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Yi Liu

Publications and source records attributed to Yi Liu.

At least 703 records · Page 39Linked to original sources
arXiv2020

Zero-energy bound states in the high-temperature superconductors at the two-dimensional limit

Majorana zero modes (MZMs) that obey the non-Abelian statistics have been intensively investigated for potential applications in topological quantum computing. The prevailing signals in tunneling experiments "fingerprinting" the existence of MZMs are the zero-energy bound states (ZEBSs). However, nearly all of the previously reported ZEBSs showing signatures of the MZMs are observed in difficult-to-fabricate heterostructures at very low temperatures and additionally require applied magnetic field. Here, by using in-situ scanning tunneling spectroscopy, we detect the ZEBSs upon the interstitial Fe adatoms deposited on two different high-temperature superconducting one-unit-cell-thick iron chalcogenides on SrTiO3(001). The spectroscopic results resemble the phenomenological characteristics of the MZMs inside the vortex cores of topological superconductors. Our experimental findings may extend the MZM explorations in connate topological superconductors towards an applicable temperature regime and down to the two-dimensional limit. While a concrete understanding of the observations is lacking, possible explanations involving novel 2D superconducting states with spin-orbit coupling, spontaneous nucleation of anomalous vortices at the magnetic sites, and noncoplanar magnetic ordering may further stimulate theoretical understandings of the scarcely captured ZEBSs in strongly correlated systems with multiband Cooper pairing.

cond-mat.supr-con↗
arXiv2020

Rate-Constrained Shaping Codes for Structured Sources

Shaping codes are used to encode information for use on channels with cost constraints. Applications include data transmission with a power constraint and, more recently, data storage on flash memories with a constraint on memory cell wear. In the latter application, system requirements often impose a rate constraint. In this paper, we study rate-constrained fixed-to-variable length shaping codes for noiseless, memoryless costly channels and general i.i.d. sources. The analysis relies on the theory of word-valued sources. We establish a relationship between the code expansion factor and minimum average symbol cost. We then determine the expansion factor that minimizes the average cost per source symbol (total cost), corresponding to a conventional optimal source code with cost. An equivalence is established between codes minimizing average symbol cost and codes minimizing total cost, and a separation theorem is proved, showing that optimal shaping can be achieved by a concatenation of optimal compression and optimal shaping for a uniform i.i.d. source. Shaping codes often incorporate, either explicitly or implicitly, some form of non-equiprobable signaling. We use our results to further explore the connections between shaping codes and codes that map a sequence of i.i.d. source symbols into an output sequence of symbols that are approximately independent and distributed according to a specified target distribution, such as distribution matching (DM) codes. Optimal DM codes are characterized in terms of a new performance measure - generalized expansion factor (GEF) - motivated by the costly channel perspective. The GEF is used to study DM codes that minimize informational divergence and normalized informational divergence.

cs.IT↗
arXiv2019

MulGAN: Facial Attribute Editing by Exemplar

Recent studies on face attribute editing by exemplars have achieved promising results due to the increasing power of deep convolutional networks and generative adversarial networks. These methods encode attribute-related information in images into the predefined region of the latent feature space by employing a pair of images with opposite attributes as input to train model, the face attribute transfer between the input image and the exemplar can be achieved by exchanging their attribute-related latent feature region. However, they suffer from three limitations: (1) the model must be trained using a pair of images with opposite attributes as input; (2) weak capability of editing multiple attributes by exemplars; (3) poor quality of generating image. Instead of imposing opposite-attribute constraints on the input image in order to make the attribute information of images be encoded in the predefined region of the latent feature space, in this work we directly apply the attribute labels constraint to the predefined region of the latent feature space. Meanwhile, an attribute classification loss is employed to make the model learn to extract the attribute-related information of images into the predefined latent feature region of the corresponding attribute, which enables our method to transfer multiple attributes of the exemplar simultaneously. Besides, a novel model structure is designed to enhance attribute transfer capabilities by exemplars while improve the quality of the generated image. Experiments demonstrate the effectiveness of our model on overcoming the above three limitations by comparing with other methods on the CelebA dataset.

