arXiv subjects

Yi Gao

Publications and source records attributed to Yi Gao.

At least 73 records · Page 4Linked to original sources

arXiv2021

Vector Detection Network: An Application Study on Robots Reading Analog Meters in the Wild

Analog meters equipped with one or multiple pointers are wildly utilized to monitor vital devices' status in industrial sites for safety concerns. Reading these legacy meters {\bi autonomously} remains an open problem since estimating pointer origin and direction under imaging damping factors imposed in the wild could be challenging. Nevertheless, high accuracy, flexibility, and real-time performance are demanded. In this work, we propose the Vector Detection Network (VDN) to detect analog meters' pointers given their images, eliminating the barriers for autonomously reading such meters using intelligent agents like robots. We tackled the pointer as a two-dimensional vector, whose initial point coincides with the tip, and the direction is along tail-to-tip. The network estimates a confidence map, wherein the peak pixels are treated as vectors' initial points, along with a two-layer scalar map, whose pixel values at each peak form the scalar components in the directions of the coordinate axes. We established the Pointer-10K dataset composing of real-world analog meter images to evaluate our approach due to no similar dataset is available for now. Experiments on the dataset demonstrated that our methods generalize well to various meters, robust to harsh imaging factors, and run in real-time.

cs.RO↗

arXiv2021

Dynamic Normalization

Batch Normalization has become one of the essential components in CNN. It allows the network to use a higher learning rate and speed up training. And the network doesn't need to be initialized carefully. However, in our work, we find that a simple extension of BN can increase the performance of the network. First, we extend BN to adaptively generate scale and shift parameters for each mini-batch data, called DN-C (Batch-shared and Channel-wise). We use the statistical characteristics of mini-batch data ($E[X], Std[X]\in\mathbb{R}^{c}$) as the input of SC module. Then we extend BN to adaptively generate scale and shift parameters for each channel of each sample, called DN-B (Batch and Channel-wise). Our experiments show that DN-C model can't train normally, but DN-B model has very good robustness. In classification task, DN-B can improve the accuracy of the MobileNetV2 on ImageNet-100 more than 2% with only 0.6% additional Mult-Adds. In detection task, DN-B can improve the accuracy of the SSDLite on MS-COCO nearly 4% mAP with the same settings. Compared with BN, DN-B has stable performance when using higher learning rate or smaller batch size.

cs.CV↗

arXiv2020

Unidirectional Oriented Water Wire in Short Nanotube

The orientation of water molecules is the key factor for the fast transport of water in small nanotubes. It has been accepted that the bidirectional water burst in short nanotubes can be transformed into unidirectional transport when the orientation of water molecules is maintained in long nanotubes under the external field. In this work, based on molecular dynamics simulations and first-principles calculations, we showed without external field, it only needs 21 water molecules to maintain the unidirectional single file water intrinsically in carbon nanotube at seconds. Detailed analysis indicates that the surprising result comes from the step by step process for the flip of water chain, which is different with the perceived concerted mechanism. Considering the thickness of cell membrane (normally 5-10 nm) is larger than the length threshold of the unidirectional water wire, this study suggests it may not need the external field to maintain the unidirectional flow in the water channel at the macroscopic timescale.

physics.comp-ph↗

arXiv2020

Single-Atom Alloy Catalysts Designed by First-Principles Calculations and Artificial Intelligence

Single-atom metal alloy catalysts (SAACs) have recently become a very active new frontier in catalysis research. The simultaneous optimization of both facile dissociation of reactants and a balanced strength of intermediates' binding make them highly efficient and selective for many industrially important reactions. However, discovery of new SAACs is hindered by the lack of fast yet reliable prediction of the catalytic properties of the sheer number of candidate materials. In this work, we address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Our approach is faster and more accurate than the current state-of-the-art linear relationships. Besides consistently predicting high efficiency of the experimentally studied Pd/Cu, Pt/Cu, Pd/Ag, Pt/Au, Pd/Au, Pt/Ni, Au/Ru, and Ni/Zn SAACs (the first metal is the dispersed component), we identify more than two hundred yet unreported candidates. Some of these new candidates are predicted to exhibit even higher stability and efficiency than the reported ones. Our study demonstrates the importance of breaking linear relationships to avoid bias in catalysis design, as well as provides a recipe for selecting best candidate materials from hundreds of thousands of transition-metal SAACs for various applications.

