arXiv subjects

Tao Yu

Publications and source records attributed to Tao Yu.

At least 361 records · Page 20Linked to original sources

arXiv2019

SParC: Cross-Domain Semantic Parsing in Context

We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at https://yale-lily.github.io/sparc.

cs.CL↗

arXiv2019

Test for homogeneity with unordered paired observations

In some applications, an experimental unit is composed of two distinct but related subunits. The response from such a unit is $(X_{1}, X_{2})$ but we observe only $Y_1 = \min\{X_{1},X_{2}\}$ and $Y_2 = \max\{X_{1},X_{2}\}$, i.e., the subunit identities are not observed. We call $(Y_1, Y_2)$ unordered paired observations. Based on unordered paired observations $\{(Y_{1i}, Y_{2i})\}_{i=1}^n$, we are interested in whether the marginal distributions for $X_1$ and $X_2$ are identical. Testing methods are available in the literature under the assumptions that $Var(X_1) = Var(X_2)$ and $Cov(X_1, X_2) = 0$. However, by extensive simulation studies, we observe that when one or both assumptions are violated, these methods have inflated type I errors or much lower powers. In this paper, we study the likelihood ratio test statistics for various scenarios and explore their limiting distributions without these restrictive assumptions. Furthermore, we develop Bartlett correction formulae for these statistics to enhance their precision when the sample size is not large. Simulation studies and real-data examples are used to illustrate the efficacy of the proposed methods.

math.ST↗

arXiv2019

Polarity and spin-orbit coupling induced large interfacial exchange coupling an asymmetric charge transfer in iridate-manganite heterostructure

Charge transfer is of particular importance in manipulating the interface physics in transition-metal oxide heterostructures. In this work, we have fabricated epitaxial bilayers composed of polar 3d LaMnO3 and nonpolar 5d SrIrO3. Systematic magnetic measurements reveal an unexpectedly large exchange bias effect in the bilayer, together with a dramatic enhancement of the coercivity of LaMnO3. Based on first-principles calculations and x-ray absorption spectroscopy measurements, such a strong interfacial magnetic coupling is found closely associated with the polar nature of LaMnO3 and the strong spin-orbit interaction in SrIrO3, which collectively drives an asymmetric interfacial charge transfer and leads to the emergence of an interfacial spin glass state. Our study provides new insight into the charge transfer in transition-metal oxide heterostructures and offers a novel means to tune the interfacial exchange coupling for a variety of device applications.

cond-mat.mes-hall↗

arXiv2019

DeepHuman: 3D Human Reconstruction from a Single Image

We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D human reconstruction from a single RGB image. To reduce the ambiguities associated with the surface geometry reconstruction, even for the reconstruction of invisible areas, we propose and leverage a dense semantic representation generated from SMPL model as an additional input. One key feature of our network is that it fuses different scales of image features into the 3D space through volumetric feature transformation, which helps to recover accurate surface geometry. The visible surface details are further refined through a normal refinement network, which can be concatenated with the volume generation network using our proposed volumetric normal projection layer. We also contribute THuman, a 3D real-world human model dataset containing about 7000 models. The network is trained using training data generated from the dataset. Overall, due to the specific design of our network and the diversity in our dataset, our method enables 3D human model estimation given only a single image and outperforms state-of-the-art approaches.

cs.CV↗

arXiv2019

SimulCap : Single-View Human Performance Capture with Cloth Simulation

This paper proposes a new method for live free-viewpoint human performance capture with dynamic details (e.g., cloth wrinkles) using a single RGBD camera. Our main contributions are: (i) a multi-layer representation of garments and body, and (ii) a physics-based performance capture procedure. We first digitize the performer using multi-layer surface representation, which includes the undressed body surface and separate clothing meshes. For performance capture, we perform skeleton tracking, cloth simulation, and iterative depth fitting sequentially for the incoming frame. By incorporating cloth simulation into the performance capture pipeline, we can simulate plausible cloth dynamics and cloth-body interactions even in the occluded regions, which was not possible in previous capture methods. Moreover, by formulating depth fitting as a physical process, our system produces cloth tracking results consistent with the depth observation while still maintaining physical constraints. Results and evaluations show the effectiveness of our method. Our method also enables new types of applications such as cloth retargeting, free-viewpoint video rendering and animations.

cs.CV↗

arXiv2019

Excitation of unidirectional exchange spin waves by a nanoscale magnetic grating

Magnon spintronics is a prosperous field that promises beyond-CMOS technology based on elementary excitations of the magnetic order that act as information carriers for future computational architectures. Unidirectional propagation of spin waves is key to the realization of magnonic logic devices. However, previous efforts to enhance the Damon-Eshbach-type nonreciprocity did not realize (let alone control) purely unidirectional propagation. Here we experimentally demonstrate excitations of unidirectional exchange spin waves by a nanoscale magnetic grating consisting of Co nanowires fabricated on an ultrathin yttrium iron garnet film. We explain and model the nearly perfect unidirectional excitation by the chirality of the magneto-dipolar interactions between the Kittel mode of the nanowires and the exchange spin waves of the film. Reversal of the magnetic configurations of film and nanowire array from parallel to antiparallel changes the direction of the excited spin waves. Our results raise the prospect of a chiral magnonic logic without the need for fragile surface states.

