arXiv subjects

Li Yang

Publications and source records attributed to Li Yang.

At least 253 records · Page 14Linked to original sources

arXiv2019

Curie Temperature of Emerging Two-Dimensional Magnetic Structures

Recent realizations of intrinsic, long-range magnetic orders in two-dimensional (2D) van der Waals materials have ignited tremendous research interests. In this work, we employ the XXZ Heisenberg model and Monte Carlo simulations to study a fundamental property of these emerging 2D magnetic materials, the Curie temperature (Tc). By including both onsite and neighbor couplings extracted from first-principles simulations, we have calculated Tc of monolayer chromium trihalides and Cr2Ge2Te6, which are of broad interests currently, and the simulation results agree with available measurements. We also clarify the roles played by anisotropic and isotropic interactions in deciding Tc of magnetic orders. Particularly, we find a universal, linear dependence between Tc and magnetic interactions within the parameter space of realistic materials. With this linear dependence, we can predict Tc of general 2D lattice structures, omitting the Monte Carlo simulations. Compared with the widely used Ising model, mean-field theory, and spin-wave theory, this work provides a convenient and quantitative estimation of Tc, giving hope to speeding up the search for novel 2D materials with higher Curie temperatures.

cond-mat.mes-hall↗

arXiv2019

Deep Learning-Enhanced Variational Monte Carlo Method for Quantum Many-Body Physics

Artificial neural networks have been successfully incorporated into variational Monte Carlo method (VMC) to study quantum many-body systems. However, there have been few systematic studies of exploring quantum many-body physics using deep neural networks (DNNs), despite of the tremendous success enjoyed by DNNs in many other areas in recent years. One main challenge of implementing DNN in VMC is the inefficiency of optimizing such networks with large number of parameters. We introduce an importance sampling gradient optimization (ISGO) algorithm, which significantly improves the computational speed of training DNN in VMC. We design an efficient convolutional DNN architecture to compute the ground state of a one-dimensional (1D) SU($N$) spin chain. Our numerical results of the ground-state energies with up to 16 layers of DNN show excellent agreement with the Bethe-Ansatz exact solution. Furthermore, we also calculate the loop correlation function using the wave function obtained. Our work demonstrates the feasibility and advantages of applying DNNs to numerical quantum many-body calculations.

cond-mat.str-el↗

arXiv2019

Quantifying Inactive Lithium in Lithium Metal Batteries

Inactive lithium (Li) formation is the immediate cause of capacity loss and catastrophic failure of Li metal batteries. However, the chemical component and the atomic level structure of inactive Li have rarely been studied due to the lack of effective diagnosis tools to accurately differentiate and quantify Li+ in solid electrolyte interphase (SEI) components and the electrically isolated unreacted metallic Li0, which together comprise the inactive Li. Here, by introducing a new analytical method, Titration Gas Chromatography (TGC), we can accurately quantify the contribution from metallic Li0 to the total amount of inactive Li. We uncover that the Li0, rather than the electrochemically formed SEI, dominates the inactive Li and capacity loss. Using cryogenic electron microscopies to further study the microstructure and nanostructure of inactive Li, we find that the Li0 is surrounded by insulating SEI, losing the electronic conductive pathway to the bulk electrode. Coupling the measurements of the Li0 global content to observations of its local atomic structure, we reveal the formation mechanism of inactive Li in different types of electrolytes, and identify the true underlying cause of low Coulombic efficiency in Li metal deposition and stripping. We ultimately propose strategies to enable the highly efficient Li deposition and stripping to enable Li metal anode for next generation high energy batteries.

cond-mat.mtrl-sci↗

arXiv2019

Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness

Deep Neural Network (DNN) trained by the gradient descent method is known to be vulnerable to maliciously perturbed adversarial input, aka. adversarial attack. As one of the countermeasures against adversarial attack, increasing the model capacity for DNN robustness enhancement was discussed and reported as an effective approach by many recent works. In this work, we show that shrinking the model size through proper weight pruning can even be helpful to improve the DNN robustness under adversarial attack. For obtaining a simultaneously robust and compact DNN model, we propose a multi-objective training method called Robust Sparse Regularization (RSR), through the fusion of various regularization techniques, including channel-wise noise injection, lasso weight penalty, and adversarial training. We conduct extensive experiments across popular ResNet-20, ResNet-18 and VGG-16 DNN architectures to demonstrate the effectiveness of RSR against popular white-box (i.e., PGD and FGSM) and black-box attacks. Thanks to RSR, 85% weight connections of ResNet-18 can be pruned while still achieving 0.68% and 8.72% improvement in clean- and perturbed-data accuracy respectively on CIFAR-10 dataset, in comparison to its PGD adversarial training baseline.

