arXiv subjects

Li Lin

Publications and source records attributed to Li Lin.

At least 109 records · Page 6Linked to original sources

arXiv2021

Towards the fabrication of a morphing plasma source for biomedical applications

A new design of plasma source that merges the main features of capacitive dielectric barrier discharge (DBD) and cold atmospheric plasma jet (CAPJ) is discussed. The DBD system consists of a flexible, porous matrix consisting of silica aerogel, which is comprised between two planar-parallel electrodes. The supply of helium flow submitted to a sinusoidal voltage of 5-9 kV in amplitude and 15 kHz in frequency provides a 2D-distribution of plasma jets that propagate around 1 cm beyond the active DBD region. The plasma multi-jet system is aimed at surface treatment of 3D objects and large areas. CAPJ performance as a hypothetical morphing source in flat and bent configurations is discussed. Optical emission spectroscopy (OES) diagnostics has provided the composition of the CAPJ through the aerogel layer. This novel source is promising for situations requiring thorough adaptation of plasma parameters to delicate samples, as for example wound healing and treatment of surgical margins in plasma-based cancer surgery.

physics.plasm-ph↗

arXiv2020

Effective Approximation for a Nonlocal Stochastic Schrödinger Equation with Oscillating Potential

We study the effective approximation for a nonlocal stochastic Schrodinger equation with a rapidly oscillating, periodically time-dependent potential. We use the natural diffusive scaling of heterogeneous system and study the limit behaviour as the scaling parameter tends to 0. This is motivated by data assimilations with non-Gaussian uncertainties. The nonlocal operator in this stochastic partial differential equation is the generator of a non-Gaussian Levy-type process (i.e., a class of anomalous diffusion processes), with non-integrable jump kernel. With help of a two-scale convergence technique, we establish effective approximation for this nonlocal stochastic partial differential equation. More precisely, we show that a nonlocal stochastic Schrodinger equation has a nonlocal effective equation. We show that it approximates the orginal stochastic Schrödinger equation weakly in a Sobolev-type space and strongly in $L^2$ space. In particular, this effective approximattion holds when the nonlocal operator is the fractional Laplacian.

math.PR↗

arXiv2020

Effective reduction for a nonlocal Zakai stochastic partial differential equation in data assimilation

We study the effective reduction for a nonlocal stochastic partial differential equation with oscillating coefficients. The nonlocal operator in this stochastic partial differential equation is the generator of non-Gaussian Lévy processes, with either \textbf{integrable} or \textbf{non-integrable} jump kernels. We examine the limiting behavior of this equation as a scaling parameter tends to zero, and derive a reduced (local or nonlocal) effective equation. In particular, this work leads to an effective reduction for a data assimilation system with Lévy noise, by examining the corresponding nonlocal Zakai stochastic partial differential equation. We show that the probability density for the reduced data assimilation system approximates that for the original system.

math.PR↗

arXiv2020

Existence and Uniqueness of a Fractional Fokker-Planck Equation

Stochastic differential equations with Levy motion arise the mathematical models for various phenomenon in geophysical and biochemical sciences. The Fokker Planck equation for such a stochastic differential equations is a nonlocal partial differential equations. We prove the existence and uniqueness of the weak solution for this equation.

math.AP↗

arXiv2020

Realization and transport investigation of a single layer-twisted bilayer graphene junction

We report on low-temperature transport study of a single layer graphene (SLG)-twisted bilayer graphene (tBLG) junction device. The SLG-tBLG junction in the device is grown by chemical vapor deposition and the device is fabricated in a Hall-bar configuration on Si/SiO$_2$ substrate. The longitudinal resistances across the SLG-tBLG junction (cross-junction resistances) on the two sides of the Hall bar and the Hall resistances of SLG and tBLG in the device are measured. In the quantum Hall regime, the measurements show that the measured cross-junction resistances exhibit a series of new quantized plateaus and the appearance of these resistance plateaus can be attributed to the presence of the well-defined edge-channel transport along the SLG-tBLG junction interface. The measurements also show that the difference between the cross-junction resistances measured on the two sides of the Hall-bar provides a sensitive measure to the edge channel transport characteristics in the two graphene layers that constitute the SLG-tBLG junction and to degeneracy lifting of the Landau levels in the tBLG layer. Temperature dependent measurements of the cross-junction resistance in the quantum Hall regime are also carried out and the influence of the transverse transport of the bulk Landau levels on the edge channel transport along the SLG-tBLG junction interface are extracted. These results enrich the understanding of the charge transport across interfaces in graphene hybrid structures and open up new opportunities for probing exotic quantum phenomena in graphene devices.

