arXiv subjects

Kai Cheng

Publications and source records attributed to Kai Cheng.

49 records · Page 3Linked to original sources

arXiv2022

Importance of magnetic shape anisotropy in determining magnetic and electronic properties of monolayer $\mathrm{VSi_2P_4}$

Two-dimensional (2D) ferromagnets have been a fascinating subject of research, and magnetic anisotropy (MA) is indispensable for stabilizing the 2D magnetic order. Here, we investigate magnetic anisotropy energy (MAE), magnetic and electronic properties of $\mathrm{VSi_2P_4}$ by using the generalized gradient approximation plus $U$ (GGA+$U$) approach. For large $U$, the magnetic shape anisotropy (MSA) energy has a more pronounced contribution to the MAE, which can overcome the magnetocrystalline anisotropy (MCA) energy to evince an easy-plane. For fixed out-of-plane MA, monolayer $\mathrm{VSi_2P_4}$ undergoes ferrovalley (FV), half-valley-metal (HVM), valley-polarized quantum anomalous Hall insulator (VQAHI), HVM and FV states with increasing $U$. However, for assumptive in-plane MA, there is no special quantum anomalous Hall (QAH) state and spontaneous valley polarization within considered $U$ range. According to the MAE and electronic structure with fixed out-of-plane or in-plane MA, the intrinsic phase diagram shows common magnetic semiconductor (CMS), FV and VQAHI in monolayer $\mathrm{VSi_2P_4}$. At representative $U$$=$3 eV widely used in references, $\mathrm{VSi_2P_4}$ can be regarded as a 2D-$XY$ magnet, not Ising-like 2D long-range order magnets predicted in previous works with only considering MCA energy. Our findings shed light on importance of MSA in determining magnetic and electronic properties of monolayer $\mathrm{VSi_2P_4}$.

cond-mat.mtrl-sci↗

arXiv2022

Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation

Multi-view depth estimation plays a critical role in reconstructing and understanding the 3D world. Recent learning-based methods have made significant progress in it. However, multi-view depth estimation is fundamentally a correspondence-based optimization problem, but previous learning-based methods mainly rely on predefined depth hypotheses to build correspondence as the cost volume and implicitly regularize it to fit depth prediction, deviating from the essence of iterative optimization based on stereo correspondence. Thus, they suffer unsatisfactory precision and generalization capability. In this paper, we are the first to explore more general image correlations to establish correspondences dynamically for depth estimation. We design a novel iterative multi-view depth estimation framework mimicking the optimization process, which consists of 1) a correlation volume construction module that models the pixel similarity between a reference image and source images as all-to-all correlations; 2) a flow-based depth initialization module that estimates the depth from the 2D optical flow; 3) a novel correlation-guided depth refinement module that reprojects points in different views to effectively fetch relevant correlations for further fusion and integrate the fused correlation for iterative depth update. Without predefined depth hypotheses, the fused correlations establish multi-view correspondence in an efficient way and guide the depth refinement heuristically. We conduct sufficient experiments on ScanNet, DeMoN, ETH3D, and 7Scenes to demonstrate the superiority of our method on multi-view depth estimation and its best generalization ability.

cs.CV↗

arXiv2022

Rare event estimation with sequential directional importance sampling (SDIS)

In this paper, we propose a sequential directional importance sampling (SDIS) method for rare event estimation. SDIS expresses a small failure probability in terms of a sequence of auxiliary failure probabilities, defined by magnifying the input variability. The first probability in the sequence is estimated with Monte Carlo simulation in Cartesian coordinates, and all the subsequent ones are computed with directional importance sampling in polar coordinates. Samples from the directional importance sampling densities used to estimate the intermediate probabilities are drawn in a sequential manner through a resample-move scheme. The latter is conveniently performed in Cartesian coordinates and directional samples are obtained through a suitable transformation. For the move step, we discuss two Markov Chain Monte Carlo (MCMC) algorithms for application in low and high-dimensional problems. Finally, an adaptive choice of the parameters defining the intermediate failure probabilities is proposed and the resulting coefficient of variation of the failure probability estimate is analyzed. The proposed SDIS method is tested on five examples in various problem settings, which demonstrate that the method outperforms existing sequential sampling reliability methods.

stat.CO↗

arXiv2022

Analysis of lane-change conflict between cars and trucks at merging section using UAV video data

