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Yu Liu

Publications and source records attributed to Yu Liu.

At least 973 records · Page 54Linked to original sources

Quantum sensing with a single-qubit pseudo-Hermitian system

Quantum sensing exploits fundamental features of quantum system to achieve highly efficient measurement of physical quantities. Here, we propose a strategy to realize a single-qubit pseudo-Hermitian sensor from a dilated two-qubit Hermitian system. The pseudo-Hermitian sensor exhibits divergent susceptibility in dynamical evolution that does not necessarily involve exceptional point. We demonstrate its potential advantages to overcome noises that cannot be averaged out by repetitive measurements. The proposal is feasible with the state-of-art experimental capability in a variety of qubit systems, and represents a step towards the application of non-Hermitian physics in quantum sensing.

quant-ph↗

A Coding-free Software Framework of Developing Web Data Management Systems

More and more enterprises recently intend to deploy data management systems in the cloud. Due to the professionalism of software development, it has still been difficult for non-programmers to develop this kind of systems, even a small one. However, the development of SaaS brings forth the more feasibility of coding-free software development than before. Based on the SaaS architecture, this paper presents a set of theory and method for coding-free construction of a data management system, on which our contributions involve in a practical application platform, a set of construction method and a set of interface on data exchange. By abstracting the common features of data management systems, we design a universal web platform to quickly generate and publish customized system instances. Moreover, we propose a kind of method to develop a data management system using a specific requirements table in spreadsheet. The corresponding platform maps the requirements table into a system instance through parsing the table model and implementing the objective system in the running stage. Finally, we implement the proposed framework and deploy it on web. The empirical result demonstrates the feasibility and availability of the coding-free method in developing web data management systems.

cs.SE↗

Multi-layered Kelvin-Helmholtz Instability in the Solar Corona

The Kelvin-Helmholtz (KH) instability is commonly found in many astrophysical, laboratory, and space plasmas. It could mix plasma components of different properties and convert dynamic fluid energy from large scale structure to smaller ones. In this study, we combined the ground-based New Vacuum Solar Telescope (NVST) and the Solar Dynamic Observatories (SDO) / Atmospheric Imaging Assembly (AIA) to observe the plasma dynamics associated with active region 12673 on 09 September 2017. In this multi-temperature view, we identified three adjacent layers of plasma flowing at different speeds, and detected KH instabilities at their interfaces. We could unambiguously track a typical KH vortex and measure its motion. We found that the speed of this vortex suddenly tripled at a certain stage. This acceleration was synchronized with the enhancements in emission measure and average intensity of the 193 Å data. We interpret this as evidence that KH instability triggers plasma heating. The intriguing feature in this event is that the KH instability observed in the NVST channel was nearly complementary to that in the AIA 193 Å. Such a multi-thermal energy exchange process is easily overlooked in previous studies, as the cold plasma component is usually not visible in the extreme ultraviolet channels that are only sensitive to high temperature plasma emissions. Our finding indicates that embedded cold layers could interact with hot plasma as invisible matters. We speculate that this process could occur at a variety of length scales and could contribute to plasma heating.

astro-ph.SR↗

Gradient Information Guided Deraining with A Novel Network and Adversarial Training

In recent years, deep learning based methods have made significant progress in rain-removing. However, the existing methods usually do not have good generalization ability, which leads to the fact that almost all of existing methods have a satisfied performance on removing a specific type of rain streaks, but may have a relatively poor performance on other types of rain streaks. In this paper, aiming at removing multiple types of rain streaks from single images, we propose a novel deraining framework (GRASPP-GAN), which has better generalization capacity. Specifically, a modified ResNet-18 which extracts the deep features of rainy images and a revised ASPP structure which adapts to the various shapes and sizes of rain streaks are composed together to form the backbone of our deraining network. Taking the more prominent characteristics of rain streaks in the gradient domain into consideration, a gradient loss is introduced to help to supervise our deraining training process, for which, a Sobel convolution layer is built to extract the gradient information flexibly. To further boost the performance, an adversarial learning scheme is employed for the first time to train the proposed network. Extensive experiments on both real-world and synthetic datasets demonstrate that our method outperforms the state-of-the-art deraining methods quantitatively and qualitatively. In addition, without any modifications, our proposed framework also achieves good visual performance on dehazing.