cs.CV↗

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

Emerging Photoluminescence from the Dark-Exciton Phonon Replica in Monolayer WSe2

Tungsten-based monolayer transition metal dichalcogenides host a long-lived dark exciton, an electron-hole pair in a spin-triplet configuration. The long lifetime and unique spin properties of the dark exciton provide exciting opportunities to explore light-matter interactions beyond electric dipole transitions. Here we demonstrate that the coupling of the dark exciton and an optically silent chiral phonon enables the intrinsic photoluminescence of the dark-exciton replica in monolayer WSe2. Gate and magnetic-field dependent PL measurements unveil a circularly-polarized replica peak located below the dark exciton by 21.6 meV, equal to E" phonon energy from Se vibrations. First-principles calculations show that the exciton-phonon interaction selectively couples the spin-forbidden dark exciton to the intravalley spin-allowed bright exciton, permitting the simultaneous emission of a chiral phonon and a circularly-polarized photon. Our discovery and understanding of the phonon replica reveals a chirality dictated emission channel of the phonons and photons, unveiling a new route of manipulating valley-spin.

cond-mat.mes-hall↗

arXiv2019

Noncollinearity-modulated electronic properties of the monolayer CrI$_3$

Introducing noncollinear magnetization into a monolayer CrI$_3$ is proposed to be an effective approach to modulate the local electronic properties of the two-dimensional (2D) magnetic material. Using first-principles calculation, we illustrate that both the conduction and valence bands in the monolayer CrI$_3$ are lowered down by spin spiral states. The distinct electronic structure of the monolayer noncollinear CrI$_3$ can be applied in nanoscale functional devices. As a proof of concept, we show that a magnetic domain wall can form a one-dimensional conducting channel in the 2D semiconductor via proper gating. Other possible applications such as electron-hole separation and identical quantum dots are also discussed.

cond-mat.mtrl-sci↗

arXiv2019

Saliency Prediction on Omnidirectional Images with Generative Adversarial Imitation Learning

When watching omnidirectional images (ODIs), subjects can access different viewports by moving their heads. Therefore, it is necessary to predict subjects' head fixations on ODIs. Inspired by generative adversarial imitation learning (GAIL), this paper proposes a novel approach to predict saliency of head fixations on ODIs, named SalGAIL. First, we establish a dataset for attention on ODIs (AOI). In contrast to traditional datasets, our AOI dataset is large-scale, which contains the head fixations of 30 subjects viewing 600 ODIs. Next, we mine our AOI dataset and determine three findings: (1) The consistency of head fixations are consistent among subjects, and it grows alongside the increased subject number; (2) The head fixations exist with a front center bias (FCB); and (3) The magnitude of head movement is similar across subjects. According to these findings, our SalGAIL approach applies deep reinforcement learning (DRL) to predict the head fixations of one subject, in which GAIL learns the reward of DRL, rather than the traditional human-designed reward. Then, multi-stream DRL is developed to yield the head fixations of different subjects, and the saliency map of an ODI is generated via convoluting predicted head fixations. Finally, experiments validate the effectiveness of our approach in predicting saliency maps of ODIs, significantly better than 10 state-of-the-art approaches.

cs.CV↗

arXiv2019

PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST

We describe PSR J1926-0652, a pulsar recently discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive single-pulse detections from FAST and long-term timing observations from the Parkes 64-m radio telescope, we probed phenomena on both long and short time scales. The FAST observations covered a wide frequency range from 270 to 800 MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at least four profile components, short-term nulling lasting from 4 to 450 pulses, complex subpulse drifting behaviours and intermittency on scales of tens of minutes. While the average band spacing P3 is relatively constant across different bursts and components, significant variations in the separation of adjacent bands are seen, especially near the beginning and end of a burst. Band shapes and slopes are quite variable, especially for the trailing components and for the shorter bursts. We show that for each burst the last detectable pulse prior to emission ceasing has different properties compared to other pulses. These complexities pose challenges for the classic carousel-type models.

