arXiv subjects

Yu Li

Publications and source records attributed to Yu Li.

At least 541 records · Page 30Linked to original sources

arXiv2019

Spin fluctuation anisotropy as a probe of orbital-selective hole-electron quasiparticle excitations in detwinned Ba(Fe1-xCox)2As2

We use inelastic neutron scattering to study spin excitation anisotropy in mechanically detwinned Ba(Fe1-xCox)2As2 with x = 0.048 and 0.054. Both samples exhibit a tetragonal-to-orthorhombic structural transition at Ts, a collinear static antiferromagnetic (AF) order at wave vector Q1 = QAF = (1, 0) below the Neel temperature TN, and superconductivity below Tc (Ts > TN > Tc). In the high temperature paramagnetic tetragonal phase (T > Ts), spin excitations centered at Q1 and Q2 = (0, 1) are gapless and have four-fold (C4) rotational symmetry. On cooling to below TN but above Tc, spin excitations become highly anisotropic, developing a gap at Q2 but still are gapless at Q1. Upon entering into the superconducting state, a neutron spin resonance appears at Q1 with no magnetic scattering at Q2. By comparing these results with those from angle resolved photoemission spectroscopy experiments, we conclude that the anisotropic shift of the dyz and dxz bands in detwinned Ba(Fe1-xCox)2As2 below Ts is associated with the spin excitation anisotropy, and the superconductivity-induced resonance arises from the electron-hole Fermi surface nesting of quasiparticles with the dyz orbital characters.

cond-mat.supr-con↗

arXiv2019

Possible 'symmetry-imposed' near-nodal two-dimensional p-wave pairing in Sr$_2$RuO$_4$

One key feature of the multi-orbital superconducting Sr$_2$RuO$_4$ is the presence of nodal or near-nodal quasiparticle excitations revealed in a wide variety of experiments. Typically, a nodal gap structure in a two-dimensional model would be inconsistent with the chiral or helical p-wave interpretations. However, we demonstrate that true gap nodes may emerge along certain high-symmetry directions on the quasi-one-dimensional Fermi surfaces, if the multi-orbital chiral or helical p-wave pairings acquire peculiar forms wherein the $d_{xz}$ and $d_{yz}$ orbitals develop $k_y$- and $k_x$-like pairings, respectively. Spin-orbit coupling $η$ induces a near-nodal gap of order $η^2/W^2 Δ_0$, where $Δ_0$ is the gap amplitude and $W$ roughly the bandwidth. Provided the aforementioned pairing is predominant, the near-nodal gap structure is robust upon the inclusion of other multi-orbital pairings that share the same symmetries. In light of the recent experimental progresses, our proposal suggests that two-dimensional p-wave pairings may still be viable candidate ground states for Sr$_2$RuO$_4$. A near-nodal helical p-wave order, for example, would also be consistent with the substantial drop in the NMR Knight shift under an in-plane magnetic field.

cond-mat.supr-con↗

arXiv2019

Flat band magnetism and helical magnetic order in Ni-doped SrCo$_2$As$_2$

A series of Sr(Co$_{1-x}$Ni$_x$)$_2$As$_2$ single crystals was synthesized allowing a comprehensive phase diagram with respect to field, temperature, and chemical substitution to be established. Our neutron diffraction experiments revealed a helimagnetic order with magnetic moments ferromagnetically (FM) aligned in the $ab$ plane and a helimagnetic wavevector of $q=(0,0,0.56)$ for $x$ = 0.1. The combination of neutron diffraction and angle-resolved photoemission spectroscopy (ARPES) measurements show that the tuning of a flat band with $d_{x^2-y^2}$ orbital character drives the helimagnetism and indicates the possibility of a quantum order-by-disorder mechanism.

cond-mat.str-el↗

arXiv2019

Nearly-degenerate $p_x+ip_y$ and $d_{x^2-y^2}$ pairing symmetry in the heavy fermion superconductor YbRh$_2$Si$_2$

Recent discovery of superconductivity in YbRh$_2$Si$_2$ has raised particular interest in its pairing mechanism and gap symmetry. Here we propose a phenomenological theory of its superconductivity and investigate possible gap structures by solving the multiband Eliashberg equations combining realistic Fermi surfaces from first-principles calculations and a quantum critical form of magnetic pairing interactions. The resulting gap symmetry shows sensitive dependence on the in-plane propagation wave vector of the quantum critical fluctuations, suggesting that superconductivity in YbRh$_2$Si$_2$ is located on the border of $(p_x+ip_y)$ and $d_{x^2-y^2}$-wave solutions. This leads to two candidate phase diagrams: one has only a spin-triplet $(p_x+ip_y)$-wave superconducting phase; the other contains multiple phases with a spin-singlet $d_{x^2-y^2}$-wave state at zero field and a field-induced spin-triplet $(p_x+ip_y)$-wave state. In addition, the electron pairing is found to be dominated by the `jungle-gym' Fermi surface rather than the `doughnut'-like one, in contrast to previous thought. This requests a more elaborate and renewed understanding of the electronic properties of YbRh$_2$Si$_2$.

