arXiv subjects

Yu Pan

Publications and source records attributed to Yu Pan.

At least 145 records · Page 8Linked to original sources

arXiv2021

Quantum Language Model with Entanglement Embedding for Question Answering

Quantum Language Models (QLMs) in which words are modelled as quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability. Nevertheless, in the current literature word sequences are basically modelled as a classical mixture of word states, which cannot fully exploit the potential of a quantum probabilistic description. A full quantum model is yet to be developed to explicitly capture the non-classical correlations within the word sequences. We propose a neural network model with a novel Entanglement Embedding (EE) module, whose function is to transform the word sequences into entangled pure states of many-body quantum systems. Strong quantum entanglement, which is the central concept of quantum information and an indication of parallelized correlations among the words, is observed within the word sequences. Numerical experiments show that the proposed QLM with EE (QLM-EE) achieves superior performance compared with the classical deep neural network models and other QLMs on Question Answering (QA) datasets. In addition, the post-hoc interpretability of the model can be improved by quantizing the degree of entanglement among the words.

cs.CL↗

arXiv2021

Making Adversarial Examples More Transferable and Indistinguishable

Fast gradient sign attack series are popular methods that are used to generate adversarial examples. However, most of the approaches based on fast gradient sign attack series cannot balance the indistinguishability and transferability due to the limitations of the basic sign structure. To address this problem, we propose a method, called Adam Iterative Fast Gradient Tanh Method (AI-FGTM), to generate indistinguishable adversarial examples with high transferability. Besides, smaller kernels and dynamic step size are also applied to generate adversarial examples for further increasing the attack success rates. Extensive experiments on an ImageNet-compatible dataset show that our method generates more indistinguishable adversarial examples and achieves higher attack success rates without extra running time and resource. Our best transfer-based attack NI-TI-DI-AITM can fool six classic defense models with an average success rate of 89.3% and three advanced defense models with an average success rate of 82.7%, which are higher than the state-of-the-art gradient-based attacks. Additionally, our method can also reduce nearly 20% mean perturbation. We expect that our method will serve as a new baseline for generating adversarial examples with better transferability and indistinguishability.

cs.CV↗

arXiv2021

Augmented Legendrian cobordism in $J^1S^1$

We consider Legendrian links and tangles in $J^1S^1$ and $J^1[0,1]$ equipped with Morse complex families over a field $\mathbb{F}$ and classify them up to Legendrian cobordism. When the coefficient field is $\mathbb{F}_2$ this provides a cobordism classification for Legendrians equipped with augmentations of the Legendrian contact homology DG-algebras. A complete set of invariants, for which arbitrary values may be obtained, is provided by the fiber cohomology, a graded monodromy matrix, and a mod $2$ spin number. We apply the classification to construct augmented Legendrian surfaces in $J^1M$ with $\dim M = 2$ realizing any prescribed monodromy representation, $Φ:π_1(M,x_0) \rightarrow \mathit{GL}(\mathbf{n}, \mathbb{F})$.

math.SG↗

arXiv2021

TedNet: A Pytorch Toolkit for Tensor Decomposition Networks

Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures. To give more researchers a flexible way to exploit TDNs, we present a Pytorch toolkit named TedNet. TedNet implements 5 kinds of tensor decomposition(i.e., CANDECOMP/PARAFAC (CP), Block-Term Tucker (BTT), Tucker-2, Tensor Train (TT) and Tensor Ring (TR) on traditional deep neural layers, the convolutional layer and the fully-connected layer. By utilizing the basic layers, it is simple to construct a variety of TDNs. TedNet is available at https://github.com/tnbar/tednet.

cs.LG↗

arXiv2021

Anomalous thermoelectric effects and quantum oscillations in the kagome metal CsV$_3$Sb$_5$

The kagome metal compounds $A$V$_3$Sb$_5$ ($A$ = K, Rb, and Cs) feature a wealth of phenomena including nontrivial band topology, charge density wave (CDW), and superconductivity. One intriguing property is the time-reversal symmetry breaking in the CDW state without local moments, which leads to anomalous transport responses. Here, we report the investigation of magneto-thermoelectric effects on high-quality CsV$_3$Sb$_5$ single crystals. A large anomalous Nernst effect is observed at temperatures below 30 K. Multiple Fermi surfaces with small effective masses are revealed by quantum oscillations in Nernst and Seebeck signals under high magnetic field. Furthermore, we find an unknown frequency, and attribute it to the magnetic breakdown across two smaller Fermi surfaces. A gap around 20 meV can be resolved from the breakdown threshold field, which we propose to be introduced by the CDW. These results shed new light on the CDW-related phenomena, particularly in $A$V$_3$Sb$_5$ compounds.

