arXiv subjects

Yu Liu

Publications and source records attributed to Yu Liu.

At least 991 records · Page 55Linked to original sources

arXiv2019

Coherent Epitaxial Semiconductor-Ferromagnetic Insulator InAs/EuS Interfaces: Band Alignment and Magnetic Structure

Hybrid semiconductor-ferromagnetic insulator heterostructures are interesting due to their tunable electronic transport, self-sustained stray field and local proximitized magnetic exchange. In this work, we present lattice matched hybrid epitaxy of semiconductor - ferromagnetic insulator InAs/EuS heterostructures and analyze the atomic-scale structure as well as their electronic and magnetic characteristics. The Fermi level at the InAs/EuS interface is found to be close to the InAs conduction band and in the bandgap of EuS, thus preserving the semiconducting properties. Both neutron and X-ray reflectivity measurements show that the ferromagnetic component is mainly localized in the EuS thin film with a suppression of the Eu moment in the EuS layer nearest the InAs. Induced moments in the adjacent InAs layers were not detected although our ab initio calculations indicate a small exchange field in the InAs layer. This work presents a step towards realizing high quality semiconductor - ferromagnetic insulator hybrids, which is a critical requirement for development of various quantum and spintronic applications without external magnetic fields.

cond-mat.mtrl-sci↗

arXiv2019

Stereoscopic Observations of an Erupting Mini-filament Driven Two-Sided-Loop Jet and the Applications for Diagnosing Filament Magnetic field

The ubiquitous solar jets or jet-like activities are generally regarded as an important source of energy and mass input to the upper solar atmosphere and the solar wind. However, questions about their triggering and driving mechanisms are not completely understood. By taking advantage of high temporal and high spatial resolution stereoscopic observations taken by the Solar Dynamic Observatory (SDO) and the Solar Terrestrial Relations Observatory (STEREO), we report an intriguing two-sided-loop jet occurred on 2013 June 02, which was dynamically associated with the eruption of a mini-filament below an overlying large filament, and two distinct reconnection processes are identified during the formation stage. The SDO observations reveals that the two-sided-loop jet showed a concave shape with a projection speed of about 80 - 136. From the other view angle, the STEREO ahead observations clearly showed that the trajectory of the two arms of the two-sided-loop were along the cavity magnetic field lines hosting the large filament. Contrary to the well-accepted theoretical model, the present observation sheds new light on our understanding of the formation mechanism of two-sided-loop jets. Moreover, the eruption of the two-sided-loop jet not only supplied mass to the overlying large filament, but also provided a rare opportunity to diagnose the magnetic structure of the overlying large filament via the method of three-dimensional reconstruction.

astro-ph.SR↗

arXiv2019

Exploiting temporal consistency for real-time video depth estimation

Accuracy of depth estimation from static images has been significantly improved recently, by exploiting hierarchical features from deep convolutional neural networks (CNNs). Compared with static images, vast information exists among video frames and can be exploited to improve the depth estimation performance. In this work, we focus on exploring temporal information from monocular videos for depth estimation. Specifically, we take the advantage of convolutional long short-term memory (CLSTM) and propose a novel spatial-temporal CSLTM (ST-CLSTM) structure. Our ST-CLSTM structure can capture not only the spatial features but also the temporal correlations/consistency among consecutive video frames with negligible increase in computational cost. Additionally, in order to maintain the temporal consistency among the estimated depth frames, we apply the generative adversarial learning scheme and design a temporal consistency loss. The temporal consistency loss is combined with the spatial loss to update the model in an end-to-end fashion. By taking advantage of the temporal information, we build a video depth estimation framework that runs in real-time and generates visually pleasant results. Moreover, our approach is flexible and can be generalized to most existing depth estimation frameworks. Code is available at: https://tinyurl.com/STCLSTM

cs.CV↗

arXiv2019

Valence transition in topological Kondo insulator

We investigate the valence transition in three-dimensional topological Kondo insulator through slave-boson analysis of periodic Anderson model. By including the effect of intra-atomic Coulomb correlation $U_{fc}$ between conduction and local electrons, we find a first-order valence transition from Kondo region to mixed valence upon ascending of local level above a critical $U_{fc}$, and this valence transition usually occurs very close to or simultaneously with a topological transition. Near the parameter region of zero-temperature valence transition, rise of temperature can generate a thermal valence transition from mixed valence to Kondo region, accompanied by a first-order topological transition. Remarkably, above a critical $U_{fc}$ which is considerable smaller than that generating paramagnetic valence transition, the original continuous antiferromagnetic transition is shifted to first order one, at which a discontinuous valence shift takes place. Upon increased $U_{fc}$, the paramagnetic valence transition approaches then converges with the first-order antiferromagnetic transition, leaving an significant valence shift on the magnetic boundary. The continuous antiferromagnetic transition, first-order antiferromagnetic transition, paramagnetic valence transition and topological transitions are all summarized in a global phase diagram. Our proposed exotic transition processes can help to understand the thermal valence variation as well as the valence shift around the pressure-induced magnetic transition in topological Kondo insulator candidates and in other heavy-fermion systems.