cs.CV↗
arXiv2019

THUEE system description for NIST 2019 SRE CTS Challenge

This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the NIST 2019 speaker recognition evaluation CTS challenge. Six subsystems, including etdnn/ams, ftdnn/as, eftdnn/ams, resnet, multitask and c-vector are developed in this evaluation.

cs.SD↗
arXiv2019

Lasing without population inversion in air

A cavity-free laser in the sky could lead to revolutionary improvements in optical remote sensing for atmospheric science. Abundant in air, nitrogen molecules are prime candidates as an active medium for such a laser. Nitrogen molecules, either neutral or singly ionized, exhibit a significant optical gain in the UV regime when pumped by an intense femtosecond laser. Despite numerous works, the physical origin of this lasing is still not understood. Here we offer a consistent interpretation of all experimental facts by introducing a concept drawn from the field of quantum optics: transient lasing without inversion. Our experiments and simulations identify the crucial role of macroscopic coherence, instead of population inversion, in the nitrogen ions lasing system. Manipulating the macroscopic coherence provides a new level of control over nitrogen air lasing and offers new perspectives for the generation of very short wavelength laser where population inversion is difficult to achieve.

physics.optics↗
arXiv2019

Dominant Dataset Selection Algorithms for Electricity Consumption Time-Series Data Analysis Based on Affine Transformation

The explosive growth of time-series data, the scale of time-series data (TSD) suggests that the scale and capability of many Internet of Things (IoT)-based applications has already been exceeded. Moreover, redundancy persists in TSD due to the correlation between information acquired via different sources. In this paper, we propose a cohort of dominant dataset selection algorithms for electricity consumption time-series data with a focus on discriminating the dominant dataset that is small dataset but capable of representing the kernel information carried by time-series data with an arbitrarily small error rate less than ". Furthermore, we prove that the selection problem of the minimum dominant dataset is an NP-complete problem. The affine transformation model is introduced to define the linear correlation relationship between time-series data objects. Our proposed framework consists of the scanning selection algorithm with O(n3) time complexity and the greedy selection algorithm with O(n4) time complexity, which are respectively proposed to select the dominant dataset based on the linear correlation distance between time-series data objects. The proposed algorithms are evaluated on the real electricity consumption data Harbin city in China. The experimental results show that the proposed algorithms not only reduce the size of the extracted kernel dataset but also ensure the time-series data integrity in terms of accuracy and efficiency.

cs.DB↗
arXiv2019

Angle-resolved photoemission spectroscopy study of crystal electric field in heavy fermion compound CePt2In7

The three-dimensional electronic structure and Ce 4f electrons of the heavy fermion superconductor CePt2In7 is investigated. Angle-resolved photoemission spectroscopy using variable photon energy establishes the existence of quasi-two and three dimensional Fermi surface topologies. Temperature-dependent 4d-4f on-resonance photoemission spectroscopies reveal that heavy quasiparticle bands begin to form at a temperature well above the characteristic (coherence) temperature T*. T* emergence may be closely related to crystal electric field splitting, particularly the low-lying heavy band formed by crystal electric field splitting.

cond-mat.str-el↗
arXiv2019

Dirac surface states in intrinsic magnetic topological insulators EuSn2As2 and MnBi2nTe3n+1