cond-mat.mtrl-sci↗

arXiv2021

Detecting quantum entanglement with unsupervised learning

Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features, especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average.Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.

quant-ph↗

arXiv2021

Giant anomalous Nernst signal in the antiferromagnet YbMnBi2

Searching for a high anomalous Nernst effect (ANE) is crucial for thermoelectric energy conversion applications because the associated unique transverse geometry facilitates module fabrication. Topological ferromagnets with large Berry curvatures show high ANEs; however, they face drawbacks such as strong magnetic disturbances and low mobility due to high magnetization. Herein, we demonstrate that YbMnBi2, a canted antiferromagnet, has a large ANE conductivity of ~10 Am-1K-1 that surpasses the common high values (i.e. 3-5 Am-1K-1) observed so far in ferromagnets. The canted spin structure of Mn guarantees a nonzero Berry curvature but generates only a weak magnetization three orders of magnitude lower than that of general ferromagnets. The heavy Bi with a large spin-orbit coupling enables a high ANE and low thermal conductivity, whereas its highly dispersive px/y orbitals ensure low resistivity. The high anomalous transverse thermoelectric performance and extremely small magnetization makes YbMnBi2 an excellent candidate for transverse thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2021

Fast query-by-example speech search using separable model

Traditional Query-by-Example (QbE) speech search approaches usually use methods based on frame-level features, while state-of-the-art approaches tend to use models based on acoustic word embeddings (AWEs) to transform variable length audio signals into fixed length feature vector representations. However, these approaches cannot meet the requirements of the search quality as well as speed at the same time. In this paper, we propose a novel fast QbE speech search method based on separable models to fix this problem. First, a QbE speech search training framework is introduced. Second, we design a novel model inference scheme based on RepVGG which can efficiently improve the QbE search quality. Third, we modify and improve our QbE speech search model according to the proposed model inference scheme. Experiments on keywords dataset shows that our proposed method can improve the GPU Real-time Factor (RTF) from 1/150 to 1/2300 by just applying separable model scheme and outperforms other state-of-the-art methods.

eess.AS↗

arXiv2021

A new way to test the WIMP dark matter models

In this paper, we investigate the possibility of testing the weakly interacting massive particle (WIMP) dark matter (DM) models by applying the simplest phenomenological model which introduces an interaction term between dark energy (DE) and WIMP DM, i.e., $Q = 3γ_{DM} Hρ_{DM}$. In general, the coupling strength $γ_{DE}$ is close to $0$ as the interaction between DE and WIMP DM is very weak, thus the effect of $γ_ {DE}$ on the evolution of $Y$ associated with DM energy density can be safely neglected. Meanwhile, our numerical calculation also indicates that $x_f\approx20$ is associated with DM freeze-out temperature, which is the same as the vanishing interaction scenario. As for DM relic density, it will be magnified by $\frac{2-3γ_{DM}}{2}[{2πg_* m_{DM}^3}/{(45 s_0 x_f^3})]^{γ_{DM}}$ times, which provides a new way to test WIMP DM models. As an example, we analyze the case in which WIMP DM is a scalar DM. (SGL+SNe+Hz) and (CMB+BAO+SNe) cosmological observations will give $γ_{DM}=0.134^{+0.17}_{-0.069}$ and $γ_{DM}=-0.0008\pm0.0016$, respectively. After further considering the constraints from DM direct detection experiment, DM indirect detection experiment, and DM relic density, we find that the allowed parameter space of the scalar DM model will be completely excluded for the former cosmological observations, while it will increase for the latter ones. Those two cosmological observations lead to an almost paradoxical conclusion. Therefore, one could expect more stringent constraints on the WMIP DM models, with the accumulation of more accurate cosmological observations in the near future.