cond-mat.str-el↗

arXiv2019

Scalable Place Recognition Under Appearance Change for Autonomous Driving

A major challenge in place recognition for autonomous driving is to be robust against appearance changes due to short-term (e.g., weather, lighting) and long-term (seasons, vegetation growth, etc.) environmental variations. A promising solution is to continuously accumulate images to maintain an adequate sample of the conditions and incorporate new changes into the place recognition decision. However, this demands a place recognition technique that is scalable on an ever growing dataset. To this end, we propose a novel place recognition technique that can be efficiently retrained and compressed, such that the recognition of new queries can exploit all available data (including recent changes) without suffering from visible growth in computational cost. Underpinning our method is a novel temporal image matching technique based on Hidden Markov Models. Our experiments show that, compared to state-of-the-art techniques, our method has much greater potential for large-scale place recognition for autonomous driving.

cs.CV↗

arXiv2019

Relative rigid objects in extriangulated categories

In this paper, we study a close relationship between relative cluster tilting theory in extriangulated categories and tau-tilting theory in module categories. Our main results show that relative rigid objects are in bijection with $τ$-rigid pairs, and also relative maximal rigid objects with support tau-tilting pairs under some assumptions. These results generalize their work by Adachi-Iyama-Reiten, Yang-Zhu and Fu-Geng-Liu. Finally, we introduce mutation of relative maximal rigid objects and show that any basic relative almost maximal rigid object has exactly two non-isomorphic indecomposable complements.

math.RT↗

arXiv2019

Use of OWL and Semantic Web Technologies at Pinterest

Pinterest is a popular Web application that has over 250 million active users. It is a visual discovery engine for finding ideas for recipes, fashion, weddings, home decoration, and much more. In the last year, the company adopted Semantic Web technologies to create a knowledge graph that aims to represent the vast amount of content and users on Pinterest, to help both content recommendation and ads targeting. In this paper, we present the engineering of an OWL ontology---the Pinterest Taxonomy---that forms the core of Pinterest's knowledge graph, the Pinterest Taste Graph. We describe modeling choices and enhancements to WebProtégé that we used for the creation of the ontology. In two months, eight Pinterest engineers, without prior experience of OWL and WebProtégé, revamped an existing taxonomy of noisy terms into an OWL ontology. We share our experience and present the key aspects of our work that we believe will be useful for others working in this area.

cs.CL↗

arXiv2019

Super Fast Beam and Channel Tracking in 2D Phased Antenna Arrays

Millimeter wave (mmWave) is an attractive candidate for high-speed mobile communications in the future. However, due to the propagation characteristics of mmWave, beam and and and and alignment becomes a key challenge for serving users with fast moving speeds. In this paper, we develop a joint beam and channel tracking algorithm that can track beams from the horizontal and vertical directions by using two-dimensional (2D) phased antenna arrays. A general sequence of optimal trial beamforming parameters is obtained to achieve the minimum Cramer-Rao lower bound (CRLB) of joint beam and channel tracking asymptotically as antenna number grows to infinity. This sequence is proved to be asymptotically optimal in different conditions, e.g., channel coefficients, path directions, and antenna array sizes. We prove that the proposed algorithm converges to the minimum CRLB in static scenarios. Simulation results show that our algorithm outperforms several existing algorithms in tracking accuracy and speed band.

cs.IT↗

arXiv2019

Lattice dynamics and phase transitions in Fe$_{3-x}$GeTe$_2$

We present Raman spectroscopy measurements of the van der Waals bonded ferromagnet Fe$_{3-x}$GeTe$_2$, together with lattice dynamics. Four out of eight Raman active modes are observed and assigned, in agreement with numerical calculations. The energies and line-widths of the observed modes display an unconventional temperature dependence at about 150 K and 220 K, followed by the nonmonotonic evolution of the Raman continuum. Whereas the former can be related to the magnetic phase transition, the origin of the latter anomaly remains an open question.

