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Li Li

Publications and source records attributed to Li Li.

At least 541 records · Page 30Linked to original sources
arXiv2021

Competitive and Cooperative electronic states in Ba(Fe$_{1-x}$T$_x$)$_2$As$_2$ with T=Co, Ni, Cr

The electronic structure inhomogeneities in Co, Ni, and Cr doped BaFe2As2 122 single crystals are compared using scanning tunneling microscopy/spectroscopy (STM/S) at the nanoscale within three bulk property regions in the phase diagram: a pure superconducting (SC) dome region (Co-122), a coexisting SC and antiferromagnetic (AFM) region (Ni-122), and a non-SC region (Cr-122). Machine learning is utilized to categorize the various nanometer scale inhomogeneous electronic states, described here as in-gap, L-shape and S-shape states immersed into the SC matrix for Ni-and Co-doped 122, and L-shape and S-shape states into the metallic matrix for Cr-doped 122. Although the relative percentages of in-gap, L-shape and S-shape states vary in the three samples, the total volume fraction of the three electronic states is quite similar. This is coincident with the number of electrons (Ni0.04 and Co0.08) and holes (Cr0.04) doped into the 122 compound. By combining the volume fractions of the three states, the local density of states (LDOS), magnetic field dependent behavior and global properties in these three samples, the in-gap state is confirmed as a magnetic impurity state from the Co or Ni dopants. In addition, the L-shape state is identified as a spin density wave (SDW) which competes with the SC phase, and the S-shape state is found to be another form of magnetic order which constructively cooperates with the SC phase, rather than competing with it. The comparison of the vortex structures indicates that the inhomogeneous electronic states serve as pinning centers for stabilizing the vortex lattice.

cond-mat.supr-con↗
arXiv2021

Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment

In this paper, we outline a way to deploy a privacy-preserving protocol for multiparty Randomized Controlled Trials on the scale of 500 million rows of data and more than a billion gates. Randomized Controlled Trials (RCTs) are widely used to improve business and policy decisions in various sectors such as healthcare, education, criminology, and marketing. A Randomized Controlled Trial is a scientifically rigorous method to measure the effectiveness of a treatment. This is accomplished by randomly allocating subjects to two or more groups, treating them differently, and then comparing the outcomes across groups. In many scenarios, multiple parties hold different parts of the data for conducting and analyzing RCTs. Given privacy requirements and expectations of each of these parties, it is often challenging to have a centralized store of data to conduct and analyze RCTs. We accomplish this by a three-stage solution. The first stage uses the Private Secret Share Set Intersection (PS$^3$I) solution to create a joined set and establish secret shares without revealing membership, while discarding individuals who were placed into more than one group. The second stage runs multiple instances of a general purpose MPC over a sharded database to aggregate statistics about each experimental group while discarding individuals who took an action before they received treatment. The third stage adds distributed and calibrated Differential Privacy (DP) noise to the aggregate statistics and uncertainty measures, providing formal two-sided privacy guarantees. We also evaluate the performance of multiple open source general purpose MPC libraries for this task. We additionally demonstrate how we have used this to create a working ads effectiveness measurement product capable of measuring hundreds of millions of individuals per experiment.

cs.CR↗
arXiv2021

Taming Process Variations in CNFET for Efficient Last Level Cache Design

Carbon nanotube field-effect transistors (CNFET) emerge as a promising alternative to CMOS transistors for the much higher speed and energy efficiency, which makes the technology particularly suitable for building the energy-hungry last level cache (LLC). However, the process variations (PVs) in CNFET caused by the imperfect fabrication lead to large timing variation and the worst-case timing dramatically limits the LLC operation speed. Particularly, we observe that the CNFET-based cache latency distribution is closely related to the LLC layouts. For the two typical LLC layouts that have the CNT growth direction aligned to the cache way direction and cache set direction respectively, we proposed variation-aware set aligned (VASA) cache and variation-aware way aligned (VAWA) cache in combination with corresponding cache optimizations such as data shuffling and page mapping to enable low-latency cache for frequently used data. According to our experiments, the optimized LLC reduces the average access latency by 32% and 45% compared to the baseline designs on the two different CNFET layouts respectively while it improves the overall performance by 6\% and 9\% and reduces the energy consumption by 4% and 8% respectively. In addition, with both the architecture induced latency variation and PV incurred latency variation considered in a unified model, we extended the VAWA and VASA cache design for the CNFET-based NUCA and the proposed NUCA achieves both significant performance improvement and energy saving compared to the straightforward variation-aware NUCA.