cs.CV↗

Semiconductor - Ferromagnetic Insulator - Superconductor Nanowires: Stray Field and Exchange Field

Nanowires can serve as flexible substrates for hybrid epitaxial growth on selected facets, allowing for design of heterostructures with complex material combinations and geometries. In this work we report on hybrid epitaxy of semiconductor - ferromagnetic insulator - superconductor (InAs/EuS/Al) nanowire heterostructures. We study the crystal growth and complex epitaxial matching of wurtzite InAs / rock-salt EuS interfaces as well as rock-salt EuS / face-centered cubic Al interfaces. Because of the magnetic anisotropy originating from the nanowire shape, the magnetic structure of the EuS phase are easily tuned into single magnetic domains. This effect efficiently ejects the stray field lines along the nanowires. With tunnel spectroscopy measurements of the density of states, we show the material has a hard induced superconducting gap, and magnetic hysteretic evolution which indicates that the magnetic exchange fields are not negligible. These hybrid nanowires fulfil key material requirements for serving as a platform for spin-based quantum applications, such as scalable topological quantum computing.

quant-ph↗

How to improve the interpretability of kernel learning

In recent years, machine learning researchers have focused on methods to construct flexible and interpretable prediction models. However, an interpretability evaluation, a relationship between generalization performance and an interpretability of the model and a method for improving the interpretability have to be considered. In this paper, a quantitative index of the interpretability is proposed and its rationality is proved, and equilibrium problem between the interpretability and the generalization performance is analyzed. Probability upper bound of the sum of the two performances is analyzed. For traditional supervised kernel machine learning problem, a universal learning framework is put forward to solve the equilibrium problem between the two performances. The condition for global optimal solution based on the framework is deduced. The learning framework is applied to the least-squares support vector machine and is evaluated by some experiments.

cs.LG↗

Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation

This paper tackles the problem of video object segmentation. We are specifically concerned with the task of segmenting all pixels of a target object in all frames, given the annotation mask in the first frame. Even when such annotation is available this remains a challenging problem because of the changing appearance and shape of the object over time. In this paper, we tackle this task by formulating it as a meta-learning problem, where the base learner grasping the semantic scene understanding for a general type of objects, and the meta learner quickly adapting the appearance of the target object with a few examples. Our proposed meta-learning method uses a closed form optimizer, the so-called "ridge regression", which has been shown to be conducive for fast and better training convergence. Moreover, we propose a mechanism, named "block splitting", to further speed up the training process as well as to reduce the number of learning parameters. In comparison with the-state-of-the art methods, our proposed framework achieves significant boost up in processing speed, while having very competitive performance compared to the best performing methods on the widely used datasets.

cs.CV↗

Blind Channel Estimation and Data Detection with Unknown Modulation and Coding Scheme

This paper investigates a complete blind receiver approach in an unknown multipath fading channel, which has multiple tasks including blind channel estimation, noise power estimation, modulation classification, channel coding recognition, and data detection. Each of these tasks has been sufficiently studied in the literature. However, to the best of our knowledge, this overall problem has not been investigated previously. This paper is the first attempt to address this overall problem jointly. We propose a complete blind receiver approach that jointly estimates the unknown channel state information and noise power, recognizes the unknown modulation and coding scheme, detects the data of interest, and thus named BERD receiver. Another merit of the proposed BERD receiver is that it can be implemented for both a single receiver and multiple receivers, which ensures successful estimation, recognition, and detection for such an extremely difficult problem. In addition, numerical results show the performance of the proposed receiver in three folds: a) the BERD receiver outperforms the linear minimum mean squared error (LMMSE) pilot-based channel estimator by over 3.5 dB at the MSE of 0.01; b) the correct modulation/coding recognition performance of the BERD receiver is within 0.3 dB as close to the recognition benchmark when the perfect channel state information (CSI) is available; c) the BERD receiver is within 0.5 dB at the bit error rate of 0.001 compared to the benchmark when the modulation, the channel coding, and the CSI are perfectly known. Finally, the BERD receiver finds many applications in both civilian and military scenarios, such as the interference cancelation in spectrum sharing, real-time signal interception, and processing in electronic warfare operations, automatic recognition of a detect signal in software-defined radio.

