arXiv subjects

Li Zhang

Publications and source records attributed to Li Zhang.

At least 847 records · Page 47Linked to original sources

arXiv2018

Real-time programmable mode-locked fiber laser with a human-like algorithm

Nonlinear polarized evolution-based passively mode-locked fiber lasers with ultrafast and high peak power pulses are a powerful tool for engineering applications and research. However, their sensitivity to polarization limits their widespread application. To address this, automatic mode-locking immune to environmental disturbances is attracting more concerns. An experimental demonstration of the first real-time programmable mode-locked fiber laser is presented, which is enabled by our proposed human-like algorithm. It combines human logic with a machine's speed, computing capability, and precision. The laser automatically locks onto multiple operation regimes such as fundamental mode-locking, harmonic mode-locking, Q-switching, and even Q-switched mode-locking without physically altering its structure. The average initial mode-locking times (with randomly-assigned original polarization states and recover time from detachment under 10 successive experiments) are only 3.1 s and 58.9 ms. We believe this intelligent laser's superior performance can find practical applications in engineering and scientific research.

physics.ins-det↗

arXiv2018

Vectorial nonlinear optical generation

Nonlinear optical generation has been a well-established way to realize frequency conversion in nonlinear optics, whereas previous studies were just focusing on the scalar light fields. Here we report a concise yet efficient experiment to realize frequency conversion from vector fields to vector fields based on the vectorial nonlinear optical process, e.g., the second-harmonic generation. Our scheme is based on two cascading type-I phase-matching BBO crystals, whose fast axes are configured elaborately to be perpendicular to each other. Without loss of generality, we take the full Poincaré beams as the vectorial light fields in our experiment, and visualize the structured features of vectorial second-harmonic fields by using Stokes polarimetry. The interesting doubling effect of polarization topological index, i.e., a low-order full Poincaré beam is converted to a high-order one are demonstrated. However, polarization singularities of both C-points and L-lines are found to keep invariant during the SHG process. Our scheme can be straightforwardly generalized to other nonlinear optical effects. Our scheme can offer a deeper understanding on the interaction of vectorial light with media and may find important applications in optical imaging, optical communication and quantum information science.

physics.optics↗

arXiv2018

Centralized adaptive traffic control strategy design across multiple intersections based on vehicle path flows: An approximated Lagrangian decomposition approach

In this paper, we first present a centralized traffic control model based on the emerging dynamic path flows. This new model in essence views the whole target network as one integral piece in which traffic propagates based on traffic flow dynamics, vehicle paths, and traffic control. In light of this centralized traffic control concept, most requirements for the existing traffic control coordination will be dropped in the optimal traffic control operations, such as the common cycle length or offsets. Instead, the optimal traffic control strategy across intersections will be highly adaptive over time to minimize the total travel time and it can also prevent any short-term traffic congestions according to the space-time characteristics of vehicle path flows. A mixed integer linear programming (MILP) formulation is then presented to model the propagations of path flows given a centralized traffic control strategy. Secondly, a new approximated Lagrangian decomposition framework is presented. Solving the proposed MILP model with the traditional Lagrangian decomposition approach faces the leveraging challenge between the problem tractability after decomposition and computing complexity. To address this issue, we propose an approximate Lagrangian decomposition framework in which the target problem is reformulated into an approximated problem by constructing its complicating attributes into the objective function instead of in constraints to reduce the computing complexity. The computing efficiency loss of the objective function can be mitigated via multiple advanced computing techniques. With the proposed approximated approach, the upper bound and tight lower bound can be obtained via two customized dynamic network loading models. In the end, one demonstrative and one real-world example are illustrated to show the validness, robustness, and scalability of the new approach.

eess.SY↗

arXiv2018

Unsupervised Learnable Sinogram Inpainting Network (SIN) for Limited Angle CT reconstruction

In this paper, we propose a sinogram inpainting network (SIN) to solve limited-angle CT reconstruction problem, which is a very challenging ill-posed issue and of great interest for several clinical applications. A common approach to the problem is an iterative reconstruction algorithm with regularization term, which can suppress artifacts and improve image quality, but requires high computational cost. The starting point of this paper is the proof of inpainting function for limited-angle sinogram is continuous, which can be approached by neural networks in a data-driven method, granted by the universal approximation theorem. Based on this, we propose SIN as the fitting function -- a convolutional neural network trained to generate missing sinogram data conditioned on scanned data. Besides CNN module, we design two differentiable and rapid modules, Radon and Inverse Radon Transformer network, to encapsulate the physical model in the training procedure. They enable new joint loss functions to optimize both sinogram and reconstructed image in sync, which improved the image quality significantly. To tackle the labeled data bottleneck in clinical research, we form a sinogram-image-sinogram closed loop, and the difference between sinograms can be used as training loss. In this way, the proposed network can be self-trained, with only limited-angle data for unsupervised domain adaptation. We demonstrate the performance of our proposed network on parallel beam X-ray CT in lung CT datasets from Data Science Bowl 2017 and the ability of unsupervised transfer learning in Zubal's phantom. The proposed method performs better than state-of-art method SART-TV in PSNR and SSIM metrics, with noticeable visual improvements in reconstructions.