cs.AR↗
arXiv2021

Research on Third-Party Libraries in AndroidApps: A Taxonomy and Systematic LiteratureReview

Third-party libraries (TPLs) have been widely used in mobile apps, which play an essential part in the entire Android ecosystem. However, TPL is a double-edged sword. On the one hand, it can ease the development of mobile apps. On the other hand, it also brings security risks such as privacy leaks or increased attack surfaces (e.g., by introducing over-privileged permissions) to mobile apps. Although there are already many studies for characterizing third-party libraries, including automated detection, security and privacy analysis of TPLs, TPL attributes analysis, etc., what strikes us odd is that there is no systematic study to summarize those studies' endeavors. To this end, we conduct the first systematic literature review on Android TPL-related research. Following a well-defined systematic literature review protocol, we collected 74 primary research papers closely related to the Android third-party library from 2012 to 2020. After carefully examining these studies, we designed a taxonomy of TPL-related research studies and conducted a systematic study to summarize current solutions, limitations, challenges and possible implications of new research directions related to third-party library analysis. We hope that these contributions can give readers a clear overview of existing TPL-related studies and inspire them to go beyond the current status quo by advancing the discipline with innovative approaches.

cs.SE↗
arXiv2021

A Systematic Assessment on Android Third-party Library Detection Tools

Third-party libraries (TPLs) have become a significant part of the Android ecosystem. Developers can employ various TPLs to facilitate their app development. Unfortunately, the popularity of TPLs also brings new security issues. For example, TPLs may carry malicious or vulnerable code, which can infect popular apps to pose threats to mobile users. Furthermore, TPL detection is essential for downstream tasks, such as vulnerabilities and malware detection. Thus, various tools have been developed to identify TPLs. However, no existing work has studied these TPL detection tools in detail, and different tools focus on different applications and techniques with performance differences. A comprehensive understanding of these tools will help us make better use of them. To this end, we conduct a comprehensive empirical study to fill the gap by evaluating and comparing all publicly available TPL detection tools based on six criteria: accuracy of TPL construction, effectiveness, efficiency, accuracy of version identification, resiliency to code obfuscation, and ease of use. Besides, we enhance these open-source tools by fixing their limitations, to improve their detection ability. Finally, we build an extensible framework that integrates all existing available TPL detection tools, providing an online service for the research community. We release the evaluation dataset and enhanced tools. According to our study, we also present the essential findings and discuss promising implications to the community. We believe our work provides a clear picture of existing TPL detection techniques and also gives a roadmap for future research.

cs.SE↗
arXiv2021

Video-based Point Cloud Compression Artifact Removal

Photo-realistic point cloud capture and transmission are the fundamental enablers for immersive visual communication. The coding process of dynamic point clouds, especially video-based point cloud compression (V-PCC) developed by the MPEG standardization group, is now delivering state-of-the-art performance in compression efficiency. V-PCC is based on the projection of the point cloud patches to 2D planes and encoding the sequence as 2D texture and geometry patch sequences. However, the resulting quantization errors from coding can introduce compression artifacts, which can be very unpleasant for the quality of experience (QoE). In this work, we developed a novel out-of-the-loop point cloud geometry artifact removal solution that can significantly improve reconstruction quality without additional bandwidth cost. Our novel framework consists of a point cloud sampling scheme, an artifact removal network, and an aggregation scheme. The point cloud sampling scheme employs a cube-based neighborhood patch extraction to divide the point cloud into patches. The geometry artifact removal network then processes these patches to obtain artifact-removed patches. The artifact-removed patches are then merged together using an aggregation scheme to obtain the final artifact-removed point cloud. We employ 3D deep convolutional feature learning for geometry artifact removal that jointly recovers both the quantization direction and the quantization noise level by exploiting projection and quantization prior. The simulation results demonstrate that the proposed method is highly effective and can considerably improve the quality of the reconstructed point cloud.

