neural Network Exchange (ONNX) is an open format to represent AI models and is supported by many machine learning frameworks. While ONNX defines unified and portable computation operators across various frameworks, the conformance tests for those operators are insufficient, which makes it difficult to verify if an operator's behavior in an ONNX backend implementation complies with the ONNX standard. In this paper, we present the first automatic unit test generator named Sionnx for verifying the compliance of ONNX implementation. First, we propose a compact yet complete set of rules to describe the operator's attributes and the properties of its operands. Second, we design an Operator Specification Language (OSL) to provide a high-level description for the operator's syntax. Finally, through this easy-to-use specification language, we are able to build a full testing specification which leverages LLVM TableGen to automatically generate unit tests for ONNX operators with much large coverage. Sionnx is lightweight and flexible to support cross-framework verification. The Sionnx framework is open-sourced in the github repository (https://github.com/alibaba/Sionnx).

cs.SE↗

arXiv2019

Gamma II for toric varieties from integrals on T-dual branes and homological mirror symmetry

In this paper we consider the oscillatory integrals on Lefschetz thimbles in the Landau-Ginzburg model as the mirror of a toric Fano manifold. We show these thimbles represent the same relative homology classes as the characteristic cycles of the corresponding constructible sheaves under the equivalence of \cite{GPS18-2}. Then the oscillatory integrals on such thimbles are the same as the integrals on the characteristic cycles and relate to genus $0$ Gromov-Witten descendant potential for $X$, and this leads to a proof of Gamma II conjecture for toric Fano manifolds.

math.SG↗

arXiv2018

Lagrangian Skeleta of Hypersurfaces in $(\mathbb{C}^*)^n$

Let $W(z_1, \cdots, z_n): (\mathbb{C}^*)^n \to \mathbb{C}$ be a Laurent polynomial in $n$ variables, and let $\mathcal{H}$ be a generic smooth fiber of $W$. In \cite{RSTZ} Ruddat-Sibilla-Treumann-Zaslow give a combinatorial recipe for a skeleton for $\mathcal{H}$. In this paper, we show that for a suitable exact symplectic structure on $\mathcal{H}$, the RSTZ-skeleton can be realized as the Liouville Lagrangian skeleton.

math.SG↗

arXiv2018

Sheaf Quantization of Legendrian Isotopy

Let $\{Λ^\infty_t\}$ be an isotopy of Legendrians (possibly singular) in a unit cosphere bundle $S^*M$. Let $Sh(M, Λ^\infty_t)$ be the differential graded (dg) derived category of constructible sheaves on $M$ with singular support at infinity contained in $Λ^\infty_t$. We prove that if the isotopy of Legendrians embeds into an isotopy of Weinstein hypersurfaces, then the categories $Sh(M, Λ^\infty_t)$ are invariant.

math.SG↗