physics.app-ph↗

arXiv2019

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

We present Spider, a large-scale, complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students. It consists of 10,181 questions and 5,693 unique complex SQL queries on 200 databases with multiple tables, covering 138 different domains. We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets. In this way, the task requires the model to generalize well to both new SQL queries and new database schemas. Spider is distinct from most of the previous semantic parsing tasks because they all use a single database and the exact same programs in the train set and the test set. We experiment with various state-of-the-art models and the best model achieves only 12.4% exact matching accuracy on a database split setting. This shows that Spider presents a strong challenge for future research. Our dataset and task are publicly available at https://yale-lily.github.io/spider

cs.CL↗

arXiv2019

Surface dynamics of rough magnetic films

The chirality of Damon-Eshbach (DE) magnons affects the transport of energy and angular momentum at the surface of magnetic films and spheres. We calculate the surface-disorder-limited dephasing and transport lifetimes of surface modes of sufficiently thick high-quality magnetic films such as yttrium iron garnet. In spite of their chirality surface magnons are not protected, but interact strongly with smooth surface roughness. Nevertheless, for long-range disorder the transport is much less affected by the suppressed back scattering (vertex correction). Moreover, in the presence of roughness ferromagnetic resonance under a \textit{uniform} microwave field can gennerate a considerable amount of surface magnons.

cond-mat.mes-hall↗

arXiv2019

Chiral excitation of spin waves in ferromagnetic films

We theoretically investigate the interlayer dipolar and exchange couplings for an array of metallic magnetic nanowires grown on top of an extended ultrathin yttrium iron garnet film. The calculated interlayer dipolar coupling agrees with observed anticrossings [Chen \emph{et al.}, Phys. Rev. Lett. \textbf{120}, 217202 (2018)], concluding that the interlayer exchange coupling is suppressed by a spacer layer between the nanowires and film. The Kittel mode in the nanowire array couples chirally to spin waves in the film, even though Damon-Eshbach surface modes do not exist. The chirality is suppressed when the interlayer exchange coupling becomes strong.

cond-mat.mes-hall↗

arXiv2018

SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task

Most existing studies in text-to-SQL tasks do not require generating complex SQL queries with multiple clauses or sub-queries, and generalizing to new, unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task. SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL generation path history and table-aware column attention encoders. We evaluate SyntaxSQLNet on the Spider text-to-SQL task, which contains databases with multiple tables and complex SQL queries with multiple SQL clauses and nested queries. We use a database split setting where databases in the test set are unseen during training. Experimental results show that SyntaxSQLNet can handle a significantly greater number of complex SQL examples than prior work, outperforming the previous state-of-the-art model by 7.3% in exact matching accuracy. We also show that SyntaxSQLNet can further improve the performance by an additional 7.5% using a cross-domain augmentation method, resulting in a 14.8% improvement in total. To our knowledge, we are the first to study this complex and cross-domain text-to-SQL task.

cs.CL↗

arXiv2018

Performance Evaluation for LTE-V based Vehicle-to-Vehicle Platooning Communication

With the raising demand for autonomous driving, vehicle-to-vehicle communications becomes a key technology enabler for the future intelligent transportation system. Based on our current knowledge field, there is limited network simulator that can support end-to-end performance evaluation for LTE-V based vehicle-to-vehicle platooning systems. To address this problem, we start with an integrated platform that combines traffic generator and network simulator together, and build the V2V transmission capability according to LTE-V specification. On top of that, we simulate the end-to-end throughput and delay profiles in different layers to compare different configurations of platooning systems. Through numerical experiments, we show that the LTE-V system is unable to support the highest degree of automation under shadowing effects in the vehicle platooning scenarios, which requires ultra-reliable low-latency communication enhancement in 5G networks. Meanwhile, the throughput and delay performance for vehicle platooning changes dramatically in PDCP layers, where we believe further improvements are necessary.

eess.SP↗

arXiv2018

Some Sufficient Conditions on Pancyclic Graphs

A pancyclic graph is a graph that contains cycles of all possible lengths from three up to the number of vertices in the graph. In this paper, we establish some new sufficient conditions for a graph to be pancyclic in terms of the edge number, the spectral radius and the signless Laplacian spectral radius of the graph.