The freeway on-ramp merging section is often identified as a crash-prone spot due to the high frequency of traffic conflicts. Very few traffic conflict analysis studies comprehensively consider different vehicle types at freeway merging section. Thus, the main objective of this study is to analyse conflicts between different vehicle types at freeway merging section. Field data are collected by Unmanned Aerial Vehicle (UAV) at merging areas in Shanghai, China. Vehicle extraction method is utilized to obtain vehicle trajectories. Time-to-collision (TTC) is utilized as the surrogate safety measure. TTC of car-car conflicts are the smallest while TTC of truck-truck conflicts are the largest. Traffic conflicts frequently occur at on-ramp and acceleration lane. Results show the spatial distribution of lane-change conflicts is significantly different between different vehicle types, suggesting that vehicle drivers should maintain safe distance especially car drivers. Besides, in order to decrease lane-change conflict at merging area, traffic management agencies are suggested to change dotted lie to solid lane at the beginning of acceleration lane.

cs.RO↗

arXiv2021

Finding Taint-Style Vulnerabilities in Linux-based Embedded Firmware with SSE-based Alias Analysis

Although the importance of using static analysis to detect taint-style vulnerabilities in Linux-based embedded firmware is widely recognized, existing approaches are plagued by three major limitations. (a) Approaches based on symbolic execution may miss alias information and therefore suffer from a high false-negative rate. (b) Approaches based on VSA (value set analysis) often provide an over-approximate pointer range. As a result, many false positives could be produced. (c) Existing work for detecting taint-style vulnerability does not consider indirect call resolution, whereas indirect calls are frequently used in Internet-facing embedded devices. As a result, many false negatives could be produced. In this work, we propose a precise demand-driven flow-, context- and field-sensitive alias analysis approach. Based on this new approach, we also design a novel indirect call resolution scheme. Combined with sanitization rule checking, our solution discovers taint-style vulnerabilities by static taint analysis. We implemented our idea with a prototype called EmTaint and evaluated it against 35 real-world embedded firmware samples from six popular vendors. EmTaint discovered at least 192 bugs, including 41 n-day bugs and 151 0-day bugs. At least 115 CVE/PSV numbers have been allocated from a subset of the reported vulnerabilities at the time of writing. Compared to state-of-the-art tools such as KARONTE and SaTC, EmTaint found significantly more bugs on the same dataset in less time.

cs.CR↗

arXiv2021

High power Figure-of-Merit, 10.6-kV AlGaN/GaN lateral Schottky barrier diode with single channel and sub-100-μm anode-to-cathode spacing

GaN-based lateral Schottky diodes (SBDs) have attracted great attention for high-power applications due to its combined high electron mobility and large critical breakdown field. However, the breakdown voltage (BV) of the SBDs are far from exploiting the material advantages of GaN at present, limiting the desire to use GaN for ultra-high voltage (UHV) applications. Then, a golden question is whether the excellent properties of GaN-based materials can be practically used in the UHV field? Here we demonstrate UHV AlGaN/GaN SBDs on sapphire with a BV of 10.6 kV, a specific on-resistance of 25.8 mΩ.cm2, yielding a power figure of merit of more than 3.8 GW/cm2. These devices are designed with single channel and 85-μm anode-to-cathode spacing, without other additional electric field management, demonstrating its great potential for the UHV application in power electronics.

cond-mat.mtrl-sci↗

arXiv2021

Improved Gate Reliability of p-GaN Gate HEMTs by Gate Doping Engineering

We present a novel p-GaN gate HEMT structure with reduced hole concentration near the Schottky interface by doping engineering in MOCVD, which aims at lowering the electric field across the gate. By employing an additional unintentionally doped GaN layer, the gate leakage current is suppressed and the gate breakdown voltage is boosted from 10.6 to 14.6 V with negligible influence on the threshold voltage and on-resistance. Time-dependent gate breakdown measurements reveal that the maximum gate drive voltage increases from 6.2 to 10.6 V for a 10-year lifetime with a 1% gate failure rate. This method effectively expands the operating voltage margin of the p-GaN gate HEMTs without any other additional process steps.

physics.app-ph↗

arXiv2020

Logic Bugs in IoT Platforms and Systems: A Review

In recent years, IoT platforms and systems have been rapidly emerging. Although IoT is a new technology, new does not mean simpler (than existing networked systems). Contrarily, the complexity (of IoT platforms and systems) is actually being increased in terms of the interactions between the physical world and cyberspace. The increased complexity indeed results in new vulnerabilities. This paper seeks to provide a review of the recently discovered logic bugs that are specific to IoT platforms and systems. In particular, 17 logic bugs and one weakness falling into seven categories of vulnerabilities are reviewed in this survey.