cond-mat.mtrl-sci↗

arXiv2020

Spin excitations in nickelate superconductors

We study theoretically spin excitations in the newly discovered nickelate superconductors based on a single-band model and the random phase approximation. The spin excitations are found to be incommensurate in a low energy region. A spin resonance phenomena is revealed as the excitation energy increases. The maximum intensity may be at the incommensurate momentum or the commensurate momentum, depending on the out-of-plane momentum. The spin excitations become incommensurate again at higher energies. The similarities and differences of the spin excitations between nickelate and cuprate superconductors are addressed. Our predicted results can be tested by inelastic neutron scattering experiments later.

cond-mat.supr-con↗

arXiv2020

MCMC Guided CNN Training and Segmentation for Pancreas Extraction

Efficient organ segmentation is the precondition of various quantitative analysis. Segmenting the pancreas from abdominal CT images is a challenging task because of its high anatomical variability in shape, size and location. What's more, the pancreas only occupies a small portion in abdomen, and the organ border is very fuzzy. All these factors make the segmentation methods of other organs less suitable for the pancreas segmentation. In this report, we propose a Markov Chain Monte Carlo (MCMC) sampling guided convolutional neural network (CNN) approach, in order to handle such difficulties in morphological and photometric variabilities. Specifically, the proposed method mainly contains three steps: First, registration is carried out to mitigate the body weight and location variability. Then, an MCMC sampling is employed to guide the sampling of 3D patches, which are fed to the CNN for training. At the same time, the pancreas distribution is also learned for the subsequent segmentation. Third, sampled from the learned distribution, an MCMC process guides the segmentation process. Lastly, the patches based segmentation is fused using a Bayesian voting scheme. This method is evaluated on the NIH pancreatic datasets which contains 82 abdominal contrast-enhanced CT volumes. Finally, we achieved a competitive result of 78.13% Dice Similarity Coefficient value and 82.65% Recall value in testing data.

cs.CV↗

arXiv2020

FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction

An innovative physics-guided learning algorithm for predicting the mechanical response of materials and structures is proposed in this paper. The key concept of the proposed study is based on the fact that physics models are governed by Partial Differential Equation (PDE), and its loading/ response mapping can be solved using Finite Element Analysis (FEA). Based on this, a special type of deep convolutional neural network (DCNN) is proposed that takes advantage of our prior knowledge in physics to build data-driven models whose architectures are of physics meaning. This type of network is named as FEA-Net and is used to solve the mechanical response under external loading. Thus, the identification of a mechanical system parameters and the computation of its responses are treated as the learning and inference of FEA-Net, respectively. Case studies on multi-physics (e.g., coupled mechanical-thermal analysis) and multi-phase problems (e.g., composite materials with random micro-structures) are used to demonstrate and verify the theoretical and computational advantages of the proposed method.

cs.CE↗

arXiv2019

Quantum phase transition of correlated iron-based superconductivity in LiFe$_{1-x}$Co$_x$As

The interplay between unconventional Cooper pairing and quantum states associated with atomic scale defects is a frontier of research with many open questions. So far, only a few of the high-temperature superconductors allow this intricate physics to be studied in a widely tunable way. We use scanning tunneling microscopy (STM) to image the electronic impact of Co atoms on the ground state of the LiFe$_{1-x}$Co$_x$As system. We observe that impurities progressively suppress the global superconducting gap and introduce low energy states near the gap edge, with the superconductivity remaining in the strong-coupling limit. Unexpectedly, the fully opened gap evolves into a nodal state before the Cooper pair coherence is fully destroyed. Our systematic theoretical analysis shows that these new observations can be quantitatively understood by the nonmagnetic Born-limit scattering effect in a s$\pm$-wave superconductor, unveiling the driving force of the superconductor to metal quantum phase transition.