cond-mat.supr-con↗

arXiv2019

Image-based reconstruction for the impact problems by using DPNNs

With the improvement of the pattern recognition and feature extraction of Deep Neural Networks (DPNNs), image-based design and optimization have been widely used in multidisciplinary researches. Recently, a Reconstructive Neural Network (ReConNN) has been proposed to obtain an image-based model from an analysis-based model [1, 2], and a steady-state heat transfer of a heat sink has been successfully reconstructed. Commonly, this method is suitable to handle stable-state problems. However, it has difficulties handling nonlinear transient impact problems, due to the bottlenecks of the Deep Neural Network (DPNN). For example, nonlinear transient problems make it difficult for the Generative Adversarial Network (GAN) to generate various reasonable images. Therefore, in this study, an improved ReConNN method is proposed to address the mentioned weaknesses. Time-dependent ordered images can be generated. Furthermore, the improved method is successfully applied in impact simulation case and engineering experiment. Through the experiments, comparisons and analyses, the improved method is demonstrated to outperform the former one in terms of its accuracy, efficiency and costs.

cs.OH↗

arXiv2019

Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images

Goal: Squamous cell carcinoma of cervix is one of the most prevalent cancer worldwide in females. Traditionally, the most indispensable diagnosis of cervix squamous carcinoma is histopathological assessment which is achieved under microscope by pathologist. However, human evaluation of pathology slide is highly depending on the experience of pathologist, thus big inter- and intra-observer variability exists. Digital pathology, in combination with deep learning provides an opportunity to improve the objectivity and efficiency of histopathologic slide analysis. Methods: In this study, we obtained 800 haematoxylin and eosin stained slides from 300 patients suffered from cervix squamous carcinoma. Based on information from morphological heterogeneity in the tumor and its adjacent area, we established deep learning models using popular convolution neural network architectures (inception-v3, InceptionResnet-v2 and Resnet50). Then random forest was introduced to feature extractions and slide-based classification. Results: The overall performance of our proposed models on slide-based tumor discrimination were outstanding with an AUC scores > 0.94. While, location identifications of lesions in whole slide images were mediocre (FROC scores > 0.52) duo to the extreme complexity of tumor tissues. Conclusion: For the first time, our analysis workflow highlighted a quantitative visual-based slide analysis of cervix squamous carcinoma. Significance: This study demonstrates a pathway to assist pathologist and accelerate the diagnosis of patients by utilizing new computational approaches.

cs.CV↗

arXiv2019

Anisotropic spin fluctuations in detwinned FeSe

Superconductivity in FeSe emerges from a nematic phase that breaks four-fold rotational symmetry in the iron plane. This phase may arise from orbital ordering, spin fluctuations, or hidden magnetic quadrupolar order. Here we use inelastic neutron scattering on a mosaic of single crystals of FeSe detwinned by mounting on a BaFe2As2 substrate to demonstrate that spin excitations are most intense at the antiferromagnetic wave vectors QAF = (1, 0) at low energies E = 6-11 meV in the normal state. This two-fold (C2) anisotropy is reduced at lower energies 3-5 meV, indicating a gapped four-fold (C4) mode. In the superconducting state, however, the strong nematic anisotropy is again reflected in the spin resonance (E = 3.7 meV) at QAF with incommensurate scattering around 5-6 meV. Our results highlight the extreme electronic anisotropy of the nematic phase of FeSe and are consistent with a highly anisotropic superconducting gap driven by spin fluctuations.

cond-mat.str-el↗

arXiv2019

Case Study of the Proof of Cook's theorem - Interpretation of A(w)

Cook's theorem is commonly expressed such as any polynomial time-verifiable problem can be reduced to the SAT problem. The proof of Cook's theorem consists in constructing a propositional formula A(w) to simulate a computation of TM, and such A(w) is claimed to be CNF to represent a polynomial time-verifiable problem w. In this paper, we investigate A(w) through a very simple example and show that, A(w) has just an appearance of CNF, but not a true logical form. This case study suggests that there exists the begging the question in Cook's theorem.

