arXiv subjects

Jin Li

Publications and source records attributed to Jin Li.

At least 253 records · Page 14Linked to original sources

arXiv2020

PrivacyGuard: Enforcing Private Data Usage Control with Blockchain and Attested Off-chain Contract Execution

The abundance and rich varieties of data are enabling many transformative applications of big data analytics that have profound societal impacts. However, there are also increasing concerns regarding the improper use of individual data owner's private data. In this paper, we propose PrivacyGuard, a system that leverages blockchain smart contract and trusted execution environment (TEE) to enable individual's control over the access and usage of their private data. Smart contracts are used to specify data usage policy, i.e., who can use what data under which conditions and what analytics to perform, while the distributed blockchain ledger is used to keep an irreversible and non-repudiable data usage record. To address the efficiency problem of on-chain contract execution and to prevent exposing private data on the publicly viewable blockchain, PrivacyGuard incorporates a novel TEE-based off-chain contract execution engine along with a protocol to securely commit the execution result onto blockchain. We have built and deployed a prototype of PrivacyGuard with Ethereum and Intel SGX. Our experiment result demonstrates that PrivacyGuard fulfills the promised privacy goal and supports analytics on data from a considerable number of data owners.

cs.CR↗

arXiv2020

SL($n$) contravariant vector valuations

All SL($n$) contravariant vector valuations on polytopes in $\mathbb R^n$ are completely classified without any additional assumptions. The facet vector is defined. It turns out to be the unique such valuation for $n\geq3$. In dimension two, the classification corresponds to the case of SL(2) covariant valuations.

math.MG↗

arXiv2020

Affine function valued valuations

A classification of SL$(n)$ contravariant, continuous function valued valuations on convex bodies is established. Such valuations are natural extensions of SL$(n)$ contravariant $L_p$ Minkowski valuations, the classification of which characterized $L_p$ projection bodies, which are fundamental in the $L_p$ Brunn-Minkowski theory, for $p \geq 1$. Hence our result will help to better understand extensions of the $L_p$ Brunn-Minkowski theory. In fact, our results characterize general projection functions which extend $L_p$ projection functions ($p$-th powers of the support functions of $L_p$ projection bodies) to projection functions in the $L_p$ Brunn-Minkowski theory for $0< p < 1$ and in the Orlicz Brunn-Minkowski theory.

math.MG↗

arXiv2020

New structure canditates for the experimentally synthesized heptazine-based and triazine-based two dimensional graphitic carbon nitride

The widely used crystal structures for both heptazine-based and triazine-based two-dimensional (2D) graphitic carbon nitride (g-C$_3$N$_4$) are the flat P-6m2 configurations. However, the experimentally synthesized 2D g-C$_3$N$_4$ possess thickness ranging in 0.2-0.5 nm, indicating that the theoretically used flat P-6m2 configurations are not the correct ground states. In this work, we propose three new corrugated structures P321, P3m1 and Pca21 with energies of 66 (86), 77 (87) and 78 (89) meV/atom lower than that of the corresponding heptazine-based (triazine-based) g-C$_3$N$_4$ in flat P-6m2 configuration, respectively. These corrugated structures have very similar periodic patterns to the flat P-6m2 ones and they are difficult to be distinguished from each other according to their top-views. The optimized thicknesses of the three corrugated structures ranging in 1.347-3.142 Å are in good agreement with the experimental results. The first-principles results show that these corrugated structural candidates are also semiconductors with band gaps slightly larger than those of the correspondingly flat P-6m2 ones. Furthermore, they possess also suitable band edge positions for sun-light-driven water-splitting at both $pH=0$ and $pH=7$ environments. Our results show that these three new structures are more promising candidates for the experimentally synthesized g-C$_3$N$_4$.

cond-mat.mtrl-sci↗

arXiv2020

Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising

Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc. These practical problems usually exhibit two distinct features: large-scale and dynamic, which requires the matching algorithm to be repeatedly executed at regular intervals. However, existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource. To address this issue, we propose \texttt{NeuSearcher} which leverages the knowledge learned from previously instances to solve new problem instances. Specifically, we design a multichannel graph neural network to predict the threshold of the matched edges weights, by which the search region could be significantly reduced. We further propose a parallel heuristic search algorithm to iteratively improve the solution quality until convergence. Experiments on both open and industrial datasets demonstrate that \texttt{NeuSearcher} can speed up 2 to 3 times while achieving exactly the same matching solution compared with the state-of-the-art approximation approaches.