cond-mat.mtrl-sci↗

arXiv2019

From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation

Many important classification problems, such as object classification, speech recognition, and machine translation, have been tackled by the supervised learning paradigm in the past, where training corpora of parallel input-output pairs are required with high cost. To remove the need for the parallel training corpora has practical significance for real-world applications, and it is one of the main goals of unsupervised learning. Recently, encouraging progress in unsupervised learning for solving such classification problems has been made and the nature of the challenges has been clarified. In this article, we review this progress and disseminate a class of promising new methods to facilitate understanding the methods for machine learning researchers. In particular, we emphasize the key information that enables the success of unsupervised learning - the sequential statistics as the distributional prior in the labels. Exploitation of such sequential statistics makes it possible to estimate parameters of classifiers without the need of paired input-output data. In this paper, we first introduce the concept of Caesar Cipher and its decryption, which motivated the construction of the novel loss function for unsupervised learning we use throughout the paper. Then we use a simple but representative binary classification task as an example to derive and describe the unsupervised learning algorithm in a step-by-step, easy-to-understand fashion. We include two cases, one with Bigram language model as the sequential statistics for use in unsupervised parameter estimation, and another with a simpler Unigram language model. For both cases, detailed derivation steps for the learning algorithm are included. Further, a summary table compares computational steps of the two cases in executing the unsupervised learning algorithm for learning binary classifiers.

cs.LG↗

arXiv2019

Modulating quantum Fisher information of qubit in dissipative cavity by coupling strength

By using the non-Markovian master equation, we investigate the effect of the cavity and the environment on the quantum Fisher information (QFI) of an atom qubit system in a dissipation cavity. We obtain the formulae of QFI for two different initial states and analyze the effect of the atom-cavity coupling and the cavity-reservoir coupling on the QFI. The results show that the dynamic behavior of the QFI is obviously dependent on the initial atomic states, the atom-cavity coupling and the cavity-reservoir coupling. The stronger the atom-cavity coupling, the quicker the QFI oscillates and the slower the QFI reduces. Especially, the QFI will tend to a stable value not zero if the atom-cavity coupling is large enough. On the other hand, the smaller the cavity-reservoir coupling, the stronger the non-Markovian effect, the slower the QFI decay. In other words, choosing the best parameter can improve the accuracy of parameter estimation. In addition, the physical explanation of the dynamic behavior of the QFI is given by means of the QFI flow.

quant-ph↗

arXiv2019

PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs

{\it SimRank} is a classic measure of the similarities of nodes in a graph. Given a node $u$ in graph $G =(V, E)$, a {\em single-source SimRank query} returns the SimRank similarities $s(u, v)$ between node $u$ and each node $v \in V$. This type of queries has numerous applications in web search and social networks analysis, such as link prediction, web mining, and spam detection. Existing methods for single-source SimRank queries, however, incur query cost at least linear to the number of nodes $n$, which renders them inapplicable for real-time and interactive analysis. { This paper proposes \prsim, an algorithm that exploits the structure of graphs to efficiently answer single-source SimRank queries. \prsim uses an index of size $O(m)$, where $m$ is the number of edges in the graph, and guarantees a query time that depends on the {\em reverse PageRank} distribution of the input graph. In particular, we prove that \prsim runs in sub-linear time if the degree distribution of the input graph follows the power-law distribution, a property possessed by many real-world graphs. Based on the theoretical analysis, we show that the empirical query time of all existing SimRank algorithms also depends on the reverse PageRank distribution of the graph.} Finally, we present the first experimental study that evaluates the absolute errors of various SimRank algorithms on large graphs, and we show that \prsim outperforms the state of the art in terms of query time, accuracy, index size, and scalability.

cs.DS↗

arXiv2019

Extracting human emotions at different places based on facial expressions and spatial clustering analysis

The emergence of big data enables us to evaluate the various human emotions at places from a statistic perspective by applying affective computing. In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user generated footprints collected in social media websites, online cognitive services are utilized to extract human emotions from facial expressions using the state-of-the-art computer vision techniques. And two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected out from over 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research may offer insights on integrating human emotions to enrich the understanding of sense of place in geography and in place-based GIS.

