arXiv subjects

Jin Li

Publications and source records attributed to Jin Li.

At least 235 records · Page 13Linked to original sources

arXiv2021

Antenna enhanced infrared photoinduced force imaging in aqueous environment with super-resolution and hypersensitivity

Tip enhanced IR spectra and imaging have been widely used in cutting-edge studies for the in-depth understanding of the composition, structure and function of interfaces at the nanoscale. However, molecular monolayer sensitivity has only been demonstrated on solid/gas interfaces. In aqueous environment, the reduced sensitivity due to strong damping of the cantilever oscillation and background IR absorption extremely limits the practical applications of tip enhanced IR nanospectroscopy. Here, we demonstrate hypersensitive nanoscale IR spectra and imaging in aqueous environment with the combination of photoinduced force (PiF) microscopy and resonant antennas. The highly confined electromagnetic field inbetween the tip end and antenna extremely amplifies the photoinduced force to the detectable level, while the excitation via plasmon internal reflection mode minimizes the environmental absorption. A polydimethylsiloxane (PDMS) layer (~1-2 nm thickness) functionalized on the AFM tip has been successfully identified in water with antennas of different sizes. Sampling volume of ~604 chemical bonds from PDMS was demonstrated with sub-10 nm spatial resolution confirmed by electric (E) field distribution mapping on antennas, which strongly suggests the desired requirements for interfacial spectroscopy. This platform demonstrates for the first time the application of photoinduced force microscopy in aqueous environments, providing a brand-new configuration to achieve highly enhanced nanoscale IR signals, which is extremely promising for future research of interfaces and nanosystems in aqueous environments.

physics.optics↗

arXiv2021

Deep Music Retrieval for Fine-Grained Videos by Exploiting Cross-Modal-Encoded Voice-Overs

Recently, the witness of the rapidly growing popularity of short videos on different Internet platforms has intensified the need for a background music (BGM) retrieval system. However, existing video-music retrieval methods only based on the visual modality cannot show promising performance regarding videos with fine-grained virtual contents. In this paper, we also investigate the widely added voice-overs in short videos and propose a novel framework to retrieve BGM for fine-grained short videos. In our framework, we use the self-attention (SA) and the cross-modal attention (CMA) modules to explore the intra- and the inter-relationships of different modalities respectively. For balancing the modalities, we dynamically assign different weights to the modal features via a fusion gate. For paring the query and the BGM embeddings, we introduce a triplet pseudo-label loss to constrain the semantics of the modal embeddings. As there are no existing virtual-content video-BGM retrieval datasets, we build and release two virtual-content video datasets HoK400 and CFM400. Experimental results show that our method achieves superior performance and outperforms other state-of-the-art methods with large margins.

cs.IR↗

arXiv2021

Ground state configuration of hydrogenated Biphenylene sheet: structure, stabilities, electronic and mechanical properties from first-principles calculations

Based on first-principles calculations, the ground state configuration (Cmma-CH) of hydrogenated Biphenylene sheet (Science, 372, 852, 2021) is carefully identified from hundreds of possible candidates generated by RG2 code (Phys. Rev. B., 97, 014104, 2018). Cmma-CH contains four benzene molecules in its crystalline cell and all of them are inequivalent due to its Cmma symmetry. The hydrogen atoms in Cmma-CH bond to carbon atoms in each benzene with a boat-like (boat-1:DDUDDU) up/down sequence and reversed boat-1 (UUDUUD) sequence in adjacent benzene rings. It is energetically less stable than the previously proposed allotropes (chair, tricycle, stirrup, boat-1, boat-2 and twist-boat) of hydrogenated graphene, but its formation energy from hydrogenating Biphenylene sheet is remarkably lower than those for hydrogenating graphene to graphane. Our results confirm that Cmma-CH is mechanically and dynamically stable 2D hydrocarbon phase which is expectable to be experimentally realized by hydrogenating the synthesized Biphenylene sheet. The HSE06 based band structures show that Cmma-CH is an indirect band gap insulators with a gap of 4.645 eV.