cs.DC↗

arXiv2020

Frequency stabilization and tuning of breathing soliton in SiN microresonators

Dissipative Kerr soliton offers broadband coherent and low-noise frequency comb and stable temporal pulse train, having shown great potential applications in spectroscopy, communications, and metrology. Breathing soliton is a particular dissipative Kerr soliton that the pulse duration and peak intensity show periodic oscillation. However, the noise and stability of the breathing soliton is still remaining unexplored, which would be the main obstacle for future applications. Here, we have investigated the breathing dissipative Kerr solitons in the silicon nitride (SiN) microrings, while the breather period shows uncertainties around MHz in both simulation and experiments. By applying a modulated pump, the breathing frequency can be injectively locked to the modulation and tuned over tens of MHz with frequency noise significantly suppressed. Our demonstration offers an alternative knob for the controlling of soliton dynamics in microresonator and paves a new avenue towards practical applications of breathing soliton.

physics.optics↗

arXiv2020

Fast Reinforcement Learning for Anti-jamming Communications

This letter presents a fast reinforcement learning algorithm for anti-jamming communications which chooses previous action with probability $τ$ and applies $ε$-greedy with probability $(1-τ)$. A dynamic threshold based on the average value of previous several actions is designed and probability $τ$ is formulated as a Gaussian-like function to guide the wireless devices. As a concrete example, the proposed algorithm is implemented in a wireless communication system against multiple jammers. Experimental results demonstrate that the proposed algorithm exceeds Q-learing, deep Q-networks (DQN), double DQN (DDQN), and prioritized experience reply based DDQN (PDDQN), in terms of signal-to-interference-plus-noise ratio and convergence rate.

eess.SP↗

arXiv2020

Theoretical prediction of a low-energy Stone-Wales graphene with intrinsic type-III Dirac-cone

Based on first-principles method we predict a new low-energy Stone-Wales graphene SW40, which has an orthorhombic lattice with Pbam symmetry and 40 carbon atoms in its crystalline cell forming well-arranged Stone-Wales patterns. The calculated total energy of SW40 is just about 133 meV higher than that of graphene, indicating its excellent stability exceeds all the previously proposed graphene allotropes. We find that SW40 processes intrinsic Type-III Dirac-cone (Phys. Rev. Lett., 120, 237403, 2018) formed by band-crossing of a local linear-band and a local flat-band, which can result in highly anisotropic Fermions in the system. Interestingly, such intrinsic type-III Dirac-cone can be effectively tuned by inner-layer strains and it will be transferred into Type-II and Type-I Dirac-cones under tensile and compressed strains, respectively. Finally, a general tight-binding model was constructed to understand the electronic properties nearby the Fermi-level in SW40. The results show that type-III Dirac-cone feature can be well understood by the $π$-electron interactions between adjacent Stone-Wales defects.

physics.comp-ph↗

arXiv2020

Controllable List-wise Ranking for Universal No-reference Image Quality Assessment

No-reference image quality assessment (NR-IQA) has received increasing attention in the IQA community since reference image is not always available. Real-world images generally suffer from various types of distortion. Unfortunately, existing NR-IQA methods do not work with all types of distortion. It is a challenging task to develop universal NR-IQA that has the ability of evaluating all types of distorted images. In this paper, we propose a universal NR-IQA method based on controllable list-wise ranking (CLRIQA). First, to extend the authentically distorted image dataset, we present an imaging-heuristic approach, in which the over-underexposure is formulated as an inverse of Weber-Fechner law, and fusion strategy and probabilistic compression are adopted, to generate the degraded real-world images. These degraded images are label-free yet associated with quality ranking information. We then design a controllable list-wise ranking function by limiting rank range and introducing an adaptive margin to tune rank interval. Finally, the extended dataset and controllable list-wise ranking function are used to pre-train a CNN. Moreover, in order to obtain an accurate prediction model, we take advantage of the original dataset to further fine-tune the pre-trained network. Experiments evaluated on four benchmark datasets (i.e. LIVE, CSIQ, TID2013, and LIVE-C) show that the proposed CLRIQA improves the state of the art by over 9% in terms of overall performance. The code and model are publicly available at https://github.com/GZHU-Image-Lab/CLRIQA.

eess.IV↗

arXiv2019

URSA: Precise Capacity Planning and Contention-aware Scheduling for Public Clouds