physics.med-ph↗

arXiv2018

Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity

Sentence encoders, which produce sentence embeddings using neural networks, are typically evaluated by how well they transfer to downstream tasks. This includes semantic similarity, an important task in natural language understanding. Although there has been much work dedicated to building sentence encoders, the accompanying transfer learning techniques have received relatively little attention. In this paper, we propose a transfer learning setting specialized for semantic similarity, which we refer to as direct network transfer. Through experiments on several standard text similarity datasets, we show that applying direct network transfer to existing encoders can lead to state-of-the-art performance. Additionally, we compare several approaches to transfer sentence encoders to semantic similarity tasks, showing that the choice of transfer learning setting greatly affects the performance in many cases, and differs by encoder and dataset.

cs.CL↗

arXiv2018

Magnetic Navigation of a Rotating Colloidal Swarm Using Ultrasound Images

Microrobots are considered as promising tools for biomedical applications. However, the imaging of them becomes challenges in order to be further applied on in vivo environments. Here we report the magnetic navigation of a paramagnetic nanoparticle based swarm using ultrasound images. The swarm can be generated using simple rotating magnetic fields, resulting in a region containing particles with a high area density. Ultrasound images of the swarm shows a periodic changing of imaging contrast. The reason for such dynamic contrast has been analyzed and experimental results are presented. Moreover, this swarm exhibits enhanced ultrasound imaging in comparison to that formed by individual nanoparticles with a low area density, and the relationship between imaging contrast and area density is testified. Furthermore, the microrobotic swarm can be navigated near a solid surface at different velocities, and the imaging contrast show negligible changes. This method allows us to localize and navigate a microrobotic swarm with enhanced ultrasound imaging indicating a promising approach for imaging of microrobots.

cs.RO↗

arXiv2018

A Magnetically-Triggered Soft Capsule for On-Demand Mucus Collection

In this work, we present a soft capsule for mucus collection in human intestine for diagnostic purpose, with reduced risk of tissue damage compared with other biopsy methods. The capsule implements passive locomotion and the sampling process is triggered by magnetic field using a permanent magnet, which is placed on the skin above the region of interest (ROI). The capsule contains a soft vacuum chamber which is sealed with wax. When magnetic field and mucus are present simultaneously, the circuit inside the capsule will be closed and nichrome wire will start to generate heat to melt the wax, and mucus will be collected into the vacuum chamber due to air pressure. Experiments on heating capability of nichrome wire, mucus collection and reliability of the capsule are conducted to validate this design.

physics.med-ph↗

arXiv2018

Statistical analysis on XMM-Newton X-ray flares of Mrk 421: distributions of peak flux and flaring time duration

The energy dissipation mechanism in blazar jet is unknown. Blazar's flares could provide insights on this problem. Here we report statistical results of XMM-Newton X-ray flares of Mrk 421. We analyze all public XMM-Newton X-ray observations for Mrk 421, and construct the light curves. Through fitting light curves, we obtain the parameters of flare-profiles, such as peak flux ($F_{\rm p}$) and flaring time duration ($T_{\rm fl}$). It is found that both the distributions of $F_{\rm p}$ and $T_{\rm fl}$ obey a power-law form, with the same index of $α_{\rm F}=α_{\rm T}\approx1$. The statistical properties are consistent with the predictions by a self-organized criticality (SOC) system with energy dissipation in one-dimensional space. This is similar to solar flare, but with different space dimensions of the energy dissipation domain. This suggests that X-ray flaers of Mrk 421 are possibly driven by a magnetic reconnection mechanism. Moreover, in the analysis, we find that variability on timescale of $\sim1000\ $s frequently appears. Such rapid variability indicates a magnetic field of $\geq 2.1δ_{\rm D}^{-1/3}$ G ($δ_{\rm D}$ is the Doppler factor) in emission region.

astro-ph.HE↗

arXiv2018

Efficient Training on Very Large Corpora via Gramian Estimation

We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with sampling of random observed and unobserved pairs, with a number of samples that grows quadratically with the corpus size, making it expensive to scale to very large corpora. We propose new efficient methods to train these models without having to sample unobserved pairs. Inspired by matrix factorization, our approach relies on adding a global quadratic penalty to all pairs of examples and expressing this term as the matrix-inner-product of two generalized Gramians. We show that the gradient of this term can be efficiently computed by maintaining estimates of the Gramians, and develop variance reduction schemes to improve the quality of the estimates. We conduct large-scale experiments that show a significant improvement in training time and generalization quality compared to traditional sampling methods.