cond-mat.supr-con↗

arXiv2019

Insulator-metal transition and quasi-flat-band of Shastry-Sutherland lattice

Insulator-metal transition is investigated self-consistently on the frustrated Shastry-Sutherland lattice in the framework of Slave-Boson mean-field theory. Due to the presence of quasi-flat band structure characteristic, the system displays a spin-density-wave (SDW) insulating phase at the weak doping levels, which is robust against frustration, and it will be transited into an SDW metallic phase at high doping levels. As further increasing the doping, the temperature or the frustration on the diagonal linking bonds, the magnetic order $m$ will be monotonically suppressed, resulting in the appearance of a paramagnetic metallic phase. Although the Fermi surface of the SDW metallic phase may be immersed by temperature, the number of mobile charges is robust against temperature at weak doping levels.

cond-mat.str-el↗

arXiv2019

Anomalous sharp peak in the London penetration depth induced by the nodeless-to-nodal superconducting transition in BaFe$_2$(As$_{1-x}$P$_x$)$_2$

The issue of whether the quantum critical point (QCP) is hidden inside unconventional superconductors is a matter of hot debate. Although a prominent experiment on London penetration depth has demonstrated the existence of the QCP in the isovalent-doped iron-based superconductor BaFe$_2$(As$_{1-x}$P$_x$)$_2$, with the observation of a sharp peak in the penetration depth in the vicinity of the disappearance of magnetic order at zero temperature, the nature of such an emerging QCP remains unclear. Here, we provide a unique picture to understand well the phenomena of the QCP based on the framework of linear response theory. Evidence from the density of states and superfluid density calculations suggests the nodeless-to-nodal pairing transition accompanied the appearance of a sharp peak in the penetration depth in BaFe$_2$(As$_{1-x}$P$_x$)$_2$. Such a pairing transition originates from the three-dimensional electronic properties with a strong interlayer superconducting pairing. This finding provides a significant insight into the understanding of the QCP observed in experiment in BaFe$_2$(As$_{1-x}$P$_x$)$_2$.

cond-mat.supr-con↗

arXiv2019

Impurity-induced resonant states in topological nodal-line semimetals

Nodal-line semimetals are characterized by a kind of topologically nontrivial bulk-band crossing, giving rise to almost flat surface states. Yet, a direct evidence of the surface states is still lacking. Here we study theoretically impurity effects in topological nodal-line semimetals based on the T-matrix method. It is found that for a bulk impurity, some in-gap states may be induced near the impurity site, while the visible resonant impurity state can only exist for certain strength of the impurity potentials. For a surface impurity, robust resonant impurity states exist in a wide range of impurity potentials. Such robust resonant states stem from the topological protected weak dispersive surface states, which can be probed by scanning tunneling microscopy, providing a strong signature of the topological surface states in the nodal-line semimetals.

cond-mat.mes-hall↗

arXiv2018

Possible pairing symmetry in the FeSe-based superconductors determined by quasiparticle interference

We study the momentum-integrated quasiparticle interference (QPI) in the FeSe-based superconductors. This method was recently proposed theoretically and has been applied to determine the pairing symmetry in these materials experimentally. Our findings suggest that, if the incipient bands and the superconducting (SC) pairing on them are taken into consideration, then the experimentally measured bound states and momentum-integrated QPI can be well fitted, even if the SC order parameter does not change sign on the Fermi surfaces. Therefore, we offer an alternative explanation to the experimental data, calling for more careful identification of the pairing symmetry that is important for the pairing mechanism.

cond-mat.supr-con↗

arXiv2018

Nearest-Neighbor Broken Bond Model for Describing the Surface Energies of Transition Metals

In our recent works, we used a nearest-neighbor broken bond (NNBB) model to fit the surface energy of Pt, Pd, and Au surfaces [Adv. Theory Simul. 1800127 (2018)]. It was verified that this model could describe the surface energies of above noble metals with high accuracy. The applications of this model in real-time simulations of equilibrium nanocrystal transformations are in good agreements with the experimental observations at macroscopic time scales quantitatively. In this work, we want to further verify the generality of the NNBB model for other transition metals. All surface tentions and lattice constants were extracted from the database of Materials Project. The number of missing NN bonds of different slabs from low-index surfaces to open surfaces were counted for both surface atoms and subsurface atoms. As presented below, linear relationships between surface energies and missing bonds number are found for all studied elements. The bond energies for different metals are obtained through fitting NN bonds and surface energy of different surfaces.