cond-mat.mtrl-sci↗

arXiv2021

Targeting the Weakest Link: Social Engineering Attacks in Ethereum Smart Contracts

Ethereum holds multiple billions of U.S. dollars in the form of Ether cryptocurrency and ERC-20 tokens, with millions of deployed smart contracts algorithmically operating these funds. Unsurprisingly, the security of Ethereum smart contracts has been under rigorous scrutiny. In recent years, numerous defense tools have been developed to detect different types of smart contract code vulnerabilities. When opportunities for exploiting code vulnerabilities diminish, the attackers start resorting to social engineering attacks, which aim to influence humans -- often the weakest link in the system. The only known class of social engineering attacks in Ethereum are honeypots, which plant hidden traps for attackers attempting to exploit existing vulnerabilities, thereby targeting only a small population of potential victims. In this work, we explore the possibility and existence of new social engineering attacks beyond smart contract honeypots. We present two novel classes of Ethereum social engineering attacks - Address Manipulation and Homograph - and develop six zero-day social engineering attacks. To show how the attacks can be used in popular programming patterns, we conduct a case study of five popular smart contracts with combined market capitalization exceeding $29 billion, and integrate our attack patterns in their source codes without altering their existing functionality. Moreover, we show that these attacks remain dormant during the test phase but activate their malicious logic only at the final production deployment. We further analyze 85,656 open-source smart contracts, and discover that 1,027 of them can be used for the proposed social engineering attacks. We conduct a professional opinion survey with experts from seven smart contract auditing firms, corroborating that the exposed social engineering attacks bring a major threat to the smart contract systems.

cs.CR↗

arXiv2021

Constraints on running vacuum models with the baryon-to-photon ratio

We study the influence of running vacuum on the baryon-to-photon ratio in running vacuum models (RVMs). When there exists a non-minimal coupling between photons and other matter in the expanding universe, the energy-momentum tensor of photons is no longer conserved, but the energy of photons could remain conserved. We discuss the conditions for the energy conservation of photons in RVMs. The photon number density and baryon number density, from the epoch of photon decoupling to the present day, are obtained in the context of RVMs by assuming that photons and baryons can be coupled to running vacuum, respectively. Both cases lead to a time-evolving baryon-to-photon ratio. However the evolution of the baryon-to-photon ratio is strictly constrained by observations. It is found that if the dynamic term of running vacuum is indeed coupled to photons or baryons, the coefficient of the dynamic term must be extremely small, which is unnatural. Therefore, our study basically rules out the possibility that running vacuum is coupled to photons or baryons in RVMs.

hep-ph↗

arXiv2021

Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge

Although significant progress in automatic learning of steganographic cost has been achieved recently, existing methods designed for spatial images are not well applicable to JPEG images which are more common media in daily life. The difficulties of migration mostly lie in the unique and complicated JPEG characteristics caused by 8x8 DCT mode structure. To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure. It works with the embedding action sampling mechanism under reinforcement learning, where a policy network learns the optimal embedding policies via maximizing the rewards provided by an environment network. The policy network is constructed following a domain-transition design paradigm, where three modules including pixel-level texture complexity evaluation, DCT feature extraction, and mode-wise rearrangement, are proposed. These modules operate in serial, gradually extracting useful features from a decompressed JPEG image and converting them into embedding policies for DCT elements, while considering JPEG characteristics including inter-block and intra-block correlations simultaneously. The environment network is designed in a gradient-oriented way to provide stable reward values by using a wide architecture equipped with a fixed preprocessing layer with 8x8 DCT basis filters. Extensive experiments and ablation studies demonstrate that the proposed method can achieve good security performance for JPEG images against both advanced feature based and modern CNN based steganalyzers.

cs.CR↗

arXiv2021

Resolving the Intrinsic Bandgap and Edge Effect of BiI3 Film Epitaxially Grown on Graphene