stat.ML↗

arXiv2018

Development and validation of Critical Thinking Disposition Inventory for Chinese medical college students (CTDI-M)

PURPOSE: Critical thinkers in medical context must be not only "able" but also "willing" to think critically. To develop and conduct psychometric testing of Critical Thinking Disposition Inventory which measure the critical thinking disposition of Chinese medical college students. METHODS: The study was conducted in two stages: (a) item generation and exploratory factor analysis (EFA) and (b) testing of psychometric properties (construct validity, split-half reliability and Cronbach's alpha, and test-retest reliability. The subjects were composed of 441 undergraduate medical and nursing students from a medical university in China. Test-retest reliability of the instrument was determined at two-week interval. Data was analyzed with SPSS13.0. RESULTS: Preliminary 264 items were obtained using an open-ended questionnaire; from which 61 items were reviewed through half open-ended questionnaire, and finally 18 items were chosen. Eighteen final items were sorted into 3 factors, which were identified as "Open-mindedness ", "Systematicity/analyticity" and "Truth seeking". The cumulative percent of variance was 57.66%. The Cronbach's alpha was 0.924 and the factors' alphas ranged from 0.824-0.862. Correlational analysis indicated moderate to high correlations between the subscales and total scores of the CTDI-CM. Our results indicated that open-mindedness and systematicity/analyticity were higher for medical students than nursing students. CONCLUSIONS: This study conducted in a Chinese medical college student population demonstrated a reliable and valid instrument for clinical thinking disposition, which measured motivation and cognitive components. The effect of enrollment year and major on the profiles of critical thinking dispositions was identified, emphasizing the importance of applying specialized teaching to students of different majors.

physics.ed-ph↗

arXiv2018

Improving Text-to-SQL Evaluation Methodology

To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary for real-world applications. To facilitate evaluation on multiple datasets, we release standardized and improved versions of seven existing datasets and one new text-to-SQL dataset. Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work. Finally, we demonstrate how the common practice of anonymizing variables during evaluation removes an important challenge of the task. Our observations highlight key difficulties, and our methodology enables effective measurement of future development.

cs.CL↗

arXiv2018

In-Orbit Instrument Performance Study and Calibration for POLAR Polarization Measurements

POLAR is a compact space-borne detector designed to perform reliable measurements of the polarization for transient sources like Gamma-Ray Bursts in the energy range 50-500keV. The instrument works based on the Compton Scattering principle with the plastic scintillators as the main detection material along with the multi-anode photomultiplier tube. POLAR has been launched successfully onboard the Chinese space laboratory TG-2 on 15th September, 2016. In order to reliably reconstruct the polarization information a highly detailed understanding of the instrument is required for both data analysis and Monte Carlo studies. For this purpose a full study of the in-orbit performance was performed in order to obtain the instrument calibration parameters such as noise, pedestal, gain nonlinearity of the electronics, threshold, crosstalk and gain, as well as the effect of temperature on the above parameters. Furthermore the relationship between gain and high voltage of the multi-anode photomultiplier tube has been studied and the errors on all measurement values are presented. Finally the typical systematic error on polarization measurements of Gamma-Ray Bursts due to the measurement error of the calibration parameters are estimated using Monte Carlo simulations.

astro-ph.IM↗

arXiv2018

Maximizing Specific Loss Power for Magnetic Hyperthermia by Hard-Soft Mixed Ferrites

We report maximized specific loss power and intrinsic loss power approaching theoretical limits for AC magnetic field heating of nanoparticles. This is achieved by engineering the effective magnetic anisotropy barrier of nanoparticles via alloying of hard and soft ferrites. 22 nm Co0.03Mn0.28Fe2.7O4/SiO2 NPs reached a specific loss power value of 3417 W/gmetal at a field of 33 kA/m and 380 kHz. Biocompatible Zn0.3Fe2.7O4/SiO2 nanoparticles achieved specific loss power of 500 W/gmetal and intrinsic loss power of 26.8 nHm2/kg at field parameters of 7 kA/m and 380 kHz, below the clinical safety limit. Magnetic bone cement achieved heating adequate for bone tumor hyperthermia, incorporating ultralow dosage of just 1 wt% of nanoparticles. In cellular hyperthermia experiments, these nanoparticles demonstrated high cell death rate at low field parameters. Zn0.3Fe2.7O4/SiO2 nanoparticles show cell viabilities above 97% at concentrations up to 0.5 mg/ml within 48 hrs, suggesting toxicity lower than that of magnetite.