In magnetic topological insulators (TIs), the interplay between magnetic order and nontrivial topology can induce fascinating topological quantum phenomena, such as the quantum anomalous Hall effect, chiral Majorana fermions and axion electrodynamics. Recently, a great deal of attention has been focused on the intrinsic magnetic TIs, where disorder effects can be eliminated to a large extent, which is expected to facilitate the emergence of topological quantum phenomena. In despite of intensive efforts, experimental evidence of the topological surface states (SSs) remains elusive. Here, by combining first-principles calculations and angle-resolved photoemission spectroscopy (ARPES) experiments, we have revealed that EuSn2As2 is an antiferromagnetic TI with observation of Dirac SSs consistent with our prediction. We also observe nearly gapless Dirac SSs in antiferromagnetic TIs MnBi2nTe3n+1 (n = 1 and 2), which were absent in previous ARPES results. These results provide clear evidence for nontrivial topology of these intrinsic magnetic TIs. Furthermore, we find that the topological SSs show no observable changes across the magnetic transition within the experimental resolution, indicating that the magnetic order has quite small effect on the topological SSs, which can be attributed to weak hybridization between the localized magnetic moments, from either 4f or 3d orbitals, and the topological electronic states. This provides insights for further research that the correlations between magnetism and topological states need to be strengthened to induce larger gaps in the topological SSs, which will facilitate the realization of topological quantum phenomena at higher temperatures.

cond-mat.mtrl-sci↗
arXiv2019

PPGAN: Privacy-preserving Generative Adversarial Network

Generative Adversarial Network (GAN) and its variants serve as a perfect representation of the data generation model, providing researchers with a large amount of high-quality generated data. They illustrate a promising direction for research with limited data availability. When GAN learns the semantic-rich data distribution from a dataset, the density of the generated distribution tends to concentrate on the training data. Due to the gradient parameters of the deep neural network contain the data distribution of the training samples, they can easily remember the training samples. When GAN is applied to private or sensitive data, for instance, patient medical records, as private information may be leakage. To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure. Besides, we introduced the Moments Accountant strategy in the PPGAN training process to improve the stability and compatibility of the model by controlling privacy loss. We also give a mathematical proof of the differential privacy discriminator. Through extensive case studies of the benchmark datasets, we demonstrate that PPGAN can generate high-quality synthetic data while retaining the required data available under a reasonable privacy budget.

cs.LG↗
arXiv2019

Global Pixel Transformers for Virtual Staining of Microscopy Images

Visualizing the details of different cellular structures is of great importance to elucidate cellular functions. However, it is challenging to obtain high quality images of different structures directly due to complex cellular environments. Fluorescence staining is a popular technique to label different structures but has several drawbacks. In particular, label staining is time consuming and may affect cell morphology, and simultaneous labels are inherently limited. This raises the need of building computational models to learn relationships between unlabeled microscopy images and labeled fluorescence images, and to infer fluorescence labels of other microscopy images excluding the physical staining process. We propose to develop a novel deep model for virtual staining of unlabeled microscopy images. We first propose a novel network layer, known as the global pixel transformer layer, that fuses global information from inputs effectively. The proposed global pixel transformer layer can generate outputs with arbitrary dimensions, and can be employed for all the regular, down-sampling, and up-sampling operators. We then incorporate our proposed global pixel transformer layers and dense blocks to build an U-Net like network. We believe such a design can promote feature reusing between layers. In addition, we propose a multi-scale input strategy to encourage networks to capture features at different scales. We conduct evaluations across various fluorescence image prediction tasks to demonstrate the effectiveness of our approach. Both quantitative and qualitative results show that our method outperforms the state-of-the-art approach significantly. It is also shown that our proposed global pixel transformer layer is useful to improve the fluorescence image prediction results.

eess.IV↗
arXiv2019

Finiteness of nonzero degree maps between three-manifolds

In this paper, it is shown that every orientable closed 3-manifold maps with nonzero degree onto at most finitely many homeomorphically distinct irreducible non-geometric orientable closed 3-manifolds. Moreover, given any nonzero integer, as a mapping degree up to sign, every orientable closed 3-manifold maps with that degree onto only finitely many homeomorphically distinct orientable closed 3-manifolds.

math.GT↗
arXiv2019

Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model

In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries. To tackle this problem, we propose a dual task constrained deep Siamese convolutional network (DTCDSCN) model, which contains three sub-networks: a change detection network and two semantic segmentation networks. DTCDSCN can accomplish both change detection and semantic segmentation at the same time, which can help to learn more discriminative object-level features and obtain a complete change detection map. Furthermore, we introduce a dual attention module (DAM) to exploit the interdependencies between channels and spatial positions, which improves the feature representation. We also improve the focal loss function to suppress the sample imbalance problem. The experimental results obtained with the WHU building dataset show that the proposed method is effective for building change detection and achieves a state-of-the-art performance in terms of four metrics: precision, recall, F1-score, and intersection over union.