cs.CC↗

arXiv2019

Inquiry of P-reduction in Cook's 1971 Paper -- from Oracle machine to Turing machine

In this paper, we inquire the key concept P-reduction in Cook's theorem and reveal that there exists the fallacy of definition in P-reduction caused by the disguised displacement of NDTM from Oracle machine to Turing machine. The definition or derivation of P-reduction is essentially equivalent to Turing's computability. Whether NP problems might been reduced to logical forms (tautology or SAT) or NP problems might been reduced each other, they have not been really proven in Cook's 1971 paper.

cs.OH↗

arXiv2019

Quantum Boson Algebra and Poisson Geometry of the Flag Variety

In his work on crystal bases \cite{Kas}, Kashiwara introduced a certain degeneration of the quantized universal enveloping algebra of a semi-simple Lie algebra $\mathfrak g$, which he called a quantum boson algebra. In this paper, we construct Kashiwara operators associated to all positive roots and use them to define a variant of Kashiwara's quantum boson algebra. We show that a quasi-classical limit of the positive half of our variant is a Poisson algebra of the form $(P \simeq \mathbb C[\mathfrak n^{\ast}], \, \{~~,~~\}_P)$, where $\mathfrak n$ is the positive part of $\mathfrak g$ and $\{~~,~~\}_P$ is a Poisson bracket that has the same rank as, but is different from, the Kirillov-Kostant bracket $\{~~,~~\}_{KK}$ on $\mathfrak n^{\ast}$. Furthermore, we prove that, in the special case of type $A$, any linear combination $a_1 \{~~,~~\}_P + a_2 \{~~,~~\}_{KK}$, $a_1, a_2 \in \mathbb C$, is again a Poisson bracket. In the general case, we establish an isomorphism of $P$ and the Poisson algebra of regular functions on the open Bruhat cell in the flag variety. In type $A$, we also construct a Casimir function on the open Bruhat cell, together with its quantization, which may be thought of as an analog of the linear function on $\mathfrak n^{\ast}$ defined by a root vector for the highest root.

math.QA↗

arXiv2019

Deep learning in bioinformatics: introduction, application, and perspective in big data era

Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at \url{https://github.com/lykaust15/Deep_learning_examples}.

q-bio.QM↗

arXiv2019

Interpretation of NDTM in the definition of NP

In this paper, we interpret NDTM (NonDeterministic Turing Machine) used to define NP by tracing to the source of NP. Originally NP was defined as the class of problems solvable in polynomial time by a NDTM in the theorem of Cook, where the NDTM was represented as Query Machine of essence Oracle. Later a model consisting of a guessing module and a checking module was proposed to replace the NDTM. This model of essence TM has a fundamental difference from the NDTM of essence Oracle, but people still use the term NDTM to designate this model, which leads to the disguised displacement of NDTM and produces out the verifier-based definition of NP as the class of problems verifiable in polynomial time by a TM (Turing Machine). This verifier-based one has been then accepted as the standard definition of NP where comes from the famous equivalence of the two definitions of NP. Since then the notion of nondeterminism is lost from NP, which causes ambiguities in understanding NP and then great difficulties in solving the P versus NP problem. Since NP is originally related with Oracle that comes from the work of Turing work about Computability, it seems quite necessary to trace back to the work of Turing and clarify further the issue about NP.

cs.CC↗

arXiv2019

Coexistence of ferromagnetic and stripe antiferromagnetic spin fluctuations in SrCo$_2$As$_2$

We use inelastic neutron scattering to study energy and wave vector dependence of spin fluctuations in SrCo$_2$As$_2$, derived from SrFe$_{2-x}$Co$_x$As$_2$ iron pnictide superconductors. Our data reveals the coexistence of antiferromagnetic (AF) and ferromagnetic (FM) spin fluctuations at wave vectors $\textbf{Q}_{\rm AF}$=(1,0) and $\textbf{Q}_{\rm FM}$=(0,0)/(2,0), respectively. By comparing neutron scattering results with those of dynamic mean field theory calculation and angle-resolved photoemission spectroscopy experiments, we conclude that both AF and FM spin fluctuations in SrCo$_2$As$_2$ are closely associated with a flat band of the $e_g$ orbitals near the Fermi level, different from the $t_{2g}$ orbitals in superconducting SrFe$_{2-x}$Co$_x$As$_2$. Therefore, Co-substitution in SrFe$_{2-x}$Co$_x$As$_2$ induces a $t_{2g}$ to $e_g$ orbital switching, and is responsible for FM spin fluctuations detrimental to the singlet pairing superconductivity.

cond-mat.str-el↗

arXiv2019

Can CNN Construct Highly Accurate Models Efficiently for High-Dimensional Problems in Complex Product Designs?

