arXiv subjects

Yu Liu

Publications and source records attributed to Yu Liu.

At least 955 records · Page 53Linked to original sources

arXiv2020

An empirical "high-confidence" candidate zone for $Fermi$ BL Lacertae objects

In the third catalog of active galactic nuclei detected by the $Fermi$ Large Area Telescope Clean (3LAC) sample, there are 402 blazars candidates of uncertain type (BCU). The proposed analysis will help to evaluate the potential optical classification flat spectrum radio quasars (FSRQs) versus BL Lacertae (BL Lacs) objects of BCUs, which can help to understand which is the most elusive class of blazar hidden in the Fermi sample. By studying the 3LAC sample, we found some critical values of $γ$-ray photon spectral index ($Γ_{\rm ph}$), variability index (VI) and radio flux (${\rm F_R}$) of the sources separate known FSRQs and BL Lac objects. We further utilize those values to defined an empirical "high-confidence" candidate zone that can be used to classify the BCUs. Within such a zone ($Γ_{\rm ph}<2.187$, log${\rm F_R}<2.258$ and ${ \rm logVI <1.702}$), we found that 120 BCUs can be classified BL Lac candidates with a higher degree of confidence (with a misjudged rate $<1\%$). Our results suggest that an empirical "high confidence" diagnosis is possible to distinguish the BL Lacs from the Fermi observations based on only on the direct observational data of $Γ_{\rm ph}$, VI and ${\rm F_R}$.

astro-ph.HE↗

arXiv2020

Transparent Gatable Superconducting Shadow Junctions

Gate tunable junctions are key elements in quantum devices based on hybrid semiconductor-superconductor materials. They serve multiple purposes ranging from tunnel spectroscopy probes to voltage-controlled qubit operations in gatemon and topological qubits. Common to all is that junction transparency plays a critical role. In this study, we grow single crystalline InAs, InSb and $\mathrm{InAs_{1-x}Sb_x}$ nanowires with epitaxial superconductors and in-situ shadowed junctions in a single-step molecular beam epitaxy process. We investigate correlations between fabrication parameters, junction morphologies, and electronic transport properties of the junctions and show that the examined in-situ shadowed junctions are of significantly higher quality than the etched junctions. By varying the edge sharpness of the shadow junctions we show that the sharpest edges yield the highest junction transparency for all three examined semiconductors. Further, critical supercurrent measurements reveal an extraordinarily high $I_\mathrm{C} R_\mathrm{N}$, close to the KO$-$2 limit. This study demonstrates a promising engineering path towards reliable gate-tunable superconducting qubits.

cond-mat.mtrl-sci↗

arXiv2020

Quantifying urban areas with multi-source data based on percolation theory

Quantifying urban areas is crucial for addressing associated urban issues such as environmental and sustainable problems. Remote sensing data, especially the nighttime light images, have been widely used to delineate urbanized areas across the world. Meanwhile, some emerging urban data, such as volunteered geographical information (e.g., OpenStreetMap) and social sensing data (e.g., mobile phone and social media), have also shown great potential in revealing urban boundaries and dynamics. However, consistent and robust methods to quantify urban areas from these multi-source data have remained elusive. Here, we propose a percolation-based method to extract urban areas from these multi-source urban data. We derive the optimal urban/non-urban threshold by considering the critical nature of urban systems with the support of the percolation theory. Furthermore, we apply the method with three open-source datasets - population, road, and nighttime light - to 28 countries. We show that the proposed method captures the similar urban characteristics in terms of urban areas from multi-source data, and Zipf's law holds well in most countries. The accuracy of the derived urban areas by different datasets has been validated with the Landsat-based reference data in 10 cities, and the accuracy can be further improved through data fusion ($κ=0.69-0.85$, mean $κ=0.78$). Our study not only provides an efficient method to quantify urban areas with open-source data, but also deepens the understanding of urban systems and sheds some light on multi-source data fusion in geographical fields.

physics.soc-ph↗

arXiv2020

Steering ultracold reactions through long-lived transient intermediates

Controlling the pathways and outcomes of reactions is a broadly pursued goal in chemistry. In gas phase reactions, this is typically achieved by manipulating the properties of the reactants, including their translational energy, orientation, and internal quantum state. In contrast, here we influence the pathway of a reaction via its intermediate complex, which is generally too short-lived to be affected by external processes. In particular, the ultracold preparation of potassium-rubidium (KRb) reactants leads to a long-lived intermediate complex (K$_2$Rb$_2^*$), which allows us to steer the reaction away from its nominal ground-state pathway onto a newly identified excited-state pathway using a laser source at 1064 nm, a wavelength commonly used to confine ultracold molecules. Furthermore, by monitoring the change in the complex population after the sudden removal of the excitation light, we directly measure the lifetime of the complex to be $360 \pm 30$ ns, in agreement with our calculations based on the Rice-Ramsperger-Kassel-Marcus (RRKM) statistical theory. Our results shed light on the origin of the two-body loss widely observed in ultracold molecule experiments. Additionally, the long complex lifetime, coupled with the observed photo-excitation pathway, opens up the possibility to spectroscopically probe the structure of the complex with high resolution, thus elucidating the reaction dynamics.

