arXiv subjects

Yuan Cao

Publications and source records attributed to Yuan Cao.

At least 181 records · Page 10Linked to original sources

arXiv2020

Flavour Hund's Coupling, Correlated Chern Gaps, and Diffusivity in Moiré Flat Bands

Interaction-driven spontaneous symmetry breaking lies at the heart of many quantum phases of matter. In moiré systems, broken spin/valley 'flavour' symmetry in flat bands underlies the parent state out of which ultimately correlated and topological ground states emerge. However, the microscopic mechanism of such flavour symmetry breaking and its connection to the low-temperature many-body phases remain to be understood. Here, we investigate the symmetry-broken many-body ground state of magic angle twisted bilayer graphene (MATBG) and its nontrivial topology using simultaneous thermodynamic and transport measurements. We directly observe flavour symmetry breaking as a pinning of the chemical potential $μ$ at all integer fillings of the moiré superlattice, highlighting the importance of flavour Hund's coupling in the many-body ground state. The topological nature of the underlying flat bands is manifested upon breaking time-reversal symmetry, where we measure energy gaps corresponding to Chern insulator states with Chern numbers $C=3,2,1$ at filling factors $ν=1,2,3$, respectively, consistent with flavour symmetry breaking in the Hofstadter's butterfly spectrum of MATBG. Moreover, our concurrent measurements of resistivity and chemical potential allow us to obtain the temperature dependence of the charge diffusivity of MATBG in the strange metal regime, a quantity previously explored only in ultracold atom systems. Our results bring us one step closer to a unified framework for understanding interactions in the topological bands of MATBG, both in the presence and absence of a magnetic field.

cond-mat.mes-hall↗

arXiv2020

An explicit expression for Euclidean self-dual cyclic codes of length $2^k$ over Galois ring ${\rm GR}(4,m)$

For any positive integers $m$ and $k$, existing literature only determines the number of all Euclidean self-dual cyclic codes of length $2^k$ over the Galois ring ${\rm GR}(4,m)$, such as in [Des. Codes Cryptogr. (2012) 63:105--112]. Using properties for Kronecker products of matrices of a specific type and column vectors of these matrices, we give a simple and efficient method to construct all these self-dual cyclic codes precisely. On this basis, we provide an explicit expression to accurately represent all distinct Euclidean self-dual cyclic codes of length $2^k$ over ${\rm GR}(4,m)$, using combination numbers. As an application, we list all distinct Euclidean self-dual cyclic codes over ${\rm GR}(4,m)$ of length $2^k$ explicitly, for $k=4,5,6$.

cs.IT↗

arXiv2020

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks

Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, despite the nice property of fast convergence, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". We design a new algorithm, called Partially adaptive momentum estimation method, which unifies the Adam/Amsgrad with SGD by introducing a partial adaptive parameter $p$, to achieve the best from both worlds. We also prove the convergence rate of our proposed algorithm to a stationary point in the stochastic nonconvex optimization setting. Experiments on standard benchmarks show that our proposed algorithm can maintain a fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks.

cs.LG↗

arXiv2020

Managing Recurrent Virtual Network Updates in Multi-Tenant Datacenters: A System Perspective

With the advent of software-defined networking, network configuration through programmable interfaces becomes practical, leading to various on-demand opportunities for network routing update in multi-tenant datacenters, where tenants have diverse requirements on network routings such as short latency, low path inflation, large bandwidth, high reliability, etc. Conventional solutions that rely on topology search coupled with an objective function https:// www.overleaf.com/project/5beb742041ab9c0e3caec84f to find desired routings have at least two shortcomings: (i) they run into scalability issues when handling consistent and frequent routing updates and (ii) they restrict the flexibility and capability to satisfy various routing requirements. To address these issues, this paper proposes a novel search and optimization decoupled design, which not only saves considerable topology search costs via search result reuse, but also avoids possible sub-optimality in greedy routing search algorithms by making decisions based on the global view of all possible routings. We implement a prototype of our proposed system, OpReduce, and perform extensive evaluations to validate its design goals.

cs.CR↗

arXiv2020

Long-distance free-space measurement-device-independent quantum key distribution

Measurement-device-independent quantum key distribution (MDI-QKD), based on two-photon interference, is immune to all attacks against the detection system and allows a QKD network with untrusted relays. Since the MDI-QKD protocol was proposed, fibre-based implementations have been rapidly developed towards longer distance, higher key rates, and network verification. However, owing to the effect of atmospheric turbulence, MDI-QKD over free-space channel remains experimentally challenging. Here, by developing the robust adaptive optics system, high precision time synchronization and frequency locking between independent photon sources located far apart, we realised the first free-space MDI-QKD over a 19.2-km urban atmospheric channel, which well exceeds the effective atmospheric thickness. Our experiment takes the first step towards satellite-based MDI-QKD. Moreover, the technology developed here opens the way to quantum experiments in free space involving long-distance interference of independent single photons.

