arXiv subjects

Jing Liu

Publications and source records attributed to Jing Liu.

At least 613 records · Page 34Linked to original sources

arXiv2019

Feature Exploration for Knowledge-guided and Data-driven Approach Based Cuffless Blood Pressure Measurement

This study explores extended feature space that is indicative of blood pressure (BP) changes for better estimation of continuous BP in an unobtrusive way. A total of 222 features were extracted from noninvasively acquired electrocardiogram (ECG) and photoplethysmogram (PPG) signals with the subject undergoing coronary angiography and/or percutaneous coronary intervention, during which intra-arterial BP was recorded simultaneously with the subject at rest and while administering drugs to induce BP variations. The association between the extracted features and the BP components, i.e. systolic BP (SBP), diastolic BP (DBP), mean BP (MBP), and pulse pressure (PP) were analyzed and evaluated in terms of correlation coefficient, cross sample entropy, and mutual information, respectively. Results show that the most relevant indicator for both SBP and MBP is the pulse full width at half maximum, and for DBP and PP, the amplitude between the peak of the first derivative of PPG (dPPG) to the valley of the second derivative of PPG (sdPPG) and the time interval between the peak of R wave and the sdPPG, respectively. As potential inputs to either the knowledge-guided model or data-driven method for cuffless BP calibration, the proposed expanded features are expected to improve the estimation accuracy of cuffless BP.

eess.SP↗

arXiv2019

Studies of helium poisoning of a Hamamatsu R5900-00-M16 photomultiplier

We report results from studies of the helium poisoning of a 16-anode photomultiplier tube R5900-00-M16 manufactured by Hamamatsu Photonics. A tube was immersed in pure helium for a period of about four months and was periodically monitored using a digital oscilloscope. Our results are based on the analysis of waveforms triggered by the dark noise pulses. Collected data yield evidence of after-pulses due to helium contamination of the tube. The probability of after-pulsing increased linearly with the exposure time to helium but the phototube suffered only a small drop in gain, indicating generally strong resilience to helium poisoning.

physics.ins-det↗

arXiv2019

Primordial Black Holes from Cosmic Domain Walls

We investigate the formation of primordial black holes (PBHs) from the collapse of spherically symmetric domain wall bubbles, which spontaneously nucleate via quantum tunneling during inflation. Since the tension of domain walls changes with time and so domain walls nucleate in a short time interval, the mass function of PBHs in general has a spike-like structure. In contrast to models in which PBHs produced from overdense regions, our model avoids the uncertainties of PBHs production mechanism. PBHs from domain walls with mass around $10^{20}\mathrm{g}$ may constitute all dark matter, those with mass around $10^{34}\mathrm{g}$ can explain the merger events of binary black holes detected by LIGO.

astro-ph.CO↗

arXiv2019

Aligning Linguistic Words and Visual Semantic Units for Image Captioning

Image captioning attempts to generate a sentence composed of several linguistic words, which are used to describe objects, attributes, and interactions in an image, denoted as visual semantic units in this paper. Based on this view, we propose to explicitly model the object interactions in semantics and geometry based on Graph Convolutional Networks (GCNs), and fully exploit the alignment between linguistic words and visual semantic units for image captioning. Particularly, we construct a semantic graph and a geometry graph, where each node corresponds to a visual semantic unit, i.e., an object, an attribute, or a semantic (geometrical) interaction between two objects. Accordingly, the semantic (geometrical) context-aware embeddings for each unit are obtained through the corresponding GCN learning processers. At each time step, a context gated attention module takes as inputs the embeddings of the visual semantic units and hierarchically align the current word with these units by first deciding which type of visual semantic unit (object, attribute, or interaction) the current word is about, and then finding the most correlated visual semantic units under this type. Extensive experiments are conducted on the challenging MS-COCO image captioning dataset, and superior results are reported when comparing to state-of-the-art approaches.

cs.CV↗

arXiv2019

Evolution of Incommensurate Superstructure and Electronic Structure with Pb Substitution in (Bi$_{2-x}$Pb$_{x}$)Sr$_2$CaCu$_2$O$_{8+δ}$ Superconductors