physics.atom-ph↗

arXiv2020

3D Gated Recurrent Fusion for Semantic Scene Completion

This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion. RGB images contain texture details of the object(s) which are vital for semantic scene understanding. Meanwhile, depth images capture geometric clues of high relevance for shape completion. Using both RGB and depth images can further boost the accuracy of SSC over employing one modality in isolation. We propose a 3D gated recurrent fusion network (GRFNet), which learns to adaptively select and fuse the relevant information from depth and RGB by making use of the gate and memory modules. Based on the single-stage fusion, we further propose a multi-stage fusion strategy, which could model the correlations among different stages within the network. Extensive experiments on two benchmark datasets demonstrate the superior performance and the effectiveness of the proposed GRFNet for data fusion in SSC. Code will be made available.

cs.CV↗

arXiv2020

Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning

Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years. Being different from the RGB datasets, different HSI datasets are generally captured by various remote sensors and have different spectral configurations. Moreover, each HSI dataset only contains very limited training samples and thus it is prone to overfitting when using deep CNNs. In this paper, we first deliver a 3D asymmetric inception network, AINet, to overcome the overfitting problem. With the emphasis on spectral signatures over spatial contexts of HSI data, AINet can convey and classify the features effectively. In addition, the proposed data fusion transfer learning strategy is beneficial in boosting the classification performance. Extensive experiments show that the proposed approach beat all of the state-of-art methods on several HSI benchmarks, including Pavia University, Indian Pines and Kennedy Space Center(KSC). Code can be found at: https://github.com/UniLauX/AINet.

cs.CV↗

arXiv2020

Long-Range Gesture Recognition Using Millimeter Wave Radar

Millimeter wave (mmWave) based gesture recognition technology provides a good human computer interaction (HCI) experience. Prior works focus on the close-range gesture recognition, but fall short in range extension, i.e., they are unable to recognize gestures more than one meter away from considerable noise motions. In this paper, we design a long-range gesture recognition model which utilizes a novel data processing method and a customized artificial Convolutional Neural Network (CNN). Firstly, we break down gestures into multiple reflection points and extract their spatial-temporal features which depict gesture details. Secondly, we design a CNN to learn changing patterns of extracted features respectively and output the recognition result. We thoroughly evaluate our proposed system by implementing on a commodity mmWave radar. Besides, we also provide more extensive assessments to demonstrate that the proposed system is practical in several real-world scenarios.

cs.HC↗

arXiv2020

Site testing campaign for the Large Optical/infrared Telescope of China: Overview

The Large Optical/infrared Telescope (LOT) is a ground-based 12m diameter optical/infrared telescope which is proposed to be built in the western part of China in the next decade. Based on satellite remote sensing data, along with geographical, logistical and political considerations, three candidate sites were chosen for ground-based astronomical performance monitoring. These sites include: Ali in Tibet, Daocheng in Sichuan, and Muztagh Ata in Xinjiang. Up until now, all three sites have continuously collected data for two years. In this paper, we will introduce this site testing campaign, and present its monitoring results obtained during the period between March 2017 and March 2019.

astro-ph.IM↗

arXiv2020

Depth Based Semantic Scene Completion with Position Importance Aware Loss

Semantic Scene Completion (SSC) refers to the task of inferring the 3D semantic segmentation of a scene while simultaneously completing the 3D shapes. We propose PALNet, a novel hybrid network for SSC based on single depth. PALNet utilizes a two-stream network to extract both 2D and 3D features from multi-stages using fine-grained depth information to efficiently captures the context, as well as the geometric cues of the scene. Current methods for SSC treat all parts of the scene equally causing unnecessary attention to the interior of objects. To address this problem, we propose Position Aware Loss(PA-Loss) which is position importance aware while training the network. Specifically, PA-Loss considers Local Geometric Anisotropy to determine the importance of different positions within the scene. It is beneficial for recovering key details like the boundaries of objects and the corners of the scene. Comprehensive experiments on two benchmark datasets demonstrate the effectiveness of the proposed method and its superior performance. Models and Video demo can be found at: https://github.com/UniLauX/PALNet.