quant-ph↗

arXiv2020

Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation

Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The second direction employs monolingual data with self-supervision to pre-train translation models, followed by fine-tuning on small amounts of supervised data. In this work, we join these two lines of research and demonstrate the efficacy of monolingual data with self-supervision in multilingual NMT. We offer three major results: (i) Using monolingual data significantly boosts the translation quality of low-resource languages in multilingual models. (ii) Self-supervision improves zero-shot translation quality in multilingual models. (iii) Leveraging monolingual data with self-supervision provides a viable path towards adding new languages to multilingual models, getting up to 33 BLEU on ro-en translation without any parallel data or back-translation.

cs.CL↗

arXiv2020

Nematicity and Competing Orders in Superconducting Magic-Angle Graphene

Strongly interacting electrons in solid-state systems often display tendency towards multiple broken symmetries in the ground state. The complex interplay between different order parameters can give rise to a rich phase diagram. Here, we report on the identification of intertwined phases with broken rotational symmetry in magic-angle twisted bilayer graphene (TBG). Using transverse resistance measurements, we find a strongly anisotropic phase located in a 'wedge' above the underdoped region of the superconducting dome. Upon crossing the superconducting dome, a reduction of the critical temperature is observed, similar to the behavior of certain cuprate superconductors. Furthermore, the superconducting state exhibits a anisotropic response to an directional-dependent in-plane magnetic field, revealing a nematic pairing state across the entire superconducting dome. These results indicate that nematic fluctuations might play an important role in the low-temperature phases of magic-angle TBG, and pave the way for using highly-tunable moiré superlattices to investigate intertwined phases in quantum materials.

cond-mat.mes-hall↗

arXiv2020

Time-Frequency Transfer through a 70 dB Free Space Channel: Towards Satellite-Ground Time Dissemination

Time and frequency transfer lies at the heart of the field of metrology. Compared to current microwave dissemination such as GPS, optical domain dissemination can provide more than one order of magnitude in terms of higher accuracy, which allows for many applications such as the redefinition of the second, tests of general relativity and fundamental quantum physics, precision navigation and quantum communication. Although optical frequency transfer has been demonstrated over thousand kilometers fiber lines, intercontinental time comparison and synchronization still requires satellite free space optical time and frequency transfer. Quite a few pioneering free space optical time and frequency experiments have been implemented at the distance of tens kilometers at ground level. However, there exists no detailed analysis or ground test to prove the feasibility of satellite-based optical time-frequency transfer. Here, we analyze the possibility of this system and then provide the first-step ground test with high channel loss. We demonstrate the optical frequency transfer with an instability of $10^{-18}$ level in 8,000 seconds across a 16-km free space channel with a loss of up to 70~dB, which is comparable with the loss of a satellite-ground link at medium earth orbit (MEO) and geostationary earth orbit (GEO).

physics.optics↗

arXiv2020

Echo State Neural Machine Translation

We present neural machine translation (NMT) models inspired by echo state network (ESN), named Echo State NMT (ESNMT), in which the encoder and decoder layer weights are randomly generated then fixed throughout training. We show that even with this extremely simple model construction and training procedure, ESNMT can already reach 70-80% quality of fully trainable baselines. We examine how spectral radius of the reservoir, a key quantity that characterizes the model, determines the model behavior. Our findings indicate that randomized networks can work well even for complicated sequence-to-sequence prediction NLP tasks.

cs.CL↗

arXiv2020

Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis

This paper proposes a hierarchical, fine-grained and interpretable latent variable model for prosody based on the Tacotron 2 text-to-speech model. It achieves multi-resolution modeling of prosody by conditioning finer level representations on coarser level ones. Additionally, it imposes hierarchical conditioning across all latent dimensions using a conditional variational auto-encoder (VAE) with an auto-regressive structure. Evaluation of reconstruction performance illustrates that the new structure does not degrade the model while allowing better interpretability. Interpretations of prosody attributes are provided together with the comparison between word-level and phone-level prosody representations. Moreover, both qualitative and quantitative evaluations are used to demonstrate the improvement in the disentanglement of the latent dimensions.

eess.AS↗

arXiv2020

Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior

Recent neural text-to-speech (TTS) models with fine-grained latent features enable precise control of the prosody of synthesized speech. Such models typically incorporate a fine-grained variational autoencoder (VAE) structure, extracting latent features at each input token (e.g., phonemes). However, generating samples with the standard VAE prior often results in unnatural and discontinuous speech, with dramatic prosodic variation between tokens. This paper proposes a sequential prior in a discrete latent space which can generate more naturally sounding samples. This is accomplished by discretizing the latent features using vector quantization (VQ), and separately training an autoregressive (AR) prior model over the result. We evaluate the approach using listening tests, objective metrics of automatic speech recognition (ASR) performance, and measurements of prosody attributes. Experimental results show that the proposed model significantly improves the naturalness in random sample generation. Furthermore, initial experiments demonstrate that randomly sampling from the proposed model can be used as data augmentation to improve the ASR performance.