cond-mat.mes-hall↗

arXiv2018

Dynamic spin-lattice coupling and nematic fluctuations in NaFeAs

We use inelastic neutron scattering to study acoustic phonons and spin excitations in single crystals of NaFeAs, a parent compound of iron pnictide superconductors. NaFeAs exhibits a tetragonal-to-orthorhombic structural transition at $T_s\approx 58$ K and a collinear antiferromagnetic (AF) order at $T_N\approx 45$ K. While longitudinal and out-of-plane transverse acoustic phonons behave as expected, the in-plane transverse acoustic phonons reveal considerable softening on cooling to $T_s$, and then harden on approaching $T_N$ before saturating below $T_N$. In addition, we find that spin-spin correlation lengths of low-energy magnetic excitations within the FeAs layer and along the $c$-axis increase dramatically below $T_s$, and show weak anomaly across $T_N$. These results suggest that the electronic nematic phase present in the paramagnetic tetragonal phase is closely associated with dynamic spin-lattice coupling, possibly arising from the one-phonon-two-magnon mechanism.

cond-mat.str-el↗

arXiv2018

Choice revision on belief bases

In this contribution we explore choice revision, a sort of belief change in which the new information is represented by a set of sentences and the agent could accept some of the sentences while rejecting the others. We propose a generalized version of expansion operation called partial expansion for developing models of choice revision. By using the partial expansion and two multiple contraction operations previously introduced in the literature, we construct two kinds of choice revision on belief bases. For each of them we propose a set of postulates and prove a partial or full representation theorem. Furthermore, we investigate the operations of making up one's mind derived from these two kinds of choice revision and also give the associated representation theorems.

cs.LO↗

arXiv2018

A Method for Locating High Energy Dissipation Region in Blazar

The production site of gamma-rays in blazar jet is an unresolved problem. We present a method to locate gamma-ray emission region in the framework of one-zone emission model. From measurements of core-shift effect, the relation between the magnetic field strengths ($B'$) in the radio cores of jet and the distances ($R$) of these radio cores from central supermassive black hole (SMBH) can be inferred. Therefore once the magnetic field strength in gamma-ray emission region ($B'_{\rm diss}$) is obtained, one can use the relation of $B'$-$R$ to derive the distance ($R_{\rm diss}$) of gamma-ray emission region from SMBH. Here we evaluate the lower limit of $B'_{\rm diss}$ by using the criteria that the optical variability timescale $t_{\rm var}$ should be longer or equal to the synchrotron radiation cooling timescale of the electrons that emit optical photons. We test the method with the observations of PSK 1510-089 and BL Lacertae, and derive $R_{\rm diss}<0.15δ_{\rm D}^{1/3}(1+A)^{2/3}\ $pc for PSK 1510-089 with $t_{\rm var}\sim$ a few hours, and $R_{\rm diss}<0.003δ_{\rm D}^{1/3}(1+A)^{2/3}\ $pc for BL Lacertae with $t_{\rm var}\sim$ a few minutes. Here $δ_{\rm D} $ is the Doppler factor and $A$ is the Compton dominance (i.e., the ratio of the Compton to the synchrotron peak luminosities).

astro-ph.HE↗

arXiv2018

Choice Revision

Choice revision is a sort of non-prioritized multiple revision, in which the agent partially accepts the new information represented by a set of sentences. We investigate the construction of choice revision based on a new approach to belief change called descriptor revision. We prove that each of two variants of choice revision based on such construction is axiomatically characterized with a set of plausible postulates, assuming that the object language is finite. Furthermore, we introduce an alternative modelling for choice revision, which is based on a type of relation on sets of sentences, named multiple believability relation. We show without assuming a finite language that choice revision constructed from such relations is axiomatically characterized with the same sets of postulates proposed for the choice revision based on descriptor revision, whenever the relations satisfy certain rationality conditions.

math.LO↗

arXiv2018

Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching

Convolutional neural networks(CNN) have been shown to perform better than the conventional stereo algorithms for stereo estimation. Numerous efforts focus on the pixel-wise matching cost computation, which is the important building block for many start-of-the-art algorithms. However, those architectures are limited to small and single scale receptive fields and use traditional methods for cost aggregation or even ignore cost aggregation. Differently we take them both into consideration. Firstly, we propose a new multi-scale matching cost computation sub-network, in which two different sizes of receptive fields are implemented parallelly. In this way, the network can make the best use of both variants and balance the trade-off between the increase of receptive field and the loss of detail. Furthermore, we show that our multi-dimension aggregation sub-network which containing 2D convolution and 3D convolution operations can provide rich context and semantic information for estimating an accurate initial disparity. Finally, experiments on challenging stereo benchmark KITTI demonstrate that the proposed method can achieve competitive results even without any additional post-processing.

cs.CV↗