Database platform-as-a-service (dbPaaS) is developing rapidly and a large number of databases have been migrated to run on the Clouds for the low cost and flexibility. Emerging Clouds rely on the tenants to provide the resource specification for their database workloads. However, they tend to over-estimate the resource requirement of their databases, resulting in the unnecessarily high cost and low Cloud utilization. A methodology that automatically suggests the "just-enough" resource specification that fulfills the performance requirement of every database workload is profitable. To this end, we propose URSA, a capacity planning and workload scheduling system for dbPaaS Clouds. USRA is comprised by an online capacity planner, a performance interference estimator, and a contention-aware scheduling engine. The capacity planner identifies the most cost-efficient resource specification for a database workload to achieve the required performance online. The interference estimator quantifies the pressure on the shared resource and the sensitivity to the shared resource contention of each database workload. The scheduling engine schedules the workloads across Cloud nodes carefully to eliminate unfair performance interference between the co-located workloads. Our real system experimental results show that URSA reduces 27.5% of CPU usage and 53.4% of memory usage for database workloads while satisfying their performance requirements. Meanwhile, URSA reduces the performance unfairness between the co-located workloads by 42.8% compared with the Kubernetes.

cs.DC↗

arXiv2019

Use of Hydrophobic-Hydrophobic Interactions for Direct Graphene Transfer

A new large-area wet transfer method to transfer graphene directly onto UHMWPE membranes without organic-coating, thermal treatment or other mediated substrate was developed. This method completely avoids the problems of PMMA-residue and possible PMMA coating induced tear-off by harnessing hydrophobic-hydrophobic interactions between graphene and UHMWPE, which is one of most facile and cheapest transfer methods that are currently available, especially for large-area graphene preparation and transfer. Multi-layer sandwich structure composite membrane of graphene and UHMWPE with desired number of layers can also be easily prepared by repeating the transfer method. The most significant applications of this transfer method lie on that it facilitates thin polymer membranes the possibilities to be directly observed with high magnification by electron microscopes, such as SEM and TEM. Thus the crystallization and phase-behavior of polymers including UHMWPE can be investigated with real-time and in-situ morphological observations, even in the presence of their solvent and with the need of thermal treatment. Morphologies and mechanical properties of stretched graphene/UHMWPE layered composite membranes were investigated, and strengthening effect of graphene on one side and both sides was observed.

cond-mat.mtrl-sci↗

arXiv2019

Direct test of the FLRW metric from strongly lensed gravitational wave observations

The assumptions of large-scale homogeneity and isotropy underly the familiar Friedmann-Lema\^ıtre-Robertson-Walker (FLRW) metric that appears to be an accurate description of our Universe. In this paper, we propose a new strategy of testing the validity of the FLRW metric, based on the galactic-scale lensing systems where strongly lensed gravitational waves and their electromagnetic counterparts can be simultaneously detected. Each strong lensing system creates opportunity to infer the curvature parameter of the Universe. Consequently, combined analysis of many such systems will provide a model-independent tool to test the validity of the FLRW metric. Our study demonstrates that the third-generation ground based GW detectors, like the Einstein Telescope (ET) and space-based detectors, like the Big Bang Observer (BBO), are promising concerning determination of the curvature parameter or possible detection of deviation from the FLRW metric. Such accurate measurements of the FLRW metric can become a milestone in precision GW cosmology.

astro-ph.CO↗

arXiv2019

Identifying extra high frequency gravitational waves generated from oscillons with cuspy potentials using deep neural networks

During oscillations of cosmology inflation around the minimum of a cuspy potential after inflation, the existence of extra high frequency gravitational waves (HFGWs) (GHz) has been proven effectively recently. Based on the electromagnetic resonance system for detecting such extra HFGWs, we adopt a new data processing scheme to identify the corresponding GW signal, which is the transverse perturbative photon fluxes (PPF). In order to overcome the problems of low efficiency and high interference in traditional data processing methods, we adopt deep learning to extract PPF and make some source parameters estimation. Deep learning is able to provide an effective method to realize classification and prediction tasks. Meanwhile, we also adopt anti-overfitting technique and make adjustment of some hyperparameters in the course of study, which improve the performance of classifier and predictor to a certain extent. Here the convolutional neural network (CNN) is used to implement deep learning process concretely. In this case, we investigate the classification accuracy varying with the ratio between the number of positive and negative samples. When such ratio exceeds to 0.11, the accuracy could reach up to 100%.