eess.AS↗

arXiv2020

Super-resolution single-photon imaging at 8.2 kilometers

Single-photon light detection and ranging (LiDAR), offering single-photon sensitivity and picosecond time resolution, has been widely adopted for active imaging applications. Long-range active imaging is a great challenge, because the spatial resolution degrades significantly with the imaging range due to the diffraction limit of the optics, and only weak echo signal photons can return but mixed with a strong background noise. Here we propose and demonstrate a photon-efficient LiDAR approach that can achieve sub-Rayleigh resolution imaging over long ranges. This approach exploits fine sub-pixel scanning and a deconvolution algorithm tailored to this long-range application. Using this approach, we experimentally demonstrated active three-dimensional (3D) single-photon imaging by recognizing different postures of a mannequin model at a stand-off distance of 8.2 km in both daylight and night. The observed spatial (transversal) resolution is about 5.5 cm at 8.2 km, which is about twice of the system's resolution. This also beats the optical system's Rayleigh criterion. The results are valuable for geosciences and target recognition over long ranges.

eess.IV↗

arXiv2020

Deep-Learning-Enabled Fast Optical Identification and Characterization of Two-Dimensional Materials

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important physical and chemical properties. However, the interpretation of imaging data heavily relies on the "intuition" of experienced researchers. As a result, many of the deep graphical features obtained through these tools are often unused because of difficulties in processing the data and finding the correlations. Such challenges can be well addressed by deep learning. In this work, we use the optical characterization of two-dimensional (2D) materials as a case study, and demonstrate a neural-network-based algorithm for the material and thickness identification of exfoliated 2D materials with high prediction accuracy and real-time processing capability. Further analysis shows that the trained network can extract deep graphical features such as contrast, color, edges, shapes, segment sizes and their distributions, based on which we develop an ensemble approach topredict the most relevant physical properties of 2D materials. Finally, a transfer learning technique is applied to adapt the pretrained network to other applications such as identifying layer numbers of a new 2D material, or materials produced by a different synthetic approach. Our artificial-intelligence-based material characterization approach is a powerful tool that would speed up the preparation, initial characterization of 2D materials and other nanomaterials and potentially accelerate new material discoveries.

cond-mat.mtrl-sci↗

arXiv2019

Cascade of Phase Transitions and Dirac Revivals in Magic Angle Graphene

Twisted bilayer graphene near the magic angle exhibits remarkably rich electron correlation physics, displaying insulating, magnetic, and superconducting phases. Here, using measurements of the local electronic compressibility, we reveal that these phases originate from a high-energy state with an unusual sequence of band populations. As carriers are added to the system, rather than filling all the four spin and valley flavors equally, we find that the population occurs through a sequence of sharp phase transitions, which appear as strong asymmetric jumps of the electronic compressibility near integer fillings of the moire lattice. At each transition, a single spin/valley flavor takes all the carriers from its partially filled peers, "resetting" them back to the vicinity of the charge neutrality point. As a result, the Dirac-like character observed near the charge neutrality reappears after each integer filling. Measurement of the in-plane magnetic field dependence of the chemical potential near filling factor one reveals a large spontaneous magnetization, further substantiating this picture of a cascade of symmetry breakings. The sequence of phase transitions and Dirac revivals is observed at temperatures well above the onset of the superconducting and correlated insulating states. This indicates that the state we reveal here, with its strongly broken electronic flavor symmetry and revived Dirac-like electronic character, is a key player in the physics of magic angle graphene, forming the parent state out of which the more fragile superconducting and correlated insulating ground states emerge.

cond-mat.mes-hall↗

arXiv2019

Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks

Empirical studies show that gradient-based methods can learn deep neural networks (DNNs) with very good generalization performance in the over-parameterization regime, where DNNs can easily fit a random labeling of the training data. Very recently, a line of work explains in theory that with over-parameterization and proper random initialization, gradient-based methods can find the global minima of the training loss for DNNs. However, existing generalization error bounds are unable to explain the good generalization performance of over-parameterized DNNs. The major limitation of most existing generalization bounds is that they are based on uniform convergence and are independent of the training algorithm. In this work, we derive an algorithm-dependent generalization error bound for deep ReLU networks, and show that under certain assumptions on the data distribution, gradient descent (GD) with proper random initialization is able to train a sufficiently over-parameterized DNN to achieve arbitrarily small generalization error. Our work sheds light on explaining the good generalization performance of over-parameterized deep neural networks.

cs.LG↗

arXiv2019

Precipitation Nowcasting with Star-Bridge Networks

Precipitation nowcasting, which aims to precisely predict the short-term rainfall intensity of a local region, is gaining increasing attention in the artificial intelligence community. Existing deep learning-based algorithms use a single network to process various rainfall intensities together, compromising the predictive accuracy. Therefore, this paper proposes a novel recurrent neural network (RNN) based star-bridge network (StarBriNet) for precipitation nowcasting. The novelty of this work lies in the following three aspects. First, the proposed network comprises multiple sub-networks to deal with different rainfall intensities and duration separately, which can significantly improve the model performance. Second, we propose a star-shaped information bridge to enhance the information flow across RNN layers. Third, we introduce a multi-sigmoid loss function to take the precipitation nowcasting criterion into account. Experimental results demonstrate