cs.IT↗

Stable halogen 2D Materials: the case of iodine and astatine

Two-dimensional (2D) materials have wide applications towards electronic devices, energy storages, and catalysis, et al. So far, most of the pure element 2D materials are composed of group IIIA,IVA, and VA elements. Beyond the scope, the orbit hybrid configuration becomes a key fact to influence 2D structure stably. Here we show a sp2d3 hybridization in the outmost electrons with O-shell for Iodine and P-shell for astatine element, builds up triangle configuration (beta-type) to form 2D structures beta-iodiene and beta-astatiene. Each atom is connected by pi bonds, and surrounded by 6 atoms. The pi bonds become possible, and band gap approaches zero because of interaction of unpaired single electron to each atom, depending on reducing bond length. By applying compression strain or spin orbit coupling (SOC), the Dirac points or topological nontrivial points can be available in the beta-iodiene and beta-astatiene. Our discovery has paved a new way to construction of 2D materials.

cond-mat.mtrl-sci↗

G4.8+6.2, a possible kilonova remnant?

Kilonova explosions typically release $\sim 10^{50-51}$ erg in kinetic energy, which is sufficient to constitute a kilonova remnant (KNR); however, it has not yet been confirmed. In this work, we investigate the probable association between G4.8+6.2 and the guest star of AD 1163, which is recorded by the Korea ancient astronomers. Although the evidence available is insufficient to draw a definite conclusion, it is at least theoretically self-consistent that the guest star of AD 1163 was a historical kilonova associated with G4.8+6.2, considering the possible short visible timescale of AD 1163, the relatively high Galactic latitude of G4.8+6.2, and that G4.8+6.2 is spatially coincident with the guest star of AD 1163. Further observation of G4.8+6.2 is needed to test our hypothesis. If our interpretation is correct, our results indicate that young KNRs should have a large diameter and low surface brightness, unlike other young supernova remnants.

astro-ph.HE↗

Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators using Time Compression Supporting Multiple Spike Codes

Spiking neural networks (SNNs) are the third generation of neural networks and can explore both rate and temporal coding for energy-efficient event-driven computation. However, the decision accuracy of existing SNN designs is contingent upon processing a large number of spikes over a long period. Nevertheless, the switching power of SNN hardware accelerators is proportional to the number of spikes processed while the length of spike trains limits throughput and static power efficiency. This paper presents the first study on developing temporal compression to significantly boost throughput and reduce energy dissipation of digital hardware SNN accelerators while being applicable to multiple spike codes. The proposed compression architectures consist of low-cost input spike compression units, novel input-and-output-weighted spiking neurons, and reconfigurable time constant scaling to support large and flexible time compression ratios. Our compression architectures can be transparently applied to any given pre-designed SNNs employing either rate or temporal codes while incurring minimal modification of the neural models, learning algorithms, and hardware design. Using spiking speech and image recognition datasets, we demonstrate the feasibility of supporting large time compression ratios of up to 16x, delivering up to 15.93x, 13.88x, and 86.21x improvements in throughput, energy dissipation, the tradeoffs between hardware area, runtime, energy, and classification accuracy, respectively based on different spike codes on a Xilinx Zynq-7000 FPGA. These results are achieved while incurring little extra hardware overhead.