physics.data-an↗

arXiv2019

Tip enhanced IR imaging with sub-10 nm resolution and hypersensitivity

IR spectroscopy has been widely used for chemical identification and quantitative analysis of reactions occurring in a specific time and space domains by measuring an average signal of the entire system1. Achieving IR measurements with nanometer-scale spatial resolution is highly desirable to obtain a detailed understanding of the composition, structure and function of interfaces2-5. The challenges in IR nanoscopy yet exist owing to the small molecular cross section and pristine optical diffraction limit. Although atomic force microscopy (AFM) based techniques, such as scattering-type scanning near-field optical microscopy and photothermal-induced resonance microscopy (PTIR), can acquire IR spectroscopy in a few tens of nanometer scale resolution6-9, IR measurements with monolayer level sensitivity remains elusive and can only be realized under critical conditions10,11. Herein, we demonstrate sub-10 nm spatial resolution sampling a volume of ~360 molecules with a strong field enhancement at the sample-tip junction by implementing noble metal substrates (Au, Ag, Pt) in photo-induced force microscopy (PiFM). This technique shows versatility and robustness of PiFM, and is promising for application in interfacial studies with hypersensitivity and super spatial resolution.

physics.optics↗

arXiv2019

On the boundary condition and related instability in the Smoothed Particle Hydrodynamics

In this work, we explore various relevant aspects of the Smoothed Particle Hydrodynamics regarding Burger's equation. The stability, precision, and efficiency of the algorithm are investigated in terms of different implementations. In particular, we argue that the boundary condition plays an essential role in the stability of numerical implementation. Besides, the issue is shown to be closely associated with the initial particle distribution and the interpolation scheme. Among others, we introduce an interpolation scheme termed symmetrized finite particle method. The main advantage of the scheme is that its implementation does not involve any derivative of the kernel function. Concerning the equation of motion, the calculations are carried out using two distinct scenarios where the particles are chosen to be either stationary or dynamically evolved. The obtained results are compared with those obtained by using the standard finite difference method for spatial derivatives. Our numerical results indicate subtle differences between different schemes regarding the choice of boundary condition. In particular, a novel type of instability is observed where the regular distribution is compromised as the particles start to traverse each other. Implications and further discussions of the present study are also addressed.

physics.comp-ph↗

arXiv2019

Isolated singularities of flat metrics on Riemann surfaces

Robert Bryant (Theorie des varietes minimales et applications, 1988, 154: 321-347) proved that an isolated singularity of a conformal metric of positive constant curvature on a Riemann surface is a conical one. Using Complex Analysis, we find all of the local models for an isolated singularity of a flat metric whose area satisfies some polynomial growth condition near the singularity. In particular, we show that an isolated singularity of a flat metric with finite area is also a conical one.

math.DG↗

arXiv2019

Universal Transforming Geometric Network

The recurrent geometric network (RGN), the first end-to-end differentiable neural architecture for protein structure prediction, is a competitive alternative to existing models. However, the RGN's use of recurrent neural networks (RNNs) as internal representations results in long training time and unstable gradients. And because of its sequential nature, it is less effective at learning global dependencies among amino acids than existing transformer architectures. We propose the Universal Transforming Geometric Network (UTGN), an end-to-end differentiable model that uses the encoder portion of the Universal Transformer architecture as an alternative for internal representations. Our experiments show that compared to RGN, UTGN achieve a $1.7$ \si{\angstrom} improvement on the free modeling portion and a $0.7$ \si{\angstrom} improvement on the template based modeling of the CASP12 competition.

q-bio.BM↗

arXiv2019

Performance study of a directional fast neutron detection system based on TPC and plastic scintillation detectors

A neutron time projection chamber can locate the approximate direction of a neutron hot spot with high efficiency and a 4π field of view. The angular resolution can be significantly improved by adding several plastic scintillation detectors and using coincidence events. The specific performances of such a coincidence imaging system are studied based on theoretical calculations and experimental results. The calculated value of the angular resolution is approximately 2° for the current system, which agrees well with the experimental results and sets an upper limit for the angular resolution of traditional back projection based online reconstruction methods. Although the statistical iterative method can breakthrough this limit and further improve the angular resolution, the time consumption is usually a problem. The coincidence imaging system can be further optimized for future applications based on the theoretical model.

physics.ins-det↗