Two-dimensional materials with layered structures, appropriate bandgap, and high carrier mobilities have gathered tremendous interests due to their potential applications in optoelectronic and photovoltaic devices. Here, we report the growth of BiI3 thin film with controllable atomic thickness on graphene-terminated 6H-SiC(0001) substrate by molecular beam epitaxy (MBE) method. The growth kinetic processes and crystalline properties of the BiI3 film are studied by scanning tunneling microscopy (STM). The scanning tunneling spectroscopy (STS) reveals a bandgap of 2.8 eV for monolayer BiI3 with a weak dependence on film thickness for few-layer BiI3, which greatly exceeds the previous reported values identified by macroscopic optical measurements. This discrepancy originates from the edge effect of BiI3 that renders the bandgap downshift to 1.5 - 1.6 eV, as identified by the STS curves and the further confirmed by density functional theory (DFT) calculations. Our work provides a method to fabricate high-quality monolayer BiI3 film and resolves its intrinsic bandgap as well as the edge effect on reduction of bandgap, benefitting not only to fundamental researches but also to nanoelectronic and optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2021

Epitaxial Growth of Quasi-One-Dimensional Bismuth-Halide Chains with Topological Non-Trivial Edge States

Quantum spin Hall insulators have one-dimensional (1D) spin-momentum locked topological edge states (ES) inside the bulk band gap, which can serve as dissipationless channels for the practical applications in low consumption electronics and high performance spintronics. However, the clean and atomically sharp ES serving as ideal 1D conducting channels are still lack. Here, we report the formation of the quasi-1D Bi4I4 nanoribbons on the surface of Bi(111) with the support of the graphene-terminated 6H-SiC(0001) and the direct observations of the topological ES at the step edge by scanning tunneling microscopy and spectroscopic-imaging results. The ES reside surround the edge of Bi4I4 nanoribbons and exhibits remarkable robustness against non time reversal symmetry perturbations. The theoretical simulations verify the topological non-trivial character of 1D ES, which is retained after considering the presence of the underlying Bi(111). Our study supports the existence of topological ES in Bi4I4 nanoribbons, paving the way to engineer the novel topological features by using the nanoribbons as the 1D building block.

cond-mat.mtrl-sci↗

arXiv2021

Testing f(R) gravity with the simulated data of gravitational waves from the Einstein Telescope

In this paper we analyze the implications of gravitational waves (GWs) as standard sirens on the modified gravity models by using the third-generation gravitational wave detector, i.e., the Einstein Telescope. Two viable models in $f(R)$ theories within the Palatini formalism are considered in our analysis ($f_{1}(\mathcal{R})=\mathcal{R}-\fracβ{\mathcal{R}^{n}}$ and $f_{2}(\mathcal{R})=\mathcal{R}+α\ln{\mathcal{R}}-β$), with the combination of simulated GW data and the latest electromagnetic (EM) observational data (including the recently released Pantheon type Ia supernovae sample, the cosmic chronometer data, and baryon acoustic oscillation distance measurements). Our analysis reveals that the standard sirens GWs, which provide an independent and complementary alternative to current experiments, could effectively eliminate the degeneracies among parameters in the two modified gravity models. In addition, we thoroughly investigate the nature of geometrical dark energy in the modified gravity theories with the assistance of $Om(z)$ and statefinder diagnostic analysis. The present analysis makes it clear-cut that the simplest cosmological constant model is still the most preferred by the current data. However, the combination of future naturally improved GW data most recent EM observations will reveal the consistency or acknowledge the tension between the $Λ$CDM model and modified gravity theories.

astro-ph.CO↗

arXiv2021

Simulation on the Transparency of Electrons and Ion Back Flow for a Time Projection Chamber based on Staggered Multiple THGEMs

The IBF and the transparent rate of electrons are two essential indicators of TPC, which affect the energy resolution and counting rate respectively. In this paper, we propose several novel strategies of staggered multi-THGEM to suppress IBF, where the geometry of the first layer THGEM will be optimized to increase the electron transparent rate. By Garfield++ simulation, the electron transparency rate can be more than 90% of single THGEM with a optimized large hole. By simulating these configurations of triple and quadruple THGEM structures, we conclude that the IBF can be reduced to 0.2% level in an optimized configuration denoted as "ACBA". This strategy for staggered THGEM could have potential applications in future TPC projects.

