arXiv subjects

Tianyu Liu

Publications and source records attributed to Tianyu Liu.

At least 235 records · Page 13Linked to original sources

arXiv2020

Sufficient and necessary conditions for stabilizing singular fractional order systems with partially measurable state

This paper is concerned with the stabilization problem of singular fractional order systems with order $α\in(0,2)$. In addition to the sufficient and necessary condition for observer based control, a sufficient and necessary condition for output feedback control is proposed by adopting matrix variable decoupling technique. The developed results are more general and efficient than the existing works, especially for the output feedback case. Finally, two illustrative examples are given to verify the effectiveness and potential of the proposed approaches.

math.DS↗

arXiv2020

Strain-induced pseudomagnetic field and quantum oscillations in kagome crystals

A kagome lattice is composed of corner-sharing triangles arranged on a honeycomb lattice such that each honeycomb bond hosts a kagome site while each kagome triangle encloses a honeycomb site. Such close relation implies that the two lattices share common features. We predict here that a kagome crystal, similar to the honeycomb lattice graphene, reacts to elastic strain in a unique way that the bulk electronic states in the vicinity of Dirac points are reorganized by the strain-induced pseudomagnetic field into flat Landau levels, while the degenerate edge states in the undeformed crystal become separated in the energy dimension. When the strain is tuned continuously, the resulting scanning pseudomagnetic field gives rise to quantum oscillations in both density of states (DOS) and electric conductivity.

cond-mat.mes-hall↗

arXiv2019

Explosion cratering in 3D granular media

Sudden release of energy in explosion creates craters in granular media. In comparison with well-studied impact cratering in granular media, our understanding of explosion cratering is still primitive. Here, we study low-energy lab-scale explosion cratering in 3D granular media using controlled pulses of pressurized air. We identify four regimes of explosion cratering at different burial depths, which are associated with distinct explosion dynamics and result in different crater morphologies. We propose a general relation between the dynamics of granular flows and the surface structures of resulting craters. Moreover, we measure the diameter of explosion craters as a function of explosion pressures, durations and burial depths. We find that the size of craters is non-monotonic with increasing burial depths, reaching a maximum at an intermediate burial depth. In addition, the crater diameter shows a weak dependence on explosion pressures and durations at small burial depths. We construct a simple model to explain this finding. Finally, we explore the scaling relations of the size of explosion craters. Despite the huge difference in energy scales, we find that the diameter of explosion craters in our experiments follows the same cube root energy scaling as explosion cratering at high energies. We also discuss the dependence of rescaled crater sizes on the inertial numbers of granular flows. These results shed light onto the rich dynamics of 3D explosion cratering and provide new insights into the general physical principles governing granular cratering processes.

cond-mat.soft↗

arXiv2019

Table-to-Text Natural Language Generation with Unseen Schemas

Traditional table-to-text natural language generation (NLG) tasks focus on generating text from schemas that are already seen in the training set. This limitation curbs their generalizabilities towards real-world scenarios, where the schemas of input tables are potentially infinite. In this paper, we propose the new task of table-to-text NLG with unseen schemas, which specifically aims to test the generalization of NLG for input tables with attribute types that never appear during training. To do this, we construct a new benchmark dataset for this task. To deal with the problem of unseen attribute types, we propose a new model that first aligns unseen table schemas to seen ones, and then generates text with updated table representations. Experimental evaluation on the new benchmark demonstrates that our model outperforms baseline methods by a large margin. In addition, comparison with standard data-to-text settings shows the challenges and uniqueness of our proposed task.

cs.CL↗

arXiv2019

Deep learning for accelerating Monte Carlo radiation transport simulation in intensity-modulated radiation therapy