cs.CV↗
arXiv2019

Open DNN Box by Power Side-Channel Attack

Deep neural networks are becoming popular and important assets of many AI companies. However, recent studies indicate that they are also vulnerable to adversarial attacks. Adversarial attacks can be either white-box or black-box. The white-box attacks assume full knowledge of the models while the black-box ones assume none. In general, revealing more internal information can enable much more powerful and efficient attacks. However, in most real-world applications, the internal information of embedded AI devices is unavailable, i.e., they are black-box. Therefore, in this work, we propose a side-channel information based technique to reveal the internal information of black-box models. Specifically, we have made the following contributions: (1) we are the first to use side-channel information to reveal internal network architecture in embedded devices; (2) we are the first to construct models for internal parameter estimation; and (3) we validate our methods on real-world devices and applications. The experimental results show that our method can achieve 96.50\% accuracy on average. Such results suggest that we should pay strong attention to the security problem of many AI applications, and further propose corresponding defensive strategies in the future.

cs.CR↗
arXiv2019

Temperature dependence of side-jump spin Hall conductivity

In the conventional paradigm of the spin Hall effect, the side-jump conductivity due to electron-phonon scattering is regarded to be temperature independent. To the contrary, we draw the distinction that, while this side-jump conductivity is temperature independent in the classical equipartition regime where the longitudinal resistivity is linear in temperature, it is temperature dependent below the equipartition regime. The mechanism resulting in this temperature dependence differs from the familiar one of the longitudinal resistivity. In the concrete example of Pt, we show that the change of the spin Hall conductivity with temperature can be as high as 50%. Experimentally accessible high-purity Pt is proposed to be suitable for observing this prominent variation below 80 K.

cond-mat.mes-hall↗
arXiv2019

Large Fermi Surface Expansion through Anisotropic c-f Mixing in the Semimetallic Kondo Lattice System CeBi

Using angle-resolved photoemission spectroscopy (ARPES) and resonant ARPES, we report evidence of strong anisotropic conduction-f electron mixing (c-f mixing) in CeBi by observing a largely expanded Ce-5d pocket at low temperature, with no change in the Bi-6p bands. The Fermi surface (FS) expansion is accompanied by a pronounced spectral weight transfer from the local 4f 0 peak of Ce (corresponding to Ce3+) to the itinerant conduction bands near the Fermi level. Careful analysis suggests that the observed large FS change (with a volume expansion of the electron pocket up to 40%) can most naturally be explained by a small valence change (~ 1%) of Ce, which coexists with a very weak Kondo screening. Our work therefore provides evidence for a FS change driven by real charge fluctuations deep in the Kondo limit, which is made possible by the low carrier density.

cond-mat.str-el↗
arXiv2019

MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks

As the development of deep neural networks, 3D object recognition is becoming increasingly popular in computer vision community. Many multi-view based methods are proposed to improve the category recognition accuracy. These approaches mainly rely on multi-view images which are rendered with the whole circumference. In real-world applications, however, 3D objects are mostly observed from partial viewpoints in a less range. Therefore, we propose a multi-view based 3D convolutional neural network, which takes only part of contiguous multi-view images as input and can still maintain high accuracy. Moreover, our model takes these view images as a joint variable to better learn spatially correlated features using 3D convolution and 3D max-pooling layers. Experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images which are captured from partial angles with less range. The results on 3D rotated real image dataset MIRO further demonstrate that MV-C3D is more adaptable in real-world scenarios. The classification accuracy can be further improved with the increasing number of view images.

cs.CV↗
arXiv2019

Latent Class Model with Application to Speaker Diarization

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. 1) Adding neighbor windows to extract more speaker information for each short segment. 2) Using a hidden Markov model to avoid frequent speaker change points. 3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance.

eess.AS↗