arXiv subjects

Jin Li

Publications and source records attributed to Jin Li.

At least 253 records · Page 14Linked to original sources

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↗

arXiv2019

Cosmic opacity: cosmological-model-independent tests from gravitational waves and Type Ia Supernova

In this paper, we present a scheme to investigate the opacity of the Universe in a cosmological-model-independent way, with the combination of current and future available data in gravitational wave (GW) and electromagnetic (EM) domain. In the FLRW metric, GWs propagate freely through a perfect fluid without any absorption and dissipation, which provides a distance measurement unaffected by the cosmic opacity. Focusing on the simulated data of gravitational waves from the third-generation gravitational wave detector (the Einstein Telescope, ET), as well as the newly-compiled SNe Ia data (JLA and Pantheon sample), we find an almost transparent universe is strongly favored at much higher redshifts ($z\sim 2.26$). Our results suggest that, although the tests of cosmic opacity are not significantly sensitive to its parametrization, a strong degeneracy between the cosmic opacity parameter and the absolute \textit{B}-band magnitude of SNe Ia is revealed in this analysis. More importantly, we obtain that future measurements of the luminosity distances of gravitational waves sources will be much more competitive than the current analyses, which makes it expectable more vigorous and convincing constraints on the cosmic opacity (and consequently on background physical mechanisms) and a deeper understanding of the intrinsic properties of type Ia supernovae in a cosmological-model-independent way.

astro-ph.CO↗

arXiv2019

Channel adversarial training for cross-channel text-independent speaker recognition

The conventional speaker recognition frameworks (e.g., the i-vector and CNN-based approach) have been successfully applied to various tasks when the channel of the enrolment dataset is similar to that of the test dataset. However, in real-world applications, mismatch always exists between these two datasets, which may severely deteriorate the recognition performance. Previously, a few channel compensation algorithms have been proposed, such as Linear Discriminant Analysis (LDA) and Probabilistic LDA. However, these methods always require the collections of different channels from a specific speaker, which is unrealistic to be satisfied in real scenarios. Inspired by domain adaptation, we propose a novel deep-learning based speaker recognition framework to learn the channel-invariant and speaker-discriminative speech representations via channel adversarial training. Specifically, we first employ a gradient reversal layer to remove variations across different channels. Then, the compressed information is projected into the same subspace by adversarial training. Experiments on test datasets with 54,133 speakers demonstrate that the proposed method is not only effective at alleviating the channel mismatch problem, but also outperforms state-of-the-art speaker recognition methods. Compared with the i-vector-based method and the CNN-based method, our proposed method achieves significant relative improvement of 44.7% and 22.6% respectively in terms of the Top1 recall.

eess.AS↗

arXiv2019

Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors

In this paper, we study an application of deep learning to the advanced LIGO and advanced Virgo coincident detection of gravitational waves (GWs) from compact binary star mergers. This deep learning method is an extension of the Deep Filtering method used by George and Huerta (2017) for multi-inputs of network detectors. Simulated coincident time series data sets in advanced LIGO and advanced Virgo detectors are analyzed for estimating source luminosity distance and sky location. As a classifier, our deep neural network (DNN) can effectively recognize the presence of GW signals when the optimal signal-to-noise ratio (SNR) of network detectors $\geq$ 9. As a predictor, it can also effectively estimate the corresponding source space parameters, including the luminosity distance $D$, right ascension $α$, and declination $δ$ of the compact binary star mergers. When the SNR of the network detectors is greater than 8, their relative errors are all less than 23%. Our results demonstrate that Deep Filtering can process coincident GW time series inputs and perform effective classification and multiple space parameter estimation. Furthermore, we compare the results obtained from one, two, and three network detectors; these results reveal that a larger number of network detectors results in a better source location.

astro-ph.IM↗

arXiv2019

Testing the Etherington's distance duality relation at higher redshifts: the combination of radio quasars and gravitational waves

In this paper we analyse the implications of the latest cosmological data sets to test the Etherington's distance duality relation (DDR), which connects the luminosity distance $D_L$ and angular diameter distance $D_A$ at the same redshift. For $D_L$ we consider the simulated data of gravitational waves from the third-generation gravitational wave detector (the Einstein Telescope, ET), which can be considered as standard candles (or standard siren), while the angular diameter distances $D_A$ are derived from the newly-compiled sample of compact radio quasars observed by very-long-baseline interferometry (VLBI), which represents a type of new cosmological standard ruler. Alleviating the absorption and scattering effects of dust in the Universe, this will create a valuable opportunity to directly test DDR at much higher precision with the combination of gravitational wave (GW) and electromagnetic (EM) signals. Our results show that, with the combination of the current radio quasar observations, the duality-distance relation can be verified at the precision of $10^{-2}$. Moreover, the Einstein Telescope ET would produce more robust constraints on the validity of such distance duality relation (at the precision of $10^{-3}$), with a larger sample of compact milliarcsecond radio quasars detected in future VLBI surveys.

astro-ph.CO↗

arXiv2019

Flexible Ultrastrong 100-nm Polyethylene Membranes with Polygonal Pore Structures

Robust nanoporous polymer films with approximate 100-nm thicknesses would be broadly applicable in technological areas such as flexible sensors, artificial skins, separators, antireflection and self-cleaning films. However, the creation of these films has been extremely challenging. To date, all reported ultrathin films are of insufficient mechanical strength for use without substrate supports. We describe here the fabrication of a new ultrastrong and highly flexible ultrahigh molecular weight polyethylene (UHMWPE) 100-nm porous membrane. Scanning and transmission electron microscopy evaluations of film microstructures reveal a planar fibrous structure with randomly oriented polygonal interconnected pores. Moreover, the tensile strength of this film is twice as strong as that of solid stainless steel, with a measured in-plane tensile strength and ductility of approximately 900 MPa and 26%, respectively. We further coated our newly developed film with a monolayer graphene to invent an optically transparent and fully conformable piezoresistive skin sensor and thus demonstrated a potential application.

physics.app-ph↗