Cancer is a primary cause of morbidity and mortality worldwide. The radiotherapy plays a more and more important role in cancer treatment. In the radiotherapy, the dose distribution maps in patient need to be calculated and evaluated for the purpose of killing tumor and protecting healthy tissue. Monte Carlo (MC) radiation transport calculation is able to account for all aspects of radiological physics within 3D heterogeneous media such as the human body and generate the dose distribution maps accurately. However, an MC calculation for doses in radiotherapy usually takes a great mass of time to achieve acceptable statistical uncertainty, impeding the MC methods from wider clinic applications. Here we introduce a convolutional neural network (CNN), termed as Monte Carlo Denoising Net (MCDNet), to achieve the acceleration of the MC dose calculations in radiotherapy, which is trained to directly predict the high-photon (noise-free) dose maps from the low-photon (noise-much) dose maps. Thirty patients with postoperative rectal cancer who accepted intensity-modulated radiation therapy (IMRT) were enrolled in this study. 3D Gamma Index Passing Rate (GIPR) is used to evaluate the performance of predicted dose maps. The experimental results demonstrate that the MCDNet can improve the GIPR of dose maps of 1x107 photons over that of 1x108 photons, yielding over 10x speed-up in terms of photon numbers used in the MC simulations of IMRT. It is of great potential to investigate the performance of this method on the other tumor sites and treatment modalities.

physics.med-ph↗

arXiv2019

A Method of Rapid Quantification of Patient-Specific Organ Dose for CT Using Coupled Deep-Learning based Multi-Organ Segmentation and GPU-accelerated Monte Carlo Dose Computing

Purpose: This paper describes a new method to apply deep-learning algorithms for automatic segmentation of radiosensitive organs from 3D tomographic CT images before computing organ doses using a GPU-based Monte Carlo code. Methods: A deep convolutional neural network (CNN) for organ segmentation is trained to automatically delineate radiosensitive organs from CT. With a GPU-based Monte Carlo dose engine (ARCHER) to derive CT dose of a phantom made from a subject's CT scan, we are then able to compute the patient-specific CT dose for each of the segmented organs. The developed tool is validated by using Relative Dose Error (RDE) against the organ doses calculated by ARCHER with manual segmentation performed by radiologists. The dose computation results are also compared against organ doses from population-average phantoms to demonstrate the improvement achieved by using the developed tool. In this study, two datasets were used: The Lung CT Segmentation Challenge 2017 (LCTSC) dataset, which contains 60 thoracic CT scan patients each with 5 segmented organs, and the Pancreas-CT (PCT) dataset, which contains 43 abdominal CT scan patients each with 8 segmented organs. Five-fold cross-validation of the new method is performed on both datasets. Results: Comparing with the traditional organ dose evaluation method that based on population-average phantom, our proposed method achieved the smaller RDE range on all organs with -4.3%~1.5% vs -31.5%~33.9% (lung), -7.0%~2.3% vs -15.2%~125.1% (heart), -18.8%~40.2% vs -10.3%~124.1% (esophagus) in the LCTSC dataset and -5.6%~1.6% vs -20.3%~57.4% (spleen), -4.5%~4.6% vs -19.5%~61.0% (pancreas), -2.3%~4.4% vs -37.8%~75.8% (left kidney), -14.9%~5.4% vs -39.9% ~14.6% (gall bladder), -0.9%~1.6% vs -30.1%~72.5% (liver), and -23.0%~11.1% vs -52.5%~-1.3% (stomach) in the PCT dataset.

physics.med-ph↗

arXiv2019

Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation

Table-to-text generation aims to translate the structured data into the unstructured text. Most existing methods adopt the encoder-decoder framework to learn the transformation, which requires large-scale training samples. However, the lack of large parallel data is a major practical problem for many domains. In this work, we consider the scenario of low resource table-to-text generation, where only limited parallel data is available. We propose a novel model to separate the generation into two stages: key fact prediction and surface realization. It first predicts the key facts from the tables, and then generates the text with the key facts. The training of key fact prediction needs much fewer annotated data, while surface realization can be trained with pseudo parallel corpus. We evaluate our model on a biography generation dataset. Our model can achieve $27.34$ BLEU score with only $1,000$ parallel data, while the baseline model only obtain the performance of $9.71$ BLEU score.

cs.CL↗

arXiv2019

GRID: a Student Project to Monitor the Transient Gamma-Ray Sky in the Multi-Messenger Astronomy Era