cs.MM↗
arXiv2021

Quantum oscillator noise spectroscopy via displaced cat states

Quantum harmonic oscillators are central to many modern quantum technologies. We introduce a method to determine the frequency noise spectrum of oscillator modes through coupling them to a qubit with continuously driven qubit-state-dependent displacements. We reconstruct the noise spectrum using a series of different drive phase and amplitude modulation patterns in conjunction with a data-fusion routine based on convex optimization. We apply the technique to the identification of intrinsic noise in the motional frequency of a single trapped ion with sensitivity to fluctuations at the sub-Hz level in a spectral range from quasi-DC up to 50 kHz.

quant-ph↗
arXiv2021

MHD analysis on the physical designs of CFETR and HFRC

The China Fusion Engineering Test Reactor (CFETR) and the Huazhong Field Reversed Configuration (HFRC), currently both under intensive physical and engineering designs in China, are the two major projects representative of the low-density steady-state and high-density pulsed pathways to fusion. One of the primary tasks of the physics designs for both CFETR and HFRC is the assessment and analysis of the magnetohydrodynamic (MHD) stability of the proposed design schemes. Comprehensive efforts on the assessment of MHD stability of CFETR and HFRC baseline scenarios have led to preliminary progresses that may further benefit engineering designs.

physics.plasm-ph↗
arXiv2021

Directly Measuring a Multiparticle Quantum Wave Function via Quantum Teleportation

We propose a new method to directly measure a general multi-particle quantum wave function, a single matrix element in a multi-particle density matrix, by quantum teleportation. The density matrix element is embedded in a virtual logical qubit and is nondestructively teleported to a single physical qubit for readout. We experimentally implement this method to directly measure the wavefunction of a photonic mixed quantum state beyond a single photon using a single observable for the first time. Our method also provides an exponential advantage over the standard quantum state tomography in measurement complexity to fully characterize a sparse multi-particle quantum state.

quant-ph↗
arXiv2021

No Cauchy Horizon Theorem for Nonlinear Electrodynamics Black Holes with Charged Scalar Hairs

We prove a no Cauchy horizon theorem for general nonlinear electrodynamics black holes with charged scalar hairs. By constructing a radially conserved charged, we show that there is no inner Cauchy horizon for both spherical and planar symmetric cases, independent of the form of scalar potential and nonlinear electrodynamics. After imposing the null energy condition, we are also able to rule out the existence of the Cauchy horizon for the hyperbolic black holes. We take the Born-Infeld black hole as a concrete example to study the interior dynamics beyond the event horizon. When the contribution from the scalar potential can be neglected, the asymptotic near-singularity takes a universal Kasner form. We also confirm that the intricate interior dynamics is closely associated with the instability of the inner Cauchy horizon triggered by scalar hairs.

gr-qc↗
arXiv2021

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

Graph neural networks (GNNs) integrate deep architectures and topological structure modeling in an effective way. However, the performance of existing GNNs would decrease significantly when they stack many layers, because of the over-smoothing issue. Node embeddings tend to converge to similar vectors when GNNs keep recursively aggregating the representations of neighbors. To enable deep GNNs, several methods have been explored recently. But they are developed from either techniques in convolutional neural networks or heuristic strategies. There is no generalizable and theoretical principle to guide the design of deep GNNs. To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs. Based on it, a novel deep GNN framework -- EGNN is designed. It could provide lower and upper constraints in terms of Dirichlet energy at each layer to avoid over-smoothing. Experimental results demonstrate that EGNN achieves state-of-the-art performance by using deep layers.

cs.LG↗
arXiv2021

Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light

The tantalizing promise of quantum computational speedup in solving certain problems has been strongly supported by recent experimental evidence from a high-fidelity 53-qubit superconducting processor1 and Gaussian boson sampling (GBS) with up to 76 detected photons. Analogous to the increasingly sophisticated Bell tests that continued to refute local hidden variable theories, quantum computational advantage tests are expected to provide increasingly compelling experimental evidence against the Extended Church-Turing thesis. In this direction, continued competition between upgraded quantum hardware and improved classical simulations is required. Here, we report a new GBS experiment that produces up to 113 detection events out of a 144-mode photonic circuit. We develop a new high-brightness and scalable quantum light source, exploring the idea of stimulated squeezed photons, which has simultaneously near-unity purity and efficiency. This GBS is programmable by tuning the phase of the input squeezed states. We demonstrate a new method to efficiently validate the samples by inferring from computationally friendly subsystems, which rules out hypotheses including distinguishable photons and thermal states. We show that our noisy GBS experiment passes the nonclassicality test using an inequality, and we reveal non-trivial genuine high-order correlation in the GBS samples, which are evidence of robustness against possible classical simulation schemes. The photonic quantum computer, Jiuzhang 2.0, yields a Hilbert space dimension up to $10^{43}$, and a sampling rate $10^{24}$ faster than using brute-force simulation on supercomputers.

