arXiv subjects

Lei Hou

Publications and source records attributed to Lei Hou.

159 records · Page 9Linked to original sources

arXiv2020

Time-Frequency Transfer through a 70 dB Free Space Channel: Towards Satellite-Ground Time Dissemination

Time and frequency transfer lies at the heart of the field of metrology. Compared to current microwave dissemination such as GPS, optical domain dissemination can provide more than one order of magnitude in terms of higher accuracy, which allows for many applications such as the redefinition of the second, tests of general relativity and fundamental quantum physics, precision navigation and quantum communication. Although optical frequency transfer has been demonstrated over thousand kilometers fiber lines, intercontinental time comparison and synchronization still requires satellite free space optical time and frequency transfer. Quite a few pioneering free space optical time and frequency experiments have been implemented at the distance of tens kilometers at ground level. However, there exists no detailed analysis or ground test to prove the feasibility of satellite-based optical time-frequency transfer. Here, we analyze the possibility of this system and then provide the first-step ground test with high channel loss. We demonstrate the optical frequency transfer with an instability of $10^{-18}$ level in 8,000 seconds across a 16-km free space channel with a loss of up to 70~dB, which is comparable with the loss of a satellite-ground link at medium earth orbit (MEO) and geostationary earth orbit (GEO).

physics.optics↗

arXiv2019

Course Concept Expansion in MOOCs with External Knowledge and Interactive Game

As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drifts and lack of knowledge guidance, existing methods can not effectively expand course concepts in complex MOOC environments. In this paper, we first build a novel boundary during searching for new concepts via external knowledge base and then utilize heterogeneous features to verify the high-quality results. In addition, to involve human efforts in our model, we design an interactive optimization mechanism based on a game. Our experiments on the four datasets from Coursera and XuetangX show that the proposed method achieves significant improvements(+0.19 by MAP) over existing methods. The source code and datasets have been published.

cs.CL↗

arXiv2019

Memories in the Photoluminescence Intermittency of Single Cesium Lead Bromide Nanocrystals

Single cesium lead bromide (CsPbBr3) nanocrystals show strong photoluminescence blinking, with on- and off- dwelling times following power-law distributions. We investigate the memory effect in the photoluminescence blinking of single CsPbBr3 nanocrystals and find positive correlations for successive on-times and successive off-times. This memory effect is not sensitive to the nature of the surface capping ligand and the embedding polymer. These observations suggest that photoluminescence intermittency and its memory are mainly controlled by intrinsic traps in the nanocrystals. These findings will help optimizing light-emitting devices based on inorganic perovskite nanocrystals.

physics.app-ph↗

arXiv2019

Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations

Multi-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough training triples, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms the current state-of-the-art methods in few-shot scenarios. Our code and datasets can be obtained from https://github.com/ THU-KEG/MetaKGR.

cs.AI↗

arXiv2018

Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision

Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and cross-lingual attention to further reduce noises. We conducted a series of experiments on three tasks: word translation, entity relatedness, and cross-lingual entity linking. The results, both qualitatively and quantitatively, demonstrate the significance of our method.

cs.CL↗

arXiv2018

Neural Collective Entity Linking

Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text. We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias. In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability. We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.

cs.CL↗

arXiv2018

Learning to Embed Sentences Using Attentive Recursive Trees

Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks to embed sentences with task-specific structures. However, existing models have no explicit mechanism to emphasize task-informative words in the tree structure. To this end, we propose an Attentive Recursive Tree model (AR-Tree), where the words are dynamically located according to their importance in the task. Specifically, we construct the latent tree for a sentence in a proposed important-first strategy, and place more attentive words nearer to the root; thus, AR-Tree can inherently emphasize important words during the bottom-up composition of the sentence embedding. We propose an end-to-end reinforced training strategy for AR-Tree, which is demonstrated to consistently outperform, or be at least comparable to, the state-of-the-art sentence embedding methods on three sentence understanding tasks.

cs.CL↗

arXiv2018

Differentiating Concepts and Instances for Knowledge Graph Embedding

Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e., instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https:// github.com/davidlvxin/TransC.

cs.AI↗

arXiv2018

DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization

We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-based deep neural network, to represent the salience degree of the summary for yielding the document. We devise a contrastive training strategy to learn the salience estimation network, and then use the learned salience score as a guide and iteratively extract the most salient sentences from the document as our generated summary. In experiments, our model not only achieves state-of-the-art ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1 F-1 score of 39.41 on CNN/Daily Mail test set with merely $1 / 100$ training set, demonstrating a tremendous data efficiency.

cs.CL↗

arXiv2016

The environmental dependence of the stellar mass fundamental plane of early-type galaxies