physics.ins-det↗

arXiv2021

Truncation-Free Matching System for Display Advertising at Alibaba

Matching module plays a critical role in display advertising systems. Without query from user, it is challenging for system to match user traffic and ads suitably. System packs up a group of users with common properties such as the same gender or similar shopping interests into a crowd. Here term crowd can be viewed as a tag over users. Then advertisers bid for different crowds and deliver their ads to those targeted users. Matching module in most industrial display advertising systems follows a two-stage paradigm. When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds. However, in applications such as display advertising at Alibaba, with very large volumes of crowds and ads, both stages of matching have to truncate the long-tailed parts for online serving, under limited latency. That's to say, not all ads have the chance to participate in online matching. This results in sub-optimal result for both advertising performance and platform revenue. In this paper, we study the truncation problem and propose a Truncation Free Matching System (TFMS). The basic idea is to decouple the matching computation from the online pipeline. Instead of executing the two-stage matching when user visits, TFMS utilizes a near-line truncation-free matching to pre-calculate and store those top valuable ads for each user. Then the online pipeline just needs to fetch the pre-stored ads as matching results. In this way, we can jump out of online system's latency and computation cost limitations, and leverage flexible computation resource to finish the user-ad matching. TFMS has been deployed in our productive system since 2019, bringing (i) more than 50% improvement of impressions for advertisers who encountered truncation before, (ii) 9.4% Revenue Per Mile gain, which is significant enough for the business.

cs.IR↗

arXiv2020

Joint Intensity-Gradient Guided Generative Modeling for Colorization

This paper proposes an iterative generative model for solving the automatic colorization problem. Although previous researches have shown the capability to generate plausible color, the edge color overflow and the requirement of the reference images still exist. The starting point of the unsupervised learning in this study is the observation that the gradient map possesses latent information of the image. Therefore, the inference process of the generative modeling is conducted in joint intensity-gradient domain. Specifically, a set of intensity-gradient formed high-dimensional tensors, as the network input, are used to train a powerful noise conditional score network at the training phase. Furthermore, the joint intensity-gradient constraint in data-fidelity term is proposed to limit the degree of freedom within generative model at the iterative colorization stage, and it is conducive to edge-preserving. Extensive experiments demonstrated that the system outperformed state-of-the-art methods whether in quantitative comparisons or user study.

cs.CV↗

arXiv2020

Measuring the viscosity of dark matter with strongly lensed gravitational waves

Based on the strongly lensed gravitational waves (GWs) from compact binary coalescence, we propose a new strategy to examine the fluid shear viscosity of dark matter (DM) in the gravitational wave domain, i.e., whether a GW experiences the damping effect when it propagates in DM fluid with nonzero shear viscosity. By assuming that the dark matter self-scatterings are efficient enough for the hydrodynamic description to be valid, our results demonstrate that future ground-based Einstein Telescope (ET) and satellite GW observatory (Big Bang Observer; BBO) may succeed in detecting any dark matter self-interactions at the scales of galaxies and clusters.

astro-ph.CO↗

arXiv2020

A Reversed and Shift Sparse Array Scheme based on the Difference and Sum Co-array

The reversed and shift (RAS) sparse array scheme, which is based on the difference and sum co-array (DSCA) and remarkably enhances the capability of identifying sources, is proposed. For the original nested array (NA) or co-prime array (CPA), there exists a large overlap between its difference co-array and sum co-array, which prevents it from obtaining high degrees of freedom (DOFs). Motivated by this fact, the RAS scheme is designed for reducing the overlap while increasing the DOFs for direction of arrival (DOA) estimation. The proposed scheme is effective for both NA and CPA. The closed-form expression for the relationship between the number of physical sensors and the number of consecutive DSCA sensors is derived. Compared with some representative DSCA based sparse arrays, the proposed one can achieve longer consecutive virtual array. Simulation experiments are carried out to exhibit the enhanced DOA estimation performance of RAS scheme.