astro-ph.HE↗

arXiv2018

Block encryption of quantum messages

In modern cryptography, block encryption is a fundamental cryptographic primitive. However, it is impossible for block encryption to achieve the same security as one-time pad. Quantum mechanics has changed the modern cryptography, and lots of researches have shown that quantum cryptography can outperform the limitation of traditional cryptography. This article proposes a new constructive mode for private quantum encryption, named $\mathcal{EHE}$, which is a very simple method to construct quantum encryption from classical primitive. Based on $\mathcal{EHE}$ mode, we construct a quantum block encryption (QBE) scheme from pseudorandom functions. If the pseudorandom functions are standard secure, our scheme is indistinguishable encryption under chosen plaintext attack. If the pseudorandom functions are permutation on the key space, our scheme can achieve perfect security. In our scheme, the key can be reused and the randomness cannot, so a $2n$-bit key can be used in an exponential number of encryptions, where the randomness will be refreshed in each time of encryption. Thus $2n$-bit key can perfectly encrypt $O(n2^n)$ qubits, and the perfect secrecy would not be broken if the $2n$-bit key is reused for only exponential times. Comparing with quantum one-time pad (QOTP), our scheme can be the same secure as QOTP, and the secret key can be reused (no matter whether the eavesdropping exists or not). Thus, the limitation of perfectly secure encryption (Shannon's theory) is broken in the quantum setting. Moreover, our scheme can be viewed as a positive answer to the open problem in quantum cryptography "how to unconditionally reuse or recycle the whole key of private-key quantum encryption". In order to physically implement the QBE scheme, we only need to implement two kinds of single-qubit gates (Pauli $X$ gate and Hadamard gate), so it is within reach of current quantum technology.

cs.CR↗

arXiv2018

Edge-Insensitive Magnetism and Half Metallicity in Graphene Nanoribbons

Realizing magnetism in graphene/carbon nanostructures is a decade-long challenge. The magnetic edge state and half metallicity in zigzag graphene nanoribbons are particularly promising [Y.-W. Son, et al., Nature 444, 347 (2006)]. However, its experimental realization has been hindered by the stringent requirement of the mono-hydrogenated zigzag edge. Using first-principle calculations, we predict that free-carrier doping can overcome this challenge and realize ferromagnetism and half-metallicity in narrow graphene nanoribbons of general types of edge structures. This magnetism exists within the density range of gate-doping experiments (~1013 cm-2) and has large spin polarization energy up to 17 meV per carrier, which induces a Zeeman splitting equivalent to an external magnetic field of a few hundred Tesla. Finally, we trace the formation mechanics of this edge-insensitive magnetism to the quantum confinement of the electronic state near the band edge and reveal the scaling law of magnetism versus the ribbon width. Our findings suggest that combining doping with quantum confinement could be a general tool to realize transition-metal-free magnetism in light-element nanostructures.

cond-mat.mes-hall↗

arXiv2018

A quantum related-key attack based on Bernstein-Vazirani algorithm

Due to the powerful computing capability of quantum computers, cryptographic researchers have applied quantum algorithms to cryptanalysis and obtained many interesting results in recent years. In this paper, we study related-key attack in the quantum setting, and proposed a specific related-key attack which can recover the key of block ciphers efficiently, as long as the attacked block ciphers satisfy certain conditions. The attack algorithm employs Bernstein-Vazirani algorithm as a subroutine and requires the attacker to query the encryption oracle with quantum superpositions. Afterwards, we rigorously demonstrate the validity of the attack and analyze its complexity. Our work shows that related-key attack is quite powerful when combined with quantum algorithms, and provides some guidance for the design of block ciphers that are secure against quantum adversaries.

quant-ph↗

arXiv2018

Quantum impossible differential and truncated differential cryptanalysis

Traditional cryptography is suffering a huge threat from the development of quantum computing. While many currently used public-key cryptosystems would be broken by Shor's algorithm, the effect of quantum computing on symmetric ones is still unclear. The security of symmetric ciphers relies heavily on the development of cryptanalytic tools. Thus, in order to accurately evaluate the security of symmetric primitives in the post-quantum world, it is significant to improve classical cryptanalytic methods using quantum algorithms. In this paper, we focus on two variants of differential cryptanalysis: truncated differential cryptanalysis and impossible differential cryptanalysis. Based on the fact that Bernstein-Vazirani algorithm can be used to find the linear structures of Boolean functions, we propose two quantum algorithms that can be used to find high-probability truncated differentials and impossible differentials of block ciphers, respectively. We rigorously prove the validity of the algorithms and analyze their complexity. Our algorithms treat all rounds of the reduced cipher as a whole and only concerns the input and output differences at its both ends, instead of specific differential characteristics. Therefore, to a certain extent, they alleviate the weakness of conventional differential cryptanalysis, namely the difficulties in finding differential characteristics as the number of rounds increases.