cond-mat.mes-hall↗

arXiv2020

A New Physically Triggered Cell Death via Transbarrier Contactless Cold Atmospheric Plasma Treatment of Cancer Cells

For years, extensive efforts have been made to discover effective, non-invasive anti-cancer therapies. Cold atmospheric plasma (CAP), is a near room temperature ionized gas composed of reactive species, charged particles, neutral particles, and electrons. CAP also has several physical factors including thermal radiation, ultraviolet (UV) radiation, and electromagnetic (EM) waves. Most of the previously reported biological effects of CAP have relied on direct contact between bulk plasma and cells, resulting in the chemical effects generally seen after CAP treatment. In this paper, we demonstrate that the electromagnetic emission produced by CAP can lead to the death of B16F10 melanoma cancer cells via a transbarrier contactless method. When compared with the effect of reactive species, the effect of the physical factors causes much greater growth inhibition. The physical-triggered growth inhibition is due to a new type of cell death, characterized by rapid leakage of bulk water from the cells, resulting in bubbles on the cell membrane, and cytoplasm shrinkage. The results of this study introduce a new possible mechanism of CAP induced cancer cell death and build a foundation for CAP to be used as a non-invasive cancer treatment in the future.

q-bio.TO↗

arXiv2020

An Approach for Process Model Extraction By Multi-Grained Text Classification

Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions. Previous process extractors heavily depend on manual features and ignore the potential relations between clues of different text granularities. In this paper, we formalize the PME task into the multi-grained text classification problem, and propose a hierarchical neural network to effectively model and extract multi-grained information without manually-defined procedural features. Under this structure, we accordingly propose the coarse-to-fine (grained) learning mechanism, training multi-grained tasks in coarse-to-fine grained order to share the high-level knowledge for the low-level tasks. To evaluate our approach, we construct two multi-grained datasets from two different domains and conduct extensive experiments from different dimensions. The experimental results demonstrate that our approach outperforms the state-of-the-art methods with statistical significance and further investigations demonstrate its effectiveness.

cs.CL↗

arXiv2019

Superlubric Nanogenerators with Superb Performances

Nanogenerators promise self-powered sensors and devices for extensive applications in internet of things, sensor networks, big data, personal healthcare systems, artificial intelligence, et al. However, low electric current densities and short product lifespans have blocked nanogenerators' applications. Here we show that structural superlubricity, a state of nearly zero friction and wear between two contacted solid surfaces, provides a revolutionary solution to the above challenge. We investigate three types of superlubric nanogenerators (SLNGs), namely the capacitor-based, triboelectric, and electret-based SLNGs, and systematically analyze the influences of material and structural parameters to these SLNGs' performances. We demonstrate that SLNGs can achieve not only enduring lifespans, but also superb performances - three orders of magnitude in current densities and output powers higher than those of conventional nanogenerators. Furthermore, SLNGs can be driven by very weak external loads (down to ~1 $μ$N) in very low frequencies (down to ~1 $μ$Hz), and are thus capable to harvest electric energies from an extremely board spectrum of environments and biosystems. Among the three types of SLNGs, the capacitor-based is synthetically most competitive in the senses of performance, fabrication and maintaining. These results can guide designs and accelerate fabrications of SLNGs toward real applications.

physics.app-ph↗

arXiv2019

The persistence of synchronization under $α$-stable noise

This work is about the synchronization of nonlinear coupled dynamical systems driven by $α$-stable noise. Firstly, we provide a novel technique to construct the relationship between synchronized system and slow-fast system. Secondly, we show that the slow component of original systems converges to the mild solution of the averaging equation under $L^{p}(1<p<α)$ sense. Finally, using the results of averaging principle for stochastic dynamical system with two-time scales, we show that the synchronization effect is persisted provided equilibria are replaced by stationary random solutions.

math.PR↗

arXiv2019

Eigenvalue estimates via Hömander's $L^2$-method

Under various elliptic boundary conditions, we obtain lower eigenvalue estimates for Dirac operators by using Hormander's weighted $L^2$-technique. Lower bounds in terms of the volume of the underlying manifolds are also deduced from the sharp Sobolev inequality due to Li and Zhu(\cite{LZ}).