cs.CR↗

arXiv2019

Dual-energy CT imaging using a single-energy CT data is feasible via deep learning

In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is, therefore, challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but DECT scanners are not widely available as single-energy CT (SECT) scanners. Here we develop a deep learning approach to perform DECT imaging by using standard SECT data. A deep learning model to map low-energy image to high-energy image using a two-stage convolutional neural network (CNN) is developed. The model was evaluated using patients who received contrast-enhanced abdomen DECT scan with a popular DE application: virtual non-contrast (VNC) imaging and contrast quantification. The HU differences between the predicted and original high-energy CT images are 3.47, 2.95, 2.38 and 2.40 HU for ROIs on the spine, aorta, liver, and stomach, respectively. The HU differences between VNC images obtained from original DECT and deep learning DECT are 4.10, 3.75, 2.33 and 2.92 HU for the 4 ROIs, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9\%, suggesting high consistency between the predicted and the original high-energy CT images. This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method can significantly simplify the DECT system design, reducing the scanning dose and imaging cost.

physics.med-ph↗

arXiv2017

Gaussian Filter in CRF Based Semantic Segmentation

Artificial intelligence is making great changes in academy and industry with the fast development of deep learning, which is a branch of machine learning and statistical learning. Fully convolutional network [1] is the standard model for semantic segmentation. Conditional random fields coded as CNN [2] or RNN [3] and connected with FCN has been successfully applied in object detection [4]. In this paper, we introduce a multi-resolution neural network for FCN and apply Gaussian filter to the extended CRF kernel neighborhood and the label image to reduce the oscillating effect of CRF neural network segmentation, thus achieve higher precision and faster training speed.

cs.CV↗

arXiv2015

Fluorescence Imaging In Vivo at Wavelengths beyond 1500 nm

Compared to imaging in the visible and near-infrared regions below 900 nm, imaging in the second near-infrared window (NIR-II, 1000-1700 nm) is a promising method for deep-tissue high-resolution optical imaging in vivo mainly due to the reduced scattering of photons traversing through biological tissues. Herein, semiconducting single-walled carbon nanotubes with large diameters were used for in vivo fluorescence imaging in the long-wavelength NIR region (1500-1700 nm, NIR-IIb). With this imaging agent, 3-4 um wide capillary blood vessels at a depth of about 3 mm could be resolved. Meanwhile, the blood-flow speeds in multiple individual vessels could be mapped simultaneously. Furthermore, NIR-IIb tumor imaging of a live mouse was explored. NIR-IIb imaging can be generalized to a wide range of fluorophores emitting at up to 1700 nm for high-performance in vivo optical imaging.

physics.bio-ph↗

arXiv2014

Ultra-Fast Fluorescence Imaging in Vivo with Conjugated Polymer Fluorophores in the Second Near-Infrared Window

In vivo fluorescence imaging in the second near-infrared window (1.0-1.7 microns) can afford deep tissue penetration and high spatial resolution, owing to the reduced scattering of long-wavelength photons. Here, we synthesize a series of low-bandgap donor/acceptor copolymers with tunable emission wavelengths of 1050-1350 nm in this window. Non-covalent functionalization with phospholipid-polyethylene glycol results in water-soluble and biocompatible polymeric nanoparticles, allowing for live cell molecular imaging at > 1000 nm with polymer fluorophores for the first time. Importantly, the high quantum yield of the polymer allows for in vivo, deep-tissue and ultrafast imaging of mouse arterial blood flow with an unprecedented frame rate of > 25 frames per second. The high time resolution results in spatially and time resolved imaging of the blood flow pattern in cardiogram waveform over a single cardiac cycle (~ 200 ms) of a mouse, which has not been observed with fluorescence imaging in this window before.

physics.bio-ph↗

arXiv2012

A photonic crystal cavity-optical fiber tip nanoparticle sensor for biomedical applications

We present a sensor capable of detecting solution-based nanoparticles using an optical fiber tip functionalized with a photonic crystal cavity. When sensor tips are retracted from a nanoparticle solution after being submerged, we find that a combination of convective fluid forces and optically-induced trapping cause an aggregation of nanoparticles to form directly on cavity surfaces. A simple readout of quantum dot photoluminescence coupled to the optical fiber shows that nanoparticle presence and concentration can be detected through modified cavity properties. Our sensor can detect both gold and iron oxide nanoparticles and can be utilized for molecular sensing applications in biomedicine.

physics.optics↗