quant-ph↗

arXiv2018

Using Bernstein-Vazirani Algorithm to Attack Block Ciphers

In this paper, we study applications of Bernstein-Vazirani algorithm and present several new methods to attack block ciphers. Specifically, we first present a quantum algorithm for finding the linear structures of a function. Based on it, we propose new quantum distinguishers for the 3-round Feistel scheme and a new quantum algorithm to recover partial key of the Even-Mansour construction. Afterwards, by observing that the linear structures of a encryption function are actually high probability differentials of it, we apply our algorithm to differential analysis and impossible differential cryptanalysis respectively. We also propose a new kind of differential cryptanalysis, called quantum small probability differential cryptanalysis, based on the fact that the linear structures found by our algorithm are also the linear structure of each component function. To our knowledge, no similar method was proposed before. The efficiency and success probability of all attacks are analyzed rigorously. Since our algorithm treats the encryption function as a whole, it avoid the disadvantage of traditional differential cryptanalysis that it is difficult to extending the differential path.

quant-ph↗

arXiv2018

Moiré Excitons in Van der Waals Heterostructures

In van der Waals (vdW) heterostructures formed by stacking two monolayer semiconductors, lattice mismatch or rotational misalignment introduces an in-plane moiré superlattice. While it is widely recognized that a moiré superlattice can modulate the electronic band structure and lead to novel transport properties including unconventional superconductivity and insulating behavior driven by correlations, its influence on optical properties has not been investigated experimentally. We present spectroscopic evidence that interlayer excitons are confined by the moiré potential in a high-quality MoSe2/WSe2 heterobilayer with small rotational twist. A series of interlayer exciton resonances with either positive or negative circularly polarized emission is observed in photoluminescence, consistent with multiple exciton states confined within the moiré potential. The recombination dynamics and temperature dependence of these interlayer exciton resonances are consistent with this interpretation. These results demonstrate the feasibility of engineering artificial excitonic crystals using vdW heterostructures for nanophotonics and quantum information applications.

cond-mat.mes-hall↗

arXiv2018

Microsecond Valley Lifetime of Defect-Bound Excitons in Monolayer WSe$_2$

In atomically thin two-dimensional semiconductors such as transition metal dichalcogenides (TMDs), controlling the density and type of defects promises to be an effective approach for engineering light-matter interactions. We demonstrate that electron-beam irradiation is a simple tool for selectively introducing defect-bound exciton states associated with chalcogen vacancies in TMDs. Our first-principles calculations and time-resolved spectroscopy measurements of monolayer WSe2 reveal that these defect-bound excitons exhibit exceptional optical properties including a recombination lifetime approaching 200 ns and a valley lifetime longer than 1 $μ$s. The ability to engineer the crystal lattice through electron irradiation provides a new approach for tailoring the optical response of TMDs for photonics, quantum optics, and valleytronics applications.

cond-mat.mes-hall↗

arXiv2018

Counterfactual Quantum Bit Commitment

We propose a framework of bit commitment protocol using a comparison scheme and present a compound comparison scheme based on counterfactual cryptography. Finally, we propose a counterfactual quantum bit commitment protocol. In security analysis, we give the proper security parameters for counterfactual quantum bit commitment and prove that intercept attack and intercept/resend attack are ineffective attack for our protocol. In addition, we explain that counterfactual quantum bit commitment protocol cannot be attacked with no-go theorem attack by current technology.

quant-ph↗

arXiv2018

A voting scheme with post-quantum security based on physical laws

Traditional cryptography is under huge threat along of the evolution of quantum information and computing. In this paper, we propose a new post-quantum voting scheme based on physical laws by using encrypted no-key protocol to transmit message in the channel, which ensures the post-quantum security. Unlike lattice-based and multivariate-based electronic voting schemes, whose security is based on the computational problems assumption that has not been solved by effective quantum algorithms until now, the security of the voting scheme based on the physical laws is depended on inherent limitations of quantum computers and not influenced by the evolution of new quantum algorithms. In detail, we also rigorously demonstrate that the scheme achieves the post-quantum security and all properties necessary for voting scheme such as the completeness, robustness, privacy, eligibility, unreusability, fairness, and verifiability.

cs.CR↗