With the increase of the nonlinearity and dimension, it is difficult for the present popular metamodeling techniques to construct reliable metamodels. To address this problem, Convolutional Neural Network (CNN) is introduced to construct a highly accurate metamodel efficiently. Considering the inherent characteristics of the CNN, it is a potential modeling tool to handle highly nonlinear and dimensional problems (hundreds-dimensional problems) with the limited training samples. In order to evaluate the proposed CNN metamodel for hundreds-dimensional and strong nonlinear problems, CNN is compared with other metamodeling techniques. Furthermore, several high-dimensional analytical functions are also employed to test the CNN metamodel. Testing and comparisons confirm the efficiency and capability of the CNN metamodel for hundreds-dimensional and strong nonlinear problems. Moreover, the proposed CNN metamodel is also applied to IsoGeometric Analysis (IGA)-based optimization successfully.

cs.CV↗

arXiv2019

Image-Based Reconstruction for a 3D-PFHS Heat Transfer Problem by ReConNN

The heat transfer performance of Plate Fin Heat Sink (PFHS) has been investigated experimentally and extensively. Commonly, the objective function of the PFHS design is based on the responses of simulations. Compared with existing studies, the purpose of this study is to transfer from analysis-based model to image-based one for heat sink designs. Compared with the popular objective function based on maximum, mean, variance values etc., more information should be involved in image-based and thus a more objective model should be constructed. It means that the sequential optimization should be based on images instead of responses and more reasonable solutions should be obtained. Therefore, an image-based reconstruction model of a heat transfer process for a 3D-PFHS is established. Unlike image recognition, such procedure cannot be implemented by existing recognition algorithms (e.g. Convolutional Neural Network) directly. Therefore, a Reconstructive Neural Network (ReConNN), integrated supervised learning and unsupervised learning techniques, is suggested and improved to achieve higher accuracy. According to the experimental results, the heat transfer process can be observed more detailed and clearly, and the reconstructed results are meaningful for the further optimizations.

cs.CE↗

arXiv2019

Heat kernel on Ricci shrinkers

In this paper, we systematically study the heat kernel of the Ricci flows induced by Ricci shrinkers. We develop several estimates which are much sharper than their counterparts in general closed Ricci flows. Many classical results, including the optimal Logarithmic Sobolev constant estimate, the Sobolev constant estimate, the no-local-collapsing theorem, the pseudo-locality theorem and the strong maximum principle for curvature tensors, are essentially improved for Ricci flows induced by Ricci shrinkers. Our results provide many necessary tools to analyze short time singularities of the Ricci flows of general dimension.

math.DG↗

arXiv2019

On the Decision Boundary of Deep Neural Networks

While deep learning models and techniques have achieved great empirical success, our understanding of the source of success in many aspects remains very limited. In an attempt to bridge the gap, we investigate the decision boundary of a production deep learning architecture with weak assumptions on both the training data and the model. We demonstrate, both theoretically and empirically, that the last weight layer of a neural network converges to a linear SVM trained on the output of the last hidden layer, for both the binary case and the multi-class case with the commonly used cross-entropy loss. Furthermore, we show empirically that training a neural network as a whole, instead of only fine-tuning the last weight layer, may result in better bias constant for the last weight layer, which is important for generalization. In addition to facilitating the understanding of deep learning, our result can be helpful for solving a broad range of practical problems of deep learning, such as catastrophic forgetting and adversarial attacking. The experiment codes are available at https://github.com/lykaust15/NN_decision_boundary

cs.NE↗

arXiv2019

Possible nodeless $s^\pm$-wave superconductivity in twisted bilayer graphene

Recent discovery of superconductivity in the twisted bilayer graphene has stimulated numerous theoretical proposals concerning its exact gap symmetry. Among them, $d+id$ or $p+ip$-wave were believed to be the most plausible solutions. Here considering the superconductivity emerges near a correlated insulating state and may be induced by antiferromagnetic spin fluctuations, we apply the strong-coupling Eliashberg theory with both inter- and intraband quantum critical pairing interactions and discuss the possible gap symmetry in an effective low-energy four-orbital model. Our calculations reveal a nodeless $s^\pm$-wave as the most probable candidate for superconducting gap symmetry in the experimentally relevant parameter range. This solution is distinctly different from previous theoretical proposals. In particular, it contains interesting topological components in the valley space, which might be tuned by experimental manipulation of the valley degree of freedom.

cond-mat.supr-con↗