cs.NE↗

Towards Flops-constrained Face Recognition

Large scale face recognition is challenging especially when the computational budget is limited. Given a \textit{flops} upper bound, the key is to find the optimal neural network architecture and optimization method. In this article, we briefly introduce the solutions of team 'trojans' for the ICCV19 - Lightweight Face Recognition Challenge~\cite{lfr}. The challenge requires each submission to be one single model with the computational budget no higher than 30 GFlops. We introduce a searched network architecture `Efficient PolyFace' based on the Flops constraint, a novel loss function `ArcNegFace', a novel frame aggregation method `QAN++', together with a bag of useful tricks in our implementation (augmentations, regular face, label smoothing, anchor finetuning, etc.). Our basic model, `Efficient PolyFace', takes 28.25 Gflops for the `deepglint-large' image-based track, and the `PolyFace+QAN++' solution takes 24.12 Gflops for the `iQiyi-large' video-based track. These two solutions achieve 94.198\% @ 1e-8 and 72.981\% @ 1e-4 in the two tracks respectively, which are the state-of-the-art results.

cs.CV↗

Anisotropic magnetocaloric effect in Fe$_{3-x}$GeTe$_2$

We present a comprehensive study on anisotropic magnetocaloric porperties of the van der Waals weak-itinerant ferromagnet Fe$_{3-x}$GeTe$_2$ that features gate-tunable room-temperature ferromagnetism in few-layer device. Intrinsic magnetocrystalline anisotropy is observed to be temperature-dependent and most likely favors the long-range magnetic order in thin Fe$_{3-x}$GeTe$_2$ crsytal. The magnetic entropy change $-ΔS_M$ also reveals an anisotropic characteristic between $H // ab$ and $H // c$, which could be well scaled into a universal curve. The peak value $-ΔS_M^{max}$ of 1.20 J kg$^{-1}$ K$^{-1}$ and the corresponding adiabatic temperature change $ΔT_{ad}$ of 0.66 K are deduced from heat capacity with out-of-plane field change of 5 T. By fitting of the field-dependent parameters of $-ΔS_M^{max}$ and the relative cooling power RCP, it gives $-ΔS_M^{max} \propto H^n$ with $n = 0.603(6)$ and $RCP \propto H^m$ with $m = 1.20(1)$ when $H // c$. Given the high and tunable $T_c$, Fe$_{3-x}$GeTe$_2$ crystals are of interest for fabricating the heterostructure-based spintronics device.

cond-mat.str-el↗

Thickness-dependent magnetic order in CrI$_3$ single crystals

Two-dimensional (2D) materials with intrinsic ferromagnetism provide unique opportunity to engineer new functionalities in nano-spintronics. One such material is CrI$_3$, showing long-range magnetic order in monolayer with the Curie temperature ($T_c$) of 45 K. Here we study detailed evolution of magnetic transition and magnetic critical properties in response to systematic reduction in crystal thickness down to 50 nm. Bulk $T_c$ of 61 K is gradually suppressed to 57 K, however, the satellite transition at $T^*$ = 45 K is observed layer-independent at fixed magnetic field of 1 kOe. The origin of $T^*$ is proposed to be a crossover from pinning to depinning of magnetic domain walls. The reduction of thickness facilitates a field-driven metamagnetic transition around 20 kOe with out-of-plane field, in contrast to the continuous changes with in-plane field. The critical analysis around $T_c$ elucidates the mean-field type interactions in microscale-thick CrI$_3$.

cond-mat.str-el↗

Capacity & Perimeter from $α$-Hermite Bounded Variation

Let $\mathcal{H}_α=Δ-(α-1)|x|^α$ be an $[1,\infty)\niα$-Hermite operator for the hydrogen atom located at the origin in $\mathbb R^d$. In this paper, we are motivated by the classical case $α=1$ to investigate the space of functions with $α$-{\it Hermite Bounded Variation} and its functional capacity and geometrical perimeter.

math.CA↗

Fractional heat semigroups on metric measure spaces with finite densities and applications to fractional dissipative equations