astro-ph.CO↗

arXiv2021

Large topological Hall effect in an easy-cone ferromagnet (Cr0.9B0.1)Te

The Berry phase understanding of electronic properties has attracted special interest in condensed matter physics, leading to phenomena such as the anomalous Hall effect and the topological Hall effect. A non-vanishing Berry phase, induced in momentum space by the band structure or in real space by a non-coplanar spin structure, is the origin of both effects. Here, we report a sign conversion of the anomalous Hall effect and a large topological Hall effect in (Cr0.9B0.1)Te single crystals. The spin reorientation from an easy-axis structure at high temperature to an easy-cone structure below 140 K leads to conversion of the Berry curvature, which influences both, anomalous and topological, Hall effects in the presence of an applied magnetic field and current. We compare and summarize the topological Hall effect in four categories with different mechanisms and have a discussion into the possible artificial fake effect of topological Hall effect in polycrystalline samples, which provides a deep understanding of the relation between spin structure and Hall properties.

cond-mat.mtrl-sci↗

arXiv2021

Heuristic Rank Selection with Progressively Searching Tensor Ring Network

Recently, Tensor Ring Networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named Progressively Searching Tensor Ring Network Search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance.

cs.CV↗

arXiv2021

AFINet: Attentive Feature Integration Networks for Image Classification

Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Recent advanced models in CNNs, such as ResNets, mainly focus on the skip connection to avoid gradient vanishing. DenseNet designs suggest creating additional bypasses to transfer features as an alternative strategy in network design. In this paper, we design Attentive Feature Integration (AFI) modules, which are widely applicable to most recent network architectures, leading to new architectures named AFI-Nets. AFI-Nets explicitly model the correlations among different levels of features and selectively transfer features with a little overhead.AFI-ResNet-152 obtains a 1.24% relative improvement on the ImageNet dataset while decreases the FLOPs by about 10% and the number of parameters by about 9.2% compared to ResNet-152.

cs.CV↗

arXiv2021

Testing f(R) gravity with the simulated data of gravitational waves from the Einstein Telescope

In this paper we analyze the implications of gravitational waves (GWs) as standard sirens on the modified gravity models by using the third-generation gravitational wave detector, i.e., the Einstein Telescope. Two viable models in $f(R)$ theories within the Palatini formalism are considered in our analysis ($f_{1}(\mathcal{R})=\mathcal{R}-\fracβ{\mathcal{R}^{n}}$ and $f_{2}(\mathcal{R})=\mathcal{R}+α\ln{\mathcal{R}}-β$), with the combination of simulated GW data and the latest electromagnetic (EM) observational data (including the recently released Pantheon type Ia supernovae sample, the cosmic chronometer data, and baryon acoustic oscillation distance measurements). Our analysis reveals that the standard sirens GWs, which provide an independent and complementary alternative to current experiments, could effectively eliminate the degeneracies among parameters in the two modified gravity models. In addition, we thoroughly investigate the nature of geometrical dark energy in the modified gravity theories with the assistance of $Om(z)$ and statefinder diagnostic analysis. The present analysis makes it clear-cut that the simplest cosmological constant model is still the most preferred by the current data. However, the combination of future naturally improved GW data most recent EM observations will reveal the consistency or acknowledge the tension between the $Λ$CDM model and modified gravity theories.

astro-ph.CO↗

arXiv2021

Spectrum Attention Mechanism for Time Series Classification

Time series classification(TSC) has always been an important and challenging research task. With the wide application of deep learning, more and more researchers use deep learning models to solve TSC problems. Since time series always contains a lot of noise, which has a negative impact on network training, people usually filter the original data before training the network. The existing schemes are to treat the filtering and training as two stages, and the design of the filter requires expert experience, which increases the design difficulty of the algorithm and is not universal. We note that the essence of filtering is to filter out the insignificant frequency components and highlight the important ones, which is similar to the attention mechanism. In this paper, we propose an attention mechanism that acts on spectrum (SAM). The network can assign appropriate weights to each frequency component to achieve adaptive filtering. We use L1 regularization to further enhance the frequency screening capability of SAM. We also propose a segmented-SAM (SSAM) to avoid the loss of time domain information caused by using the spectrum of the whole sequence. In which, a tumbling window is introduced to segment the original data. Then SAM is applied to each segment to generate new features. We propose a heuristic strategy to search for the appropriate number of segments. Experimental results show that SSAM can produce better feature representations, make the network converge faster, and improve the robustness and classification accuracy.