The Gamma-Ray Integrated Detectors (GRID) is a space mission concept dedicated to monitoring the transient gamma-ray sky in the energy range from 10 keV to 2 MeV using scintillation detectors onboard CubeSats in low Earth orbits. The primary targets of GRID are the gamma-ray bursts (GRBs) in the local universe. The scientific goal of GRID is, in synergy with ground-based gravitational wave (GW) detectors such as LIGO and VIRGO, to accumulate a sample of GRBs associated with the merger of two compact stars and study jets and related physics of those objects. It also involves observing and studying other gamma-ray transients such as long GRBs, soft gamma-ray repeaters, terrestrial gamma-ray flashes, and solar flares. With multiple CubeSats in various orbits, GRID is unaffected by the Earth occultation and serves as a full-time and all-sky monitor. Assuming a horizon of 200 Mpc for ground-based GW detectors, we expect to see a few associated GW-GRB events per year. With about 10 CubeSats in operation, GRID is capable of localizing a faint GRB like 170817A with a 90% error radius of about 10 degrees, through triangulation and flux modulation. GRID is proposed and developed by students, with considerable contribution from undergraduate students, and will remain operated as a student project in the future. The current GRID collaboration involves more than 20 institutes and keeps growing. On August 29th, the first GRID detector onboard a CubeSat was launched into a Sun-synchronous orbit and is currently under test.

astro-ph.IM↗

arXiv2019

A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces

Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems. However, precise calibration of multiple lidars is challenging since the feature correspondences in scan points cannot always provide enough constraints. To address this problem, the existing methods require fixed calibration targets in scenes or rely exclusively on additional sensors. In this paper, we present a novel method that enables automatic lidar calibration without these restrictions. Three linearly independent planar surfaces appearing in surroundings is utilized to find correspondences. Two components are developed to ensure the extrinsic parameters to be found: a closed-form solver for initialization and an optimizer for refinement by minimizing a nonlinear cost function. Simulation and experimental results demonstrate the high accuracy of our calibration approach with the rotation and translation errors smaller than 0.05rad and 0.1m respectively.

cs.CV↗

arXiv2019

Counting perfect matchings and the eight-vertex model

We study the approximation complexity of the partition function of the eight-vertex model on general 4-regular graphs. For the first time, we relate the approximability of the eight-vertex model to the complexity of approximately counting perfect matchings, a central open problem in this field. Our results extend those in arXiv:1811.03126 [cs.CC]. In a region of the parameter space where no previous approximation complexity was known, we show that approximating the partition function is at least as hard as approximately counting perfect matchings via approximation-preserving reductions. In another region of the parameter space which is larger than the previously known FPRASable region, we show that computing the partition function can be reduced to (with or without approximation) counting perfect matchings. Moreover, we give a complete characterization of nonnegatively weighted (not necessarily planar) 4-ary matchgates, which has been open for several years. The key ingredient of our proof is a geometric lemma. We also identify a region of the parameter space where approximating the partition function on planar 4-regular graphs is feasible but on general 4-regular graphs is equivalent to approximately counting perfect matchings. To our best knowledge, these are the first problems of this kind.

cs.CC↗

arXiv2019

Magnon quantum anomalies in Weyl ferromagnets

When subjected to parallel electric field $\mathbf E$ and magnetic field $\mathbf B$, Weyl semimetals exhibit the exotic transport property known as the chiral anomaly due to the pumping of electrons between Weyl cones of opposite chiralities. When one or both electromagnetic (EM) fields are replaced by strain-induced chiral pseudo-electromagnetic (pseudo-EM) fields, other types of quantum anomalies occur. In the present paper, we will show that such quantum anomalies can be reproduced in a completely different system -- a Weyl ferromagnet whose magnonic structure remarkably encodes Weyl physics. By analytical and numerical calculations, we will show that the magnon bands can be Landau-quantized by either an inhomogeneous electric field $\mathbf E$ or a chiral pseudo-electric field $\mathbf e$ induced by a torsional strain, and magnons can be pumped along bands by either an inhomogeneous magnetic field $\mathbf B$ or a chiral pseudo-magnetic field $\mathbf b$ due to a dynamic uniaxial strain. We list the magnon quantum anomalies and the associated anomalous spin and heat currents in the Weyl ferromagnet, and show they are distinct from their Weyl semimetal counterparts.

cond-mat.mes-hall↗

arXiv2018

Approximability of the Eight-vertex Model

We initiate a study of the classification of approximation complexity of the eight-vertex model defined over 4-regular graphs. The eight-vertex model, together with its special case the six-vertex model, is one of the most extensively studied models in statistical physics, and can be stated as a problem of counting weighted orientations in graph theory. Our result concerns the approximability of the partition function on all 4-regular graphs, classified according to the parameters of the model. Our complexity results conform to the phase transition phenomenon from physics. We introduce a quantum decomposition of the eight-vertex model and prove a set of closure properties in various regions of the parameter space. Furthermore, we show that there are extra closure properties on 4-regular planar graphs. These regions of the parameter space are concordant with the phase transition threshold. Using these closure properties, we derive polynomial time approximation algorithms via Markov chain Monte Carlo. We also show that the eight-vertex model is NP-hard to approximate on the other side of the phase transition threshold.