Let $(\mathbb M, d,μ)$ be a metric measure space with upper and lower densities: $$ \begin{cases} |||μ|||_β:=\sup_{(x,r)\in \mathbb M\times(0,\infty)} μ(B(x,r))r^{-β}<\infty;\\ |||μ|||_{β^{\star}}:=\inf_{(x,r)\in \mathbb M\times(0,\infty)} μ(B(x,r))r^{-β^{\star}}>0, \end{cases} $$ where $β, β^{\star}$ are two positive constants which are less than or equal to the Hausdorff dimension of $\mathbb M$. Assume that $p_t(\cdot,\cdot)$ is a heat kernel on $\mathbb M$ satisfying Gaussian upper estimates and $\mathcal L$ is the generator of the semigroup associated with $p_t(\cdot,\cdot)$. In this paper, via a method independent of Fourier transform, we establish the decay estimates for the kernels of the fractional heat semigroup $\{e^{-t \mathcal{L}^α}\}_{t>0}$ and the operators $\{\mathcal{L}^{θ/2} e^{-t \mathcal{L}^α}\}_{t>0}$, respectively. By these estimates, we obtain the regularity for the Cauchy problem of the fractional dissipative equation associated with $\mathcal L$ on $(\mathbb M, d,μ)$. Moreover, based on the geometric-measure-theoretic analysis of a new $L^p$-type capacity defined in $\mathbb{M}\times(0,\infty)$, we also characterize a nonnegative Randon measure $ν$ on $\mathbb M\times(0,\infty)$ such that $R_αL^p(\mathbb M)\subseteq L^q(\mathbb M\times(0,\infty),ν)$ under $(α,p,q)\in (0,1)\times(1,\infty)\times(1,\infty)$, where $u=R_αf$ is the weak solution of the fractional diffusion equation $(\partial_t+ \mathcal{L}^α)u(t,x)=0$ in $\mathbb M\times(0,\infty)$ subject to $u(0,x)=f(x)$ in $\mathbb M$.

math.AP↗

In defense of OSVOS

As a milestone for video object segmentation, one-shot video object segmentation (OSVOS) has achieved a large margin compared to the conventional optical-flow based methods regarding to the segmentation accuracy. Its excellent performance mainly benefit from the three-step training mechanism, that are: (1) acquiring object features on the base dataset (i.e. ImageNet), (2) training the parent network on the training set of the target dataset (i.e. DAVIS-2016) to be capable of differentiating the object of interest from the background. (3) online fine-tuning the interested object on the first frame of the target test set to overfit its appearance, then the model can be utilized to segment the same object in the rest frames of that video. In this paper, we argue that for the step (2), OSVOS has the limitation to 'overemphasize' the generic semantic object information while 'dilute' the instance cues of the object(s), which largely block the whole training process. Through adding a common module, video loss, which we formulate with various forms of constraints (including weighted BCE loss, high-dimensional triplet loss, as well as a novel mixed instance-aware video loss), to train the parent network in the step (2), the network is then better prepared for the step (3), i.e. online fine-tuning on the target instance. Through extensive experiments using different network structures as the backbone, we show that the proposed video loss module can improve the segmentation performance significantly, compared to that of OSVOS. Meanwhile, since video loss is a common module, it can be generalized to other fine-tuning based methods and similar vision tasks such as depth estimation and saliency detection.

cs.CV↗

Minimax Estimation of Large Precision Matrices with Bandable Cholesky Factor

Last decade witnesses significant methodological and theoretical advances in estimating large precision matrices. In particular, there are scientific applications such as longitudinal data, meteorology and spectroscopy in which the ordering of the variables can be interpreted through a bandable structure on the Cholesky factor of the precision matrix. However, the minimax theory has still been largely unknown, as opposed to the well established minimax results over the corresponding bandable covariance matrices. In this paper, we focus on two commonly used types of parameter spaces, and develop the optimal rates of convergence under both the operator norm and the Frobenius norm. A striking phenomenon is found: two types of parameter spaces are fundamentally different under the operator norm but enjoy the same rate optimality under the Frobenius norm, which is in sharp contrast to the equivalence of corresponding two types of bandable covariance matrices under both norms. This fundamental difference is established by carefully constructing the corresponding minimax lower bounds. Two new estimation procedures are developed: for the operator norm, our optimal procedure is based on a novel local cropping estimator targeting on all principle submatrices of the precision matrix while for the Frobenius norm, our optimal procedure relies on a delicate regression-based thresholding rule. Lepski's method is considered to achieve optimal adaptation. We further establish rate optimality in the nonparanormal model. Numerical studies are carried out to confirm our theoretical findings.

math.ST↗