math.DG↗

arXiv2019

An External Knowledge Enhanced Multi-label Charge Prediction Approach with Label Number Learning

Multi-label charge prediction is a task to predict the corresponding accusations for legal cases, and recently becomes a hot topic. However, current studies use rough methods to deal with the label number. These methods manually set parameters to select label numbers, which has an effect in final prediction quality. We propose an external knowledge enhanced multi-label charge prediction approach that has two phases. One is charge label prediction phase with external knowledge from law provisions, the other one is number learning phase with a number learning network (NLN) designed. Our approach enhanced by external knowledge can automatically adjust the threshold to get label number of law cases. It combines the output probabilities of samples and their corresponding label numbers to get final prediction results. In experiments, our approach is connected to some state of-the art deep learning models. By testing on the biggest published Chinese law dataset, we find that our approach has improvements on these models. We future conduct experiments on multi-label samples from the dataset. In items of macro-F1, the improvement of baselines with our approach is 3%-5%; In items of micro-F1, the significant improvement of our approach is 5%-15%. The experiment results show the effectiveness our approach for multi-label charge prediction.

cs.CL↗

arXiv2019

StegoAppDB: a Steganography Apps Forensics Image Database

In this paper, we present a new reference dataset simulating digital evidence for image steganography. Steganography detection is a digital image forensic topic that is relatively unknown in practical forensics, although stego app use in the wild is on the rise. This paper introduces the first database consisting of mobile phone photographs and stego images produced from mobile stego apps, including a rich set of side information, offering simulated digital evidence. StegoAppDB, a steganography apps forensics image database, contains over 810,000 innocent and stego images using a minimum of 10 different phone models from 24 distinct devices, with detailed provenanced data comprising a wide range of ISO and exposure settings, EXIF data, message information, embedding rates, etc. We develop a camera app, Cameraw, specifically for data acquisition, with multiple images per scene, saving simultaneously in both DNG and high-quality JPEG formats. Stego images are created from these original images using selected mobile stego apps through a careful process of reverse engineering. StegoAppDB contains cover-stego image pairs including for apps that resize the stego dimensions. We retainthe original devices and continue to enlarge the database, and encourage the image forensics community to use StegoAppDB. While designed for steganography, we discuss uses of this publicly available database to other digital image forensic topics.

eess.IV↗

arXiv2019

Distributed and Application-aware Task Scheduling in Edge-clouds

Edge computing is an emerging technology which places computing at the edge of the network to provide an ultra-low latency. Computation offloading, a paradigm that migrates computing from mobile devices to remote servers, can now use the power of edge computing by offloading computation to cloudlets in edge-clouds. However, the task scheduling of computation offloading in edge-clouds faces a two-fold challenge. First, as cloudlets are geographically distributed, it is difficult for each cloudlet to perform load balancing without centralized control. Second, as tasks of computation offloading have a wide variety of types, to guarantee the user quality of experience (QoE) in terms of task types is challenging. In this paper, we present Petrel, a distributed and application-aware task scheduling framework for edge-clouds. Petrel implements a sample-based load balancing technology and further adopts adaptive scheduling policies according to task types. This application-aware scheduling not only provides QoE guarantee but also improves the overall scheduling performance. Trace-driven simulations show that Petrel achieves a significant improvement over existing scheduling strategies.

cs.DC↗

arXiv2018

Transport signatures of relativistic quantum scars in a graphene cavity

We study a relativistic quantum cavity system realized by etching out from a graphene sheet by quantum transport measurements and theoretical calculations. The conductance of the graphene cavity has been measured as a function of the back gate voltage (or the Fermi energy) and the magnetic field applied perpendicular to the graphene sheet, and characteristic conductance contour patterns are observed in the measurements. In particular, two types of high conductance contour lines, i.e., straight and parabolic-like high conductance contour lines, are found in the measurements. The theoretical calculations are performed within the framework of tight-binding approach and Green's function formalism. Similar characteristic high conductance contour features as in the experiments are found in the calculations. The wave functions calculated at points selected along a straight conductance contour line are found to be dominated by a chain of scars of high probability distributions arranged as a necklace following the shape of cavity and the current density distributions calculated at these point are dominated by an overall vortex in the cavity. These characteristics are found to be insensitive to increasing magnetic field. However, the wave function probability distributions and the current density distributions calculated at points selected along a parabolic-like contour line show a clear dependence on increasing magnetic field, and the current density distributions at these points are characterized by the complex formation of several localized vortices in the cavity. Our work brings a new insight into quantum chaos in relativistic particle systems and would greatly stimulate experimental and theoretical efforts towards this still emerging field.