High-quality Bi$_{2-x}$Pb$_{x}$Sr$_2$CaCu$_2$O$_{8+δ}$ (Bi2212) single crystals have been successfully grown by the traveling solvent floating zone technique with a wide range of Pb substitution ($x=0-0.8$). The samples are characterized by transmission electron microscope (TEM) and measured by high resolution laser-based angle-resolved photoemission spectroscopy (ARPES) with different photon energies. A systematic evolution of the electronic structure and superstructure with Pb substitution has been revealed for the first time. The superstructure shows a significant change with Pb substitution and the incommensurate modulation vector ($\textbf{Q}$) decreases with increasing Pb substitution. In the meantime, the superstructure intensity from ARPES measurements also decreases dramatically with increasing Pb concentration. The superstructure in Bi2212 can be effectively suppressed by Pb substitution and it nearly disappears with a Pb substitution of $x=0.8$. We also find that the superstructure bands in ARPES measurements depend sensitively on the photon energy of lasers used; they can become even stronger than the main band when using a laser photon energy of 10.897 eV. These results provide important information on the origin of the incommensurate superstructure and its control and suppression in bismuth-based high temperature superconductors.

cond-mat.supr-con↗

arXiv2019

Low-frequency Gravitational Waves from Double-inflection-point Inflation

We study the production of gravitational waves from primordial scalar perturbations in double-inflection-point inflation, in which one of the inflection points predicts the power spectra consistent with CMB observations at large scales and the other generates a large peak in the power spectrum of scalar perturbations at small scales. We find that the reduced gravitational waves at low frequencies can be detected by future space-based laser interferometers.

astro-ph.CO↗

arXiv2019

A General Deep Learning Framework for Network Reconstruction and Dynamics Learning

Many complex processes can be viewed as dynamical systems on networks. However, in real cases, only the performances of the system are known, the network structure and the dynamical rules are not observed. Therefore, recovering latent network structure and dynamics from observed time series data are important tasks because it may help us to open the black box, and even to build up the model of a complex system automatically. Although this problem hosts a wealth of potential applications in biology, earth science, and epidemics etc., conventional methods have limitations. In this work, we introduce a new framework, Gumbel Graph Network (GGN), which is a model-free, data-driven deep learning framework to accomplish the reconstruction of both network connections and the dynamics on it. Our model consists of two jointly trained parts: a network generator that generating a discrete network with the Gumbel Softmax technique; and a dynamics learner that utilizing the generated network and one-step trajectory value to predict the states in future steps. We exhibit the universality of our framework on different kinds of time-series data: with the same structure, our model can be trained to accurately recover the network structure and predict future states on continuous, discrete, and binary dynamics, and outperforms competing network reconstruction methods.

cond-mat.dis-nn↗

arXiv2019

Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling

0-1 knapsack is of fundamental importance in computer science, business, operations research, etc. In this paper, we present a deep learning technique-based method to solve large-scale 0-1 knapsack problems where the number of products (items) is large and/or the values of products are not necessarily predetermined but decided by an external value assignment function during the optimization process. Our solution is greatly inspired by the method of Lagrange multiplier and some recent adoptions of game theory to deep learning. After formally defining our proposed method based on them, we develop an adaptive gradient ascent method to stabilize its optimization process. In our experiments, the presented method solves all the large-scale benchmark KP instances in a minute whereas existing methods show fluctuating runtime. We also show that our method can be used for other applications, including but not limited to the point cloud resampling.

cs.LG↗

arXiv2019

Distinct Superconducting Gap on Two Bilayer-Split Fermi Surface Sheets in $Bi_2Sr_2CaCu_2O_{8+δ}$ Superconductor

High resolution laser-based angle-resolved photoemission measurements were carried out on an overdoped $Bi_2Sr_2CaCu_2O_{8+δ}$ superconductor with a Tc of 75 K. Two Fermi surface sheets caused by bilayer splitting are clearly identified with rather different doping levels: the bonding sheet corresponds to a doping level of 0.14 which is slightly underdoped while the antibonding sheet has a doping of 0.27 that is heavily overdoped, giving an overall doping level of 0.20 for the sample. Different superconducting gap sizes on the two Fermi surface sheets are revealed for the first time. The superconducting gap on the antibonding Fermi surface sheet follows a standard d-wave form while it deviates from the standard d-wave form for the bonding Fermi surface sheet. The maximum gap difference between the two Fermi surface sheets near the antinodal region is $\sim$2 meV. These observations provide important information for studying the relationship between the Fermi surface topology and superconductivity, and the layer-dependent superconductivity in high temperature cuprate superconductors.