math.CO↗

arXiv2018

Measure-theoretic mean equicontinuity and bounded complexity

Let $(X,\mathcal{B},μ,T)$ be a measure preserving system. We say that a function $f\in L^2(X,μ)$ is $μ$-mean equicontinuous if for any $ε>0$ there is $k\in \mathbb{N}$ and measurable sets ${A_1,A_2,\cdots,A_k}$ with $μ\left(\bigcup\limits_{i=1}^k A_i\right)>1-ε$ such that whenever $x,y\in A_i$ for some $1\leq i\leq k$, one has \[ \limsup_{n\to\infty}\frac{1}{n}\sum_{j=0}^{n-1}|f(T^jx)-f(T^jy)|<ε. \] Measure complexity with respect to $f$ is also introduced. It is shown that $f$ is an almost periodic function if and only if $f$ is $μ$-mean equicontinuous if and only if $μ$ has bounded complexity with respect to $f$. Ferenczi studied measure-theoretic complexity using $α$-names of a partition and the Hamming distance. He proved that if a measure preserving system is ergodic, then the complexity function is bounded if and only if the system has discrete spectrum. We show that this result holds without the assumption of ergodicity.

math.DS↗

arXiv2018

Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images

A variety of deep neural networks have been applied in medical image segmentation and achieve good performance. Unlike natural images, medical images of the same imaging modality are characterized by the same pattern, which indicates that same normal organs or tissues locate at similar positions in the images. Thus, in this paper we try to incorporate the prior knowledge of medical images into the structure of neural networks such that the prior knowledge can be utilized for accurate segmentation. Based on this idea, we propose a novel deep network called knowledge-based fully convolutional network (KFCN) for medical image segmentation. The segmentation function and corresponding error is analyzed. We show the existence of an asymptotically stable region for KFCN which traditional FCN doesn't possess. Experiments validate our knowledge assumption about the incorporation of prior knowledge into the convolution kernels of KFCN and show that KFCN can achieve a reasonable segmentation and a satisfactory accuracy.

cs.CV↗

arXiv2018

TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation

Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data. This requires a system that understands users' questions and converts them to SQL queries automatically. In this paper we present a novel approach, TypeSQL, which views this problem as a slot filling task. Additionally, TypeSQL utilizes type information to better understand rare entities and numbers in natural language questions. We test this idea on the WikiSQL dataset and outperform the prior state-of-the-art by 5.5% in much less time. We also show that accessing the content of databases can significantly improve the performance when users' queries are not well-formed. TypeSQL gets 82.6% accuracy, a 17.5% absolute improvement compared to the previous content-sensitive model.

cs.CL↗

arXiv2018

DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor

We propose DoubleFusion, a new real-time system that combines volumetric dynamic reconstruction with data-driven template fitting to simultaneously reconstruct detailed geometry, non-rigid motion and the inner human body shape from a single depth camera. One of the key contributions of this method is a double layer representation consisting of a complete parametric body shape inside, and a gradually fused outer surface layer. A pre-defined node graph on the body surface parameterizes the non-rigid deformations near the body, and a free-form dynamically changing graph parameterizes the outer surface layer far from the body, which allows more general reconstruction. We further propose a joint motion tracking method based on the double layer representation to enable robust and fast motion tracking performance. Moreover, the inner body shape is optimized online and forced to fit inside the outer surface layer. Overall, our method enables increasingly denoised, detailed and complete surface reconstructions, fast motion tracking performance and plausible inner body shape reconstruction in real-time. In particular, experiments show improved fast motion tracking and loop closure performance on more challenging scenarios.

cs.CV↗

arXiv2018

A Guide of Fingerprint Based Radio Emitter Localization using Multiple Sensors

Location information is essential to varieties of applications. It is one of the most important context to be detected by wireless distributed sensors, which is a key technology in Internet-of-Things. Fingerprint-based methods, which compare location unique fingerprints collected beforehand with the fingerprint measured from the target, have attracted much attention recently in both of academia and industry. They have been successfully used for many location-based applications.From the viewpoint of practical applications, in this paper, four different typical approaches of fingerprint-based radio emitter localization system are introduced with four different representative applications: localization of LTE smart phone used for anti-cheating in exams, indoor localization of Wi-Fi terminals, localized light control in BEMS using location information of occupants, and illegal radio localization in outdoor environments. Based on the different practical application scenarios, different solutions, which are designed to enhance the localization performance, are discussed in detail. To the best of the authors' knowledge, this is the first paper to give a guideline for readers about fingerprint-based localization system in terms of fingerprint selection, hardware architecture design and algorithm enhancement

eess.SP↗

arXiv2018

Curvature-based Comparison of Two Neural Networks

In this paper we show the similarities and differences of two deep neural networks by comparing the manifolds composed of activation vectors in each fully connected layer of them. The main contribution of this paper includes 1) a new data generating algorithm which is crucial for determining the dimension of manifolds; 2) a systematic strategy to compare manifolds. Especially, we take Riemann curvature and sectional curvature as part of criterion, which can reflect the intrinsic geometric properties of manifolds. Some interesting results and phenomenon are given, which help in specifying the similarities and differences between the features extracted by two networks and demystifying the intrinsic mechanism of deep neural networks.

cs.LG↗