cond-mat.mtrl-sci↗

arXiv2018

Detecting competing orders through the edge states in the heterostructures with high-Tc superconductors

Considering a $d$-wave superconductor being in proximity to a two-dimensional Weyl model, a topological superconductor with gapless edge states may be realized. We here demonstrate that the system can become topologically trivial when an additional d-density-wave order is also included. The edge states become gapped and may be detected in experiments. For this, the d-density-wave order can be detected experimentally, so that different scenarios for the pseudogap in high-T$_c$ superconductors may be distinguished. Moreover, this method may be used to detect various hidden orders through putting high-T$_c$ superconductors being in proximity to various topological nontrivial materials.

cond-mat.supr-con↗

arXiv2018

Resolving different pairing states in Weyl superconductors through the single-particle spectrum

We study theoretically single-particle spectra of Weyl superconductors. Three different superconducting pairing states are addressed, which are the BCS-type states with the $s$-wave pairing symmetry and the $p+ip$-wave pairing symmetry, and the FFLO pairing state. We elaborate that these three states can be resolved well based on the bulk and surface spectral functions as well as the local density of states. The single impurity effect is also explored, which may help us to differentiate the BCS-type pairing states and the FFLO state further.

cond-mat.supr-con↗

arXiv2018

Molecular Heat Engines: Quantum Coherence Effects

Recent developments in nanoscale experimental techniques made it possible to utilize single molecule junctions as devices for electronics and energy transfer with quantum coherence playing an important role in their thermoelectric characteristics. Theoretical studies on the efficiency of nanoscale devices usually employ rate (Pauli) equations, which do not account for quantum coherence. Therefore, the question whether quantum coherence could improve the efficiency of a molecular device cannot be fully addressed within such considerations. Here, we employ a nonequilibrium Green function approach to study the effects of quantum coherence and dephasing on the thermoelectric performance of molecular heat engines. Within a generic bichromophoric donor-bridge-acceptor junction model, we show that quantum coherence may increase efficiency compared to quasi-classical (rate equation) predictions and that pure dephasing and dissipation destroy this effect.

cond-mat.mes-hall↗

arXiv2017

In-gap bound states induced by a single nonmagnetic impurity in sign-preserving s-wave superconductors with incipient bands

We have investigated the in-gap bound states (IGBS) induced by a single nonmagnetic impurity in multiband superconductors with incipient bands. Contrary to the naive expectation, we found that even if the superconducting (SC) order parameter is sign-preserving s-wave on the Fermi surfaces, the incipient bands may still affect the appearance and locations of the IGBS, although the gap between the incipient bands and the Fermi level is much larger than the SC gap. Therefore in scanning tunneling microscopy experiments, the IGBS induced by a single nonmagnetic impurity are not the definitive evidences for the sign-changing order parameter on the Fermi surfaces. Our findings have special implications for the experimental determination of the pairing symmetry in the FeSe-based superconductors.

cond-mat.supr-con↗

arXiv2017

Large scale digital prostate pathology image analysis combining feature extraction and deep neural network

Histopathological assessments, including surgical resection and core needle biopsy, are the standard procedures in the diagnosis of the prostate cancer. Current interpretation of the histopathology images includes the determination of the tumor area, Gleason grading, and identification of certain prognosis-critical features. Such a process is not only tedious, but also prune to intra/inter-observe variabilities. Recently, FDA cleared the marketing of the first whole slide imaging system for digital pathology. This opens a new era for the computer aided prostate image analysis and feature extraction based on the digital histopathology images. In this work, we present an analysis pipeline that includes localization of the cancer region, grading, area ratio of different Gleason grades, and cytological/architectural feature extraction. The proposed algorithm combines the human engineered feature extraction as well as those learned by the deep neural network. Moreover, the entire pipeline is implemented to directly operate on the whole slide images produced by the digital scanners and is therefore potentially easy to translate into clinical practices. The algorithm is tested on 368 whole slide images from the TCGA data set and achieves an overall accuracy of 75% in differentiating Gleason 3+4 with 4+3 slides.

cs.CV↗