cond-mat.supr-con↗

arXiv2019

Gravitational wave production after inflation with cuspy potentials

We investigate the effect of the cuspiness of scalar potentials on the production of gravitational waves during oscillon formation after inflation. We consider a more general form of potentials with a mass parameter $M$, which repoduce cuspy potentials for fields much larger than $M$, and smooth potentials in the opposite limit. For cuspy potentials, nonsmooth oscillations of the inflaton induce an amplification of the inflaton fluctuations at the bottom of the potential, so that oscillons copiously form, which leads to a significant stochastic gravitational wave background with a double-peak spectrum. By varying the parameter $M$, we find that cuspy potentials yield stronger signals of gravitational waves and the generation of gravitational waves disappears for smooth potentials. Moreover, we calculate the equation of state after inflation and find the presence of a quasi matter-dominated stage right before the transition to the radiation-dominated stage.

astro-ph.CO↗

arXiv2019

Dual Attention Network for Scene Segmentation

In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the selfattention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data. We make the code and trained model publicly available at https://github.com/junfu1115/DANet

cs.CV↗

arXiv2019

Synergistic Effects of Nanoparticle Heating and Amoxicillin on H. Pylori Inhibition

We report the design and development of a dual-functional magnetic nanoparticle platform for potential treatment of H. pylori infection. We show that an ultralow concentration of Mn0.3Fe2.7O4@SiO2 nanoparticles subjected to a moderate AC magnetic field, without bulk heating effect, can deposit heat locally and effectively inhibit H. pylori growth and virulence in vitro. When coupled with antibiotic amoxicillin, the dual-functional amoxicillin loaded Mn0.3Fe2.7O4@SiO2 further decreases the bacteria survival rate by a factor of 7 and 5, respectively, compared to amoxicillin treatment and nanoparticle heating alone. The synergistic effect can be partially attributed to the heating induced damage to the cell membrane and protective biofilm, which may increase the permeability of antibiotics to bacteria. Our method provides a viable approach to treat H. pylori infection, with the potential of reducing side effects and enhancing the efficacy for combating drug resistant strains.

physics.app-ph↗

arXiv2019

A Collision Probability Estimation Algorithm Used in the Space Debris Evolutionary Model

An in-depth analysis is performed on the problem that one parameter of the Cube model can affects the final simulation results of space debris long-term evolution model, which weakens the representativeness of the space debris evolution model. We made some improvements and proposed an Improved-Cube (I-Cube) model. By multiple Monte Carlo simulations, it is indicated that the I-Cube model offered a more accurate and more reasonable option for collision probability estimation in the space debris evolution process. The simulation results of space debris long-term evolution model are no longer sensitive to the collision probability estimation model parameters, thus improved the reliability of space debris long-term evolution model.

physics.space-ph↗

arXiv2019

An introduction to a new space debris evolution model -- SOLEM

SOLEM is the first space debris long-term evolution model of China. This paper describes the principles, components and workflow of the SOLEM. The effects of different mitigation measures based on SOLEM model are analyzed and presented. The limitation of the model is pointed out and its future improvement work-plan is prospected.

astro-ph.EP↗

arXiv2019

Controlling exciton dynamics in two-dimensional MoS2 on hyperbolic metamaterial-based nanophotonic platform