cond-mat.mes-hall↗

arXiv2018

Tackling Android Stego Apps in the Wild

Digital image forensics is a young but maturing field, encompassing key areas such as camera identification, detection of forged images, and steganalysis. However, large gaps exist between academic results and applications used by practicing forensic analysts. To move academic discoveries closer to real-world implementations, it is important to use data that represent "in the wild" scenarios. For detection of stego images created from steganography apps, images generated from those apps are ideal to use. In this paper, we present our work to perform steg detection on images from mobile apps using two different approaches: "signature" detection, and machine learning methods. A principal challenge of the ML task is to create a great many of stego images from different apps with certain embedding rates. One of our main contributions is a procedure for generating a large image database by using Android emulators and reverse engineering techniques. We develop algorithms and tools for signature detection on stego apps, and provide solutions to issues encountered when creating ML classifiers.

cs.CR↗

arXiv2018

Echo: An Edge-Centric Code Offloading System with Quality of Service Guarantee

Code offloading is promising to accelerate mobile applications and save energy of mobile devices by shifting some computation to cloud. However, existing code offloading systems suffer from a long communication delay between mobile devices and cloud. To address this challenge, in this paper, we consider to deploy edge nodes in the proximity of mobile devices, and study how they benefit code offloading. We design an edge-centric code offloading system, called Echo, over a three-layer computing hierarchy consisting of mobile devices, edge and cloud. A critical problem needs to be addressed by Echo is to decide which method should be offloaded to which computing platform (edge or cloud). Different from existing offloading systems that let mobile devices individually make offloading decisions, Echo implements a centralized decision engine at the edge node. This edge-centric design can fully exploit the limited hardware resources at the edge to provide an offloading service with Quality of Service guarantee. Furthermore, we propose some novel mechanisms, e.g., lazy object transmission and differential object update, to further improve system performance. The results of a small-scale real deployment and trace-driven simulations show that Echo significantly outperforms existing

cs.DC↗

arXiv2018

Micro-sized cold atmospheric plasma source for brain and breast cancer treatment

Micro-sized cold atmospheric plasma (uCAP) has been developed to expand the applications of CAP in cancer therapy. In this paper, uCAP devices with different nozzle lengths were applied to investigate effects on both brain (glioblastoma U87) and breast (MDA-MB-231) cancer cells. Various diagnostic techniques were employed to evaluate the parameters of uCAP devices with different lengths such as potential distribution, electron density, and optical emission spectroscopy. The generation of short- and long-lived species (such as hydroxyl radical (.OH), superoxide (O2-), hydrogen peroxide (H2O2), nitrite (NO2-), et al) were studied. These data revealed that uCAP treatment with a 20 mm length tube has a stronger effect than that of the 60 mm tube due to the synergetic effects of reactive species and free radicals. Reactive species generated by uCAP enhanced tumor cell death in a dose-dependent fashion and was not specific with regards to tumor cell type.

q-bio.TO↗

arXiv2018

Low-field magnetotransport in graphene cavity devices

Confinement and edge structures are known to play significant roles in electronic and transport properties of two-dimensional materials. Here, we report on low-temperature magnetotransport measurements of lithographically patterned graphene cavity nanodevices. It is found that the evolution of the low-field magnetoconductance characteristics with varying carrier density exhibits different behaviors in graphene cavity and bulk graphene devices. In the graphene cavity devices, we have observed that intravalley scattering becomes dominant as the Fermi level gets close to the Dirac point. We associate this enhanced intravalley scattering to the effect of charge inhomogeneities and edge disorder in the confined graphene nanostructures. We have also observed that the dephasing rate of carriers in the cavity devices follows a parabolic temperature dependence, indicating that the direct Coulomb interaction scattering mechanism governs the dephasing at low temperatures. Our results demonstrate the importance of confinement in carrier transport in graphene nanostructure devices.

cond-mat.mes-hall↗