cs.CV↗

arXiv2020

Temporal Interlacing Network

For a long time, the vision community tries to learn the spatio-temporal representation by combining convolutional neural network together with various temporal models, such as the families of Markov chain, optical flow, RNN and temporal convolution. However, these pipelines consume enormous computing resources due to the alternately learning process for spatial and temporal information. One natural question is whether we can embed the temporal information into the spatial one so the information in the two domains can be jointly learned once-only. In this work, we answer this question by presenting a simple yet powerful operator -- temporal interlacing network (TIN). Instead of learning the temporal features, TIN fuses the two kinds of information by interlacing spatial representations from the past to the future, and vice versa. A differentiable interlacing target can be learned to control the interlacing process. In this way, a heavy temporal model is replaced by a simple interlacing operator. We theoretically prove that with a learnable interlacing target, TIN performs equivalently to the regularized temporal convolution network (r-TCN), but gains 4% more accuracy with 6x less latency on 6 challenging benchmarks. These results push the state-of-the-art performances of video understanding by a considerable margin. Not surprising, the ensemble model of the proposed TIN won the $1^{st}$ place in the ICCV19 - Multi Moments in Time challenge. Code is made available to facilitate further research at https://github.com/deepcs233/TIN

cs.CV↗

arXiv2019

Probing ultracold chemistry using ion spectrometry

Rapid progress in atomic, molecular, and optical (AMO) physics techniques enabled the creation of ultracold samples of molecular species and opened opportunities to explore chemistry in the ultralow temperature regime. In particular, both the external and internal quantum degrees of freedom of the reactant atoms and molecules are controlled, allowing studies that explored the role of the long range potential in ultracold reactions. The kinetics of these reactions have typically been determined using the loss of reactants as proxies. To extend such studies into the short-range, we developed an experimental apparatus that combines the production of quantum-state-selected ultracold KRb molecules with ion mass and kinetic energy spectrometry, and directly observed KRb + KRb reaction intermediates and products [Science, 2019, 366, 1111]. Here, we present the apparatus in detail. For future studies that aim for detecting the quantum states of the reaction products, we demonstrate a photodissociation based scheme to calibrate the ion kinetic energy spectrometer at low energies.

physics.chem-ph↗

arXiv2019

Direct observation of bimolecular reactions of ultracold KRb molecules

Femtochemistry techniques have been instrumental in accessing the short time scales necessary to probe transient intermediates in chemical reactions. Here we take the contrasting approach of prolonging the lifetime of an intermediate by preparing reactant molecules in their lowest ro-vibronic quantum state at ultralow temperatures, thereby drastically reducing the number of exit channels accessible upon their mutual collision. Using ionization spectroscopy and velocity-map imaging of a trapped gas of potassium-rubidium molecules at a temperature of 500~nK, we directly observe reactants, intermediates, and products of the reaction $^{40}$K$^{87}$Rb + $^{40}$K$^{87}$Rb $\rightarrow$ K$_2$Rb$^*_2$ $\rightarrow$ K$_2$ + Rb$_2$. Beyond observation of a long-lived energy-rich intermediate complex, this technique opens the door to further studies of quantum-state resolved reaction dynamics in the ultracold regime.

physics.atom-ph↗

arXiv2019

Optimization of heterogeneous ternary Li3PO4-Li3BO3-Li2SO4 mixture for Li-ion conductivity by machine learning

Mixing heterogeneous Li-ion conductive materials is one of potential ways to enhance the Li-ion conductivity more than that of the parent materials. However, the development of the mixtures had not exhibited significant progress because it is a formidable task to cover the vast possible composition of the parent materials using traditional ways. Here, we introduce a fashion based on machine learning to optimize the composition ratio of ternary Li3PO4-Li3BO3-Li2SO4 mixture for its Li-ion conductivity. According to our results, the optimum composition of the ternary mixture system is 25:14:61 (Li3PO4: Li3BO3: Li2SO4 in mol%), whose Li-ion conductivity is measured as 4.9 x 10E-4 S/cm at 300 °C. Our X-ray structure analysis indicates that Li-ion conductivity in the mixing systems is enhanced by virtue of the coexistence of two or more phases. Although the mechanism enhancing Li-ion conductivity is not simple, our results demonstrate the effectiveness of machine learning for the development of materials.