quant-ph↗
arXiv2021

Composition and Application of Current Advanced Driving Assistance System: A Review

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, and novel applications in real-world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support and computation algorithms. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about the definition of ADAS from different institutes are reviewed in this paper, and future approaches about ADAS in China are introduced in particular.

cs.AI↗
arXiv2021

A Real-Time Dispatching Strategy for Shared Automated Electric Vehicles with Performance Guarantees

Real-time vehicle dispatching operations in traditional car-sharing systems is an already computationally challenging scheduling problem. Electrification only exacerbates the computational difficulties as charge level constraints come into play. To overcome this complexity, we employ an online minimum drift plus penalty (MDPP) approach for SAEV systems that (i) does not require a priori knowledge of customer arrival rates to the different parts of the system (i.e. it is practical from a real-world deployment perspective), (ii) ensures the stability of customer waiting times, (iii) ensures that the deviation of dispatch costs from a desirable dispatch cost can be controlled, and (iv) has a computational time-complexity that allows for real-time implementation. Using an agent-based simulator developed for SAEV systems, we test the MDPP approach under two scenarios with real-world calibrated demand and charger distributions: 1) a low-demand scenario with long trips, and 2) a high-demand scenario with short trips. The comparisons with other algorithms under both scenarios show that the proposed online MDPP outperforms all other algorithms in terms of both reduced customer waiting times and vehicle dispatching costs.

eess.SY↗
arXiv2021

Chameleon dark energy can resolve the Hubble tension

Values of the Hubble constant between the direct measurements from various independent local observations and that inferred from the cosmic microwave background with the $Λ$-cold-dark-matter model are in tension with persistent significance. We propose a late-time inhomogeneous resolution suggesting that a chameleon field coupled to a local overdensity of matter could be trapped at a higher potential energy density as an effective cosmological constant driving the local expansion rate faster than that of the background with lower matter density. We illustrate this mechanism in a toy model in which a region with only $20\%$ overdensity of matter is sufficient to resolve the Hubble tension, and the Hubble constant measured by the local distance ladders could be accommodated by the chameleon coupled to the observed overdensities from the large-scale structure surveys.

astro-ph.CO↗
arXiv2021

Determining The Magnetic Potential In The Fractional Magnetic Calderón Problem

We determine both the magnetic potential and the electric potential from the exterior partial measurements of the Dirichlet-to-Neumann map in the fractional linear magnetic Calderón problem by using an integral identity. We also determine both the magnetic potential and the nonlinearity in the fractional semilinear magnetic Calderón problem by using a first order linearization.

math.AP↗
arXiv2021

Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction

Click-through rate (CTR) prediction is one of the most central tasks in online advertising systems. Recent deep learning-based models that exploit feature embedding and high-order data nonlinearity have shown dramatic successes in CTR prediction. However, these models work poorly on cold-start ads with new IDs, whose embeddings are not well learned yet. In this paper, we propose Graph Meta Embedding (GME) models that can rapidly learn how to generate desirable initial embeddings for new ad IDs based on graph neural networks and meta learning. Previous works address this problem from the new ad itself, but ignore possibly useful information contained in existing old ads. In contrast, GMEs simultaneously consider two information sources: the new ad and existing old ads. For the new ad, GMEs exploit its associated attributes. For existing old ads, GMEs first build a graph to connect them with new ads, and then adaptively distill useful information. We propose three specific GMEs from different perspectives to explore what kind of information to use and how to distill information. In particular, GME-P uses Pre-trained neighbor ID embeddings, GME-G uses Generated neighbor ID embeddings and GME-A uses neighbor Attributes. Experimental results on three real-world datasets show that GMEs can significantly improve the prediction performance in both cold-start (i.e., no training data is available) and warm-up (i.e., a small number of training samples are collected) scenarios over five major deep learning-based CTR prediction models. GMEs can be applied to conversion rate (CVR) prediction as well.

cs.IR↗