The discovery of two-dimensional transition metal dichalcogenides (2D TMDs) has promised next-generation photonics and optoelectronics applications, particularly in the realm of nanophotonics. Arguably, the most crucial fundamental processes in these applications are the exciton migration and charge transfer in 2D TMDs. However, exciton dynamics in 2D TMDs have never been studied on a nanophotonic platform and more importantly, the control of exciton dynamics by means of nanophotonic structures has yet to be explored. Here, for the first time, we demonstrate the control of exciton dynamics in MoS2 monolayers by introducing a hyperbolic metamaterial (HMM) substrate. We reveal the migration mechanisms of various excitons in MoS2 monolayers. Furthermore, we demonstrate the Förster radius of the A-excitons can be increased by introducing HMMs through the nonlocal effects of HMMs due to the Purcell effect. On the other hand, the diffusion coefficient is unchanged for the C-excitons on HMMs. This study provides a revolutionary step forward in enabling 2D TMD nanophotonics hybrid devices.

cond-mat.mtrl-sci↗

arXiv2019

Towards Time-Aware Distant Supervision for Relation Extraction

Distant supervision for relation extraction heavily suffers from the wrong labeling problem. To alleviate this issue in news data with the timestamp, we take a new factor time into consideration and propose a novel time-aware distant supervision framework (Time-DS). Time-DS is composed of a time series instance-popularity and two strategies. Instance-popularity is to encode the strong relevance of time and true relation mention. Therefore, instance-popularity would be an effective clue to reduce the noises generated through distant supervision labeling. The two strategies, i.e., hard filter and curriculum learning are both ways to implement instance-popularity for better relation extraction in the manner of Time-DS. The curriculum learning is a more sophisticated and flexible way to exploit instance-popularity to eliminate the bad effects of noises, thus get better relation extraction performance. Experiments on our collected multi-source news corpus show that Time-DS achieves significant improvements for relation extraction.

cs.CL↗

arXiv2019

Multiqubit matter-wave interferometry under decoherence and the Heisenberg scaling recovery

Most matter-wave interferometry (MWI) schemes for quantum sensing are so far evaluated in ideal situations without noises. In this work, we provide assessments of generic multiqubit MWI schemes under Markovian dephasing noises. We find that for certain classes of the MWI schemes with scale factors that are nonlinearly dependent on the interrogation time, the optimal precision of maximally entangled probes \emph{decreases} with increasing the particle number $N$, for both independent and collective dephasing situations. This result challenges the conventional wisdom found in dephasing Ramsey-type interferometers. We initiate the analyses by investigating the optimal precision of multiqubit Sagnac atom interferometry for rotation sensing. And we show that due to the competition between the unconventional interrogation-time quadratic phase accumulation and the exponential dephasing processes, the Greenberger-Horne-Zeilinger (GHZ) state, which is the optimal input state in noiseless scenarios, leads to vanishing quantum Fisher information in the large-$N$ regime. Then our assessments are further extended to generic MWI schemes for quantum sensing with entangled states and under decoherence. Finally, a quantum error-correction logical GHZ state is tentatively analyzed, which could have the potential to recover the Heisenberg scaling and improve the sensitivity.

quant-ph↗

arXiv2019

Detailed Electronic Structure of the Three-Dimensional Fermi Surface and its Sensitivity to Charge Density Wave Transition in ZrTe3 Revealed by High Resolution Laser-Based Angle-Resolved Photoemission Spectroscopy

The detailed information of the electronic structure is the key for understanding the nature of charge density wave (CDW) order and its relationship with superconducting order in microscopic level. In this paper, we present high resolution laser-based angle-resolved photoemission spectroscopy (ARPES) study on the three-dimensional (3D) hole-like Fermi surface around the Brillouin zone center in a prototypical qusi-one-dimensional CDW and superconducting system ZrTe3. Double Fermi surface sheets are clearly resolved for the 3D hole-like Fermi surface around the zone center. The 3D Fermi surface shows a pronounced shrinking with increasing temperature. In particular, the quasiparticle scattering rate along the 3D Fermi surface experiences an anomaly near the charge density wave transition temperature of ZrTe3 (~63 K). Signature of electron-phonon coupling is observed with a dispersion kink at ~20 meV; the strength of the electron-phonon coupling around the 3D Fermi surface is rather weak. These results indicate that the 3D Fermi surface is also closely connected to the charge-density-wave transition and suggest a more global impact on the entire electronic structure induced by CDW phase transition in ZrTe3.

cond-mat.mtrl-sci↗