cs.CV↗

arXiv2019

RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene completion (SSC). SSC is composed of 3D shape completion (SC) and semantic scene labeling while most of the existing methods use depth as the sole input which causes the performance bottleneck. Moreover, the state-of-the-art methods employ 3D CNNs which have cumbersome networks and tremendous parameters. We introduce a light-weight Dimensional Decomposition Residual network (DDR) for 3D dense prediction tasks. The novel factorized convolution layer is effective for reducing the network parameters, and the proposed multi-scale fusion mechanism for depth and color image can improve the completion and segmentation accuracy simultaneously. Our method demonstrates excellent performance on two public datasets. Compared with the latest method SSCNet, we achieve 5.9% gains in SC-IoU and 5.7% gains in SSC-IOU, albeit with only 21% network parameters and 16.6% FLOPs employed compared with that of SSCNet.

cs.CV↗

arXiv2019

Experimental Quantum-enhanced Cryptographic Remote Control

The Internet of Things (IoT), as a cutting-edge integrated cross-technology, promises to informationize people's daily lives, while being threatened by continuous challenges of eavesdropping and tampering. The emerging quantum cryptography, harnessing the random nature of quantum mechanics, may also enable unconditionally secure control network, beyond the applications in secure communications. Here, we present a quantum-enhanced cryptographic remote control scheme that combines quantum randomness and one-time pad algorithm for delivering commands remotely. We experimentally demonstrate this on an unmanned aircraft vehicle (UAV) control system. We precharge quantum random number (QRN) into controller and controlee before launching UAV, instead of distributing QRN like standard quantum communication during flight. We statistically verify the randomness of both quantum keys and the converted ciphertexts to check the security capability. All commands in the air are found to be completely chaotic after encryption, and only matched keys on UAV can decipher those commands precisely. In addition, the controlee does not response to the commands that are not or incorrectly encrypted, showing the immunity against interference and decoy. Our work adds true randomness and quantum enhancement into the realm of secure control algorithm in a straightforward and practical fashion, providing a promoted solution for the security of artificial intelligence and IoT.

cs.CR↗

arXiv2019

Two-dimensional honeycomb borophene oxide: A promising anode material offering super high capacity for Li/Na-ion batteries

Rational design of novel two-dimensional (2D) electrode materials with high capacity is crucial for the further development of Li-ion and Na-ion batteries. Herein, based on first-principles calculations, we systemically investigate Li and Na storage behaviors in the recently discovered 2D topological nodal-loop metal - the honeycomb borophene oxide (h-B2O). We show that h-B2O is an almost ideal anode material. It has good conductivity before and after Li/Na adsorption, fast ion diffusion with diffusion barrier less than 0.5 eV, low open-circuit voltage (less than 1 V), and small lattice change (less than 6.2%) during intercalation. Most remarkably, its theoretical storage capacity is extremely high, reaching up to 2137 mAh/g for Li and 1425 mAh/g for Na. Its Li storage capacity is more than six times higher than graphite (372 mAh/g), and is actually the highest among all 2D materials discovered to date. Our results strongly suggest that 2D h-B2O is an exceedingly promising anode material for both Li- and Na-ion batteries with super high capacity.

cond-mat.mtrl-sci↗

arXiv2019

Talking Face Generation by Adversarially Disentangled Audio-Visual Representation

Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech. This is a challenging task because face appearance variation and semantics of speech are coupled together in the subtle movements of the talking face regions. Existing works either construct specific face appearance model on specific subjects or model the transformation between lip motion and speech. In this work, we integrate both aspects and enable arbitrary-subject talking face generation by learning disentangled audio-visual representation. We find that the talking face sequence is actually a composition of both subject-related information and speech-related information. These two spaces are then explicitly disentangled through a novel associative-and-adversarial training process. This disentangled representation has an advantage where both audio and video can serve as inputs for generation. Extensive experiments show that the proposed approach generates realistic talking face sequences on arbitrary subjects with much clearer lip motion patterns than previous work. We also demonstrate the learned audio-visual representation is extremely useful for the tasks of automatic lip reading and audio-video retrieval.

cs.CV↗

arXiv2019

Gorenstein dimension of abelian categories

Let C be triangulated category and X a cluster tilting subcategory of C. Koenig and Zhu showed that the quotient category C/X is Gorenstein of Gorenstein dimension at most one. The notion of an extriangulated category was introduced by Nakaoka and Palu as a simultaneous generalization of exact categories and triangulated categories. Now let C be extriangulated category with enough projectives and enough injectives, and X a cluster tilting subcategory of C. In this article, we show that under certain conditions the quotient category C/X is Gorenstein of Gorenstein dimension at most one. As an application, this result generalizes work by Koenig and Zhu.

math.RT↗