Aims. We investigate the environmental dependence of the stellar mass fundamental plane (FP$_*$) using the early-type galaxy sample from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Methods. The FP$_*$ is calculated by replacing the luminosity in the fundamental plane (FP) with stellar mass. Based on the SDSS group catalog, we characterize the galaxy environment according to the mass of the host dark matter halo and the position in the halo. In halos with the same mass bin, the color distributions of central and satellite galaxies are different. Therefore, we calculate FP$_*$ coefficients of galaxies in different environments and compare them with those of the FP to study the contribution of the stellar population. Results. We find that coefficient $a$ of the FP$_*$ is systematically larger than that of the FP, but coefficient $b$ of the FP$_*$ is similar to the FP. Moreover, the environmental dependence of the FP$_*$ is similar to that of the FP. For central galaxies, FP$_*$ coefficients are significantly dependent on the halo mass. For satellite galaxies, the correlation between FP$_*$ coefficients and the halo mass is weak. Conclusions. We conclude that the tilt of the FP is not primarily driven by the stellar population.

astro-ph.GA↗

arXiv2015

Stability of similarity measurements for bipartite networks

Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-$n$-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on.

physics.soc-ph↗

arXiv2015

Explosive Formation and Dynamics of Vapor Nanobubbles around a Continuously Heated Gold Nanosphere

We form sub-micrometer-sized vapor bubbles around a single laser heating gold nanoparticle in a liquid and monitor them through optical scattering of a probe laser. The fast, inertia-governed expansion is followed by a slower contraction and disappearance after some tens of nanoseconds. In a narrow range of illumination powers, bubble time traces show a clear echo signature. We attribute it to sound waves released upon the initial explosion and reflected by flat interfaces, hundreds of microns away from the particle. Echoes can trigger new explosions. A steady state of nanobubble with a vapor shell surrounding the heated nanoparticle can be reached by a proper time profile of the heating intensity. Stable nanobubbles could have original applications for light modulation and for enhanced optical-acoustic coupling in photoacoustic microscopy.

cond-mat.mes-hall↗

arXiv2014

The Fundamental Plane Relation of Early-Type Galaxies: Environmental Dependence

Using a sample of 70,793 early-type galaxies from SDSS DR7, we study the environmental dependence of the fundamental plane relation. With the help of the galaxy group catalogue based on SDSS DR7, we calculate the fundamental planes in different dark matter halo mass bins for central and satellite galaxies respectively. We find the environmental dependence of the fundamental plane coefficients are similar in $g$, $r$, $i$ and $z$ bands. The environmental dependence for central and satellite galaxies is significantly different. While the fundamental plane coefficients of centrals vary systematically with the halo mass, those of satellites are similar in different halo mass bins. The discrepancy between centrals and satellites are significant in small halos, but negligible in the largest halo mass bins. These results remain the same when we only keep red galaxies, or galaxies with $b/a>0.6$, or galaxies in a specific radius range in the sample. After the correction of the sky background, results are still similar. We suggest that the different environmental effects of the halo mass on centrals and satellites may arise from the different quenching processes of them.

astro-ph.GA↗

arXiv2014

Supercontinuum generation and carrier envelope offset frequency measurement in a tapered single-mode fiber

We report supercontinuum generation by launching femtosecond Yb fiber laser pulses into a tapered single-mode fiber of 3 um core diameter. A spectrum of more than one octave, from 550 to 1400 nm, has been obtained with an output power of 1.3 W at a repetition rate of 250 MHz, corresponding to a coupling efficiency of up to 60%. By using a typical f-2f interferometer, the carrier envelope offset frequency was measured and found to have a signal-to-noise ratio of nearly 30 dB.

physics.optics↗

arXiv2014

Memory effect of the online user preference

The mechanism of the online user preference evolution is of great significance for understanding the online user behaviors and improving the quality of online services. Since users are allowed to rate on objects in many online systems, ratings can well reflect the users' preference. With two benchmark datasets from online systems, we uncover the memory effect in users' selecting behavior which is the sequence of qualities of selected objects and the rating behavior which is the sequence of ratings delivered by each user. Furthermore, the memory duration is presented to describe the length of a memory, which exhibits the power-law distribution, i.e., the probability of the occurring of long-duration memory is much higher than that of the random case which follows the exponential distribution. We present a preference model in which a Markovian process is utilized to describe the users' selecting behavior, and the rating behavior depends on the selecting behavior. With only one parameter for each of the user's selecting and rating behavior, the preference model could regenerate any duration distribution ranging from the power-law form (strong memory) to the exponential form (weak memory).

physics.soc-ph↗