cs.CC↗

arXiv2018

Torpid Mixing of Markov Chains for the Six-vertex Model on $\mathbb{Z}^2$

In this paper, we study the mixing time of two widely used Markov chain algorithms for the six-vertex model, Glauber dynamics and the directed-loop algorithm, on the square lattice $\mathbb{Z}^2$. We prove, for the first time that, on finite regions of the square lattice these Markov chains are torpidly mixing under parameter settings in the ferroelectric phase and the anti-ferroelectric phase.

cs.DS↗

arXiv2018

Incorporating Glosses into Neural Word Sense Disambiguation

Word Sense Disambiguation (WSD) aims to identify the correct meaning of polysemous words in the particular context. Lexical resources like WordNet which are proved to be of great help for WSD in the knowledge-based methods. However, previous neural networks for WSD always rely on massive labeled data (context), ignoring lexical resources like glosses (sense definitions). In this paper, we integrate the context and glosses of the target word into a unified framework in order to make full use of both labeled data and lexical knowledge. Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly encodes the context and glosses of the target word. GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods. We further extend the original gloss of word sense via its semantic relations in WordNet to enrich the gloss information. The experimental results show that our model outperforms the state-of-theart systems on several English all-words WSD datasets.

cs.CL↗

arXiv2018

Charge/Spin Supercurrent and the Fulde-Ferrell State Induced by Crystal Deformation in Weyl/Dirac Superconductors

It has been predicted that emergent chiral magnetic fields can be generated by crystal deformation in Weyl/Dirac metals and superconductors. The emergent fields give rise to chiral anomaly phenomena as in the case of Weyl semimetals with usual electromagnetic fields. Here, we clarify effects of the chiral magnetic field on Cooper pairs in Weyl/Dirac superconductors on the basis of the Ginzburg-Landau equation microscopically derived from the quasiclassical Eilenberger formalism. It is found that Cooper pairs are affected by the emergent chiral magnetic field in a dramatic way, and the pseudo-Lorentz force due to the chiral magnetic field stabilizes the Fulde-Ferrell state and causes a charge/spin supercurrent which flows parallel to the chiral magnetic field in the case of Weyl/Dirac superconductors. This effect is in analogy with the chiral magnetic effect (CME) of Weyl semimetals. In addition, we elucidate that neither Meissner effect nor vortex state due to chiral magnetic fields occurs.

cond-mat.supr-con↗

arXiv2018

Quantum oscillations and Dirac-Landau levels in Weyl superconductors

When magnetic field is applied to metals and semimetals quantum oscillations appear as individual Landau levels cross the Fermi level. Quantum oscillations generally do not occur in superconductors (SC) because magnetic field is either expelled from the sample interior or, if strong enough, drives the material into the normal state. In addition, elementary excitations of a superconductor -- Bogoliubov quasiparticles -- do not carry a well defined electric charge and therefore do not couple in a simple way to the applied magnetic field. We predict here that in Weyl superconductors certain types of elastic strain have the ability to induce chiral pseudo-magnetic field which can reorganize the electronic states into Dirac-Landau levels with linear band crossings at low energy. The resulting quantum oscillations in the quasiparticle density of states and thermal conductivity can be experimentally observed under a bending deformation of a thin film Weyl SC and provide new insights into this fascinating family of materials.

cond-mat.supr-con↗

arXiv2017

Approximability of the Six-vertex Model

In this paper we take the first step toward a classification of the approximation complexity of the six-vertex model, an object of extensive research in statistical physics. Our complexity results conform to the phase transition phenomenon from physics. We show that the approximation complexity of the six-vertex model behaves dramatically differently on the two sides separated by the phase transition threshold. Furthermore, we present structural properties of the six-vertex model on planar graphs for parameter settings that have known relations to the Tutte polynomial $T(G, x, y)$.

cs.CC↗

arXiv2017

Table-to-text Generation by Structure-aware Seq2seq Learning

Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https://github.com/tyliupku/wiki2bio.

cs.CL↗