cond-mat.mtrl-sci↗

arXiv2019

Critical behavior and magnetocaloric effect in VI$_3$

Layered van der Waals ferromagnets are promising candidates for designing new spintronic devices. Here we investigated the critical properties and magnetocaloric effect connected with ferromagnetic transition in layered van der Waals VI$_3$ single crystals. The critical exponents $β= 0.244(5)$ with a critical temperature $T_c = 50.10(2)$ K and $γ= 1.028(12)$ with $T_c = 49.97(5)$ K are obtained from the modified Arrott plot, whereas $δ= 5.24(2)$ is obtained from a critical isotherm analysis at $T_c = 50$ K. The magnetic entropy change $-ΔS_M(T,H)$ features a maximum at $T_c$, i.e., $-ΔS_M^{max} \sim$ 2.64 (2.27) J kg$^{-1}$ K$^{-1}$ with out-of-plane (in-plane) field change of 5 T. This is consistent with $-ΔS_M^{max}$ $\sim$ 2.80 J kg$^{-1}$ K$^{-1}$ deduced from heat capacity and the corresponding adiabatic temperature change $ΔT_{ad}$ $\sim$ 0.96 K with out-of-plane field change of 5 T. The critical analysis suggests that the ferromagnetic phase transition in VI$_3$ is situated close to a three- to two-dimensional critical point. The rescaled $ΔS_M(T,H)$ curves collapse onto a universal curve, confirming a second-order type of the magnetic transition and reliability of the obtained critical exponents.

cond-mat.mtrl-sci↗

arXiv2019

Magnetic anisotropy and entropy change in trigonal Cr$_{0.62}$Te

We present a comprehensive investigation on anisotropic magnetic and magnetocaloric properties of the quasi-two-dimensional weak itinerant ferromagnet trigonal Cr$_{0.62}$Te single crystals. Magnetic-anisotropy-induced satellite transition $T^*$ is observed at low fields applied parallel to the $ab$ plane below $T_c$. The $T^*$ is featured by an anomalous magnetization downturn, similar to that in structurally related CrI$_3$, and shows a monotonous shift towards lower temperature with increasing field. Magnetocrystalline anisotropy is also reflected in magnetic entropy change $ΔS_M(T,H)$ and relative cooling power RCP. Given the high $T_c$, Cr$_{0.62}$Te crystals are materials of interest for nanofabrication in basic science and applied technology.

cond-mat.mtrl-sci↗

arXiv2019

Local electric field induced spin photocurrent in ReS2

Circular polarized photocurrent is observed near the electrodes on a few-layer ReS2sample at room temperature. For both electrodes, the spatial distribution of the circular polarized photocurrent shows a feature of two wings, with one positive and the other negative. We suggest that this phenomenon arises from the inverse spin Hall effect due to local electric field near the electrode. Bias voltage that modulates this field further controls the sign and magnitude of the inverse spin Hall effect photocurrent. Our research shows that electric field near electrodes has a significant impact on spin transmission operation, hence it could be taken into account for manufacturing spintronic devices in future.

physics.app-ph↗

arXiv2019

Topological magnetic-spin textures in two-dimensional van der Waals Cr2Ge2Te6

Two-dimensional (2D) van der Waals (vdW) materials show a range of profound physical properties that can be tailored through their incorporation in heterostructures and manipulated with external forces. The recent discovery of long-range ferromagnetic order down to atomic layers provides an additional degree of freedom in engineering 2D materials and their heterostructure devices for spintronics, valleytronics and magnetic tunnel junction switches. Here, using direct imaging by cryo-Lorentz transmission electron microscopy we show that topologically nontrivial magnetic-spin states, skyrmionic bubbles, can be realized in exfoliated insulating 2D vdW Cr2Ge2Te6. Due to the competition between dipolar interactions and uniaxial magnetic anisotropy, hexagonally-packed nanoscale bubble lattices emerge by field cooling with magnetic field applied along the out-of-plane direction. Despite a range of topological spin textures in stripe domains arising due to pair formation and annihilation of Bloch lines, bubble lattices with single chirality are prevalent. Our observation of topologically-nontrivial homochiral skyrmionic bubbles in exfoliated vdW materials provides a new avenue for novel quantum states in atomically-thin insulators for magneto-electronic and quantum devices.

cond-mat.mtrl-sci↗

arXiv2019

A game method for improving the interpretability of convolution neural network

Real artificial intelligence always has been focused on by many machine learning researchers, especially in the area of deep learning. However deep neural network is hard to be understood and explained, and sometimes, even metaphysics. The reason is, we believe that: the network is essentially a perceptual model. Therefore, we believe that in order to complete complex intelligent activities from simple perception, it is necessary to con-struct another interpretable logical network to form accurate and reasonable responses and explanations to external things. Researchers like Bolei Zhou and Quanshi Zhang have found many explanatory rules for deep feature extraction aimed at the feature extraction stage of convolution neural network. However, although researchers like Marco Gori have also made great efforts to improve the interpretability of the fully connected layers of the network, the problem is also very difficult. This paper firstly analyzes its reason. Then a method of constructing logical network based on the fully connected layers and extracting logical relation between input and output of the layers is proposed. The game process between perceptual learning and logical abstract cognitive learning is implemented to improve the interpretable performance of deep learning process and deep learning model. The benefits of our approach are illustrated on benchmark data sets and in real-world experiments.

cs.LG↗