cs.LG↗

arXiv2021

RegNet: Self-Regulated Network for Image Classification

The ResNet and its variants have achieved remarkable successes in various computer vision tasks. Despite its success in making gradient flow through building blocks, the simple shortcut connection mechanism limits the ability of re-exploring new potentially complementary features due to the additive function. To address this issue, in this paper, we propose to introduce a regulator module as a memory mechanism to extract complementary features, which are further fed to the ResNet. In particular, the regulator module is composed of convolutional RNNs (e.g., Convolutional LSTMs or Convolutional GRUs), which are shown to be good at extracting Spatio-temporal information. We named the new regulated networks as RegNet. The regulator module can be easily implemented and appended to any ResNet architecture. We also apply the regulator module for improving the Squeeze-and-Excitation ResNet to show the generalization ability of our method. Experimental results on three image classification datasets have demonstrated the promising performance of the proposed architecture compared with the standard ResNet, SE-ResNet, and other state-of-the-art architectures.

eess.IV↗

arXiv2020

Constructions of Lagrangian cobordisms

Lagrangian cobordisms between Legendrian knots arise in Symplectic Field Theory and impose an interesting and not well-understood relation on Legendrian knots. There are some known "elementary" building blocks for Lagrangian cobordisms that are smoothly the attachment of $0$- and $1$-handles. An important question is whether every pair of non-empty Legendrians that are related by a connected Lagrangian cobordism can be related by a ribbon Lagrangian cobordism, in particular one that is "decomposable" into a composition of these elementary building blocks. We will describe these and other combinatorial building blocks as well as some geometric methods, involving the theory of satellites, to construct Lagrangian cobordisms. We will then survey some known results, derived through Heegaard Floer Homology and contact surgery, that may provide a pathway to proving the existence of nondecomposable (nonribbon) Lagrangian cobordisms.

math.SG↗

arXiv2020

Phase transition and entropic force of de Sitter black hole in massive gravity

It is well known that de Sitter(dS) black holes generally have a black hole horizon and a cosmological horizon, both of which have Hawking radiation. But the radiation temperature of the two horizons is generally different, so dS black holes do not meet the requirements of thermal equilibrium stability, which brings certain difficulties to the study of the thermodynamic characteristics of black holes. In this paper, dS black hole is regarded as a thermodynamic system, and the effective thermodynamic quantities of the system are obtained. The influence of various state parameters on the effective thermodynamic quantities in the massive gravity space-time is discussed. The condition of the phase transition of the de Sitter black hole in massive gravity space-time is given. We consider that the total entropy of the dS black hole is the sum of the corresponding entropy of the two horizons plus an extra term from the correlation of the two horizons. By comparing the entropic force of interaction between black hole horizon and the cosmological horizon with Lennard-Jones force between two particles, we find that the change rule of entropic force between the two system is surprisingly the same. The research will help us to explore the real reason of accelerating expansion of the universe.

hep-th↗

arXiv2020

New highly-anisotropic Rh-based Heusler compound for magnetic recording

The development of high-density magnetic recording media is limited by the superparamagnetism in very small ferromagnetic crystals. Hard magnetic materials with strong perpendicular anisotropy offer stability and high recording density. To overcome the difficulty of writing media with a large coercivity, heat assisted magnetic recording (HAMR) has been developed, rapidly heating the media to the Curie temperature Tc before writing, followed by rapid cooling. Requirements are a suitable Tc, coupled with anisotropic thermal conductivity and hard magnetic properties. Here we introduce Rh2CoSb as a new hard magnet with potential for thin film magnetic recording. A magnetocrystalline anisotropy of 3.6 MJm-3 is combined with a saturation magnetization of μ0Ms = 0.52 T at 2 K (2.2 MJm-3 and 0.44 T at room-temperature). The magnetic hardness parameter of 3.7 at room temperature is the highest observed for any rare-earth free hard magnet. The anisotropy is related to an unquenched orbital moment of 0.42 μB on Co, which is hybridized with neighbouring Rh atoms with a large spin-orbit interaction. Moreover, the pronounced temperature-dependence of the anisotropy that follows from its Tc of 450 K, together with a high thermal conductivity of 20 Wm-1K-1, makes Rh2CoSb a candidate for development for heat assisted writing with a recording density in excess of 10 Tb/in2.

cond-mat.mtrl-sci↗