eess.SP↗

arXiv2020

Ultrastrong, Ultraflexible, and Ultratransparent Polyethylene Cellular Nanofilms

Light weight and mechanically robust cellular polymer nanofilms provide materials solutions to many cutting-edge technologies, such as high-flux membrane filtration, ultrathin flexible energy storage, and skin-conformable devices. However, it remains challenging to fabricate hand manipulatable cellular polymer nanofilms for use as self-standing structural materials. Herein, we used a sequential planar extension approach to transform low-entanglement ultrahigh molecular weight polyethylene (UHMWPE) gel films dispersed in porogenic polyethylene oligomers into cellular nanofilms consisting of stretch-dominated triangular cells of molecularly anisotropic cell edges. The microstructure afforded the cellular nanofilm, which had a thickness down to 20 nm, with a unique combination of ultratransparency (>98.5%), ultrahigh in-plane tensile strength (1071 MPa.cm^3.g^(-1)), and ultrahigh flexibility: a 43 nm thick film can deflect reversibly up to 8.0 mm in depth (185,000 times) under a spherical indentation load. As an application, we fabricated the nanofilm into a freestanding ultratransparent respiratory face covering. The new polyethylene cellular nanofilms are expected to represent a new class of platform membranes for advancing fundamental and technological development.

cond-mat.mtrl-sci↗

arXiv2020

Role of Digital Twin in Optical Communication: Fault Management, Hardware Configuration, and Transmission Simulation

Optical communication is developing rapidly in the directions of hardware resource diversification, transmission system flexibility, and network function virtualization. Its proliferation poses a significant challenge to traditional optical communication management and control systems. Digital twin (DT), a technology that utilizes data, models, and algorithms and integrates multiple disciplines, acts as a bridge between the real and virtual worlds for comprehensive connectivity. In the digital space, virtual models are stablished dynamically to simulate and describe the states, behaviors, and rules of physical objects in the physical space. DT has been significantly developed and widely applied in industrial and military fields. This study introduces the DT technology to optical communication through interdisciplinary crossing and proposes a DT framework suitable for optical communication. The intelligent fault management model, flexible hardware configuration model, and dynamic transmission simulation model are established in the digital space with the help of deep learning algorithms to ensure the highreliability operation and high-efficiency management of optical communication systems and networks.

cs.NI↗

arXiv2020

Machine Learning for Nanohertz Gravitational Wave Detection and Parameter Estimation with Pulsar Timing Array

Studies have shown that the use of pulsar timing arrays (PTAs) is among the approaches with the highest potential to detect very low-frequency gravitational waves in the near future. Although the capture of gravitational waves (GWs) by PTAs has not been reported yet, many related theoretical studies and some meaningful detection limits have been reported. In this study, we focused on the nanohertz GWs from individual supermassive binary black holes. Given specific pulsars (PSR J1909$-$3744, PSR J1713$+$0747, PSR J0437$-$4715), the corresponding GW$-$induced timing residuals in PTAs with Gaussian white noise can be simulated. Further, we report the classification of the simulated PTA data and parameter estimation for potential GW sources using machine learning based on neural networks. As a classifier, the convolutional neural network shows high accuracy when the combined signal to noise ratio $\geq$1.33 for our simulated data. Further, we applied a recurrent neural network to estimate the chirp mass ($\mathcal{M}$) of the source and luminosity distance ($\text{D}_{p}$) of the pulsars and Bayesian neural networks (BNNs) to obtain the uncertainties of chirp mass estimation. Knowledge of the uncertainties is crucial to astrophysical observation. In our case, the mean relative error of chirp mass estimation is less than $13.6\%$. Although these results are achieved for simulated PTA data, we believe that they will be important for realizing intelligent processing in PTA data analysis.

astro-ph.IM↗

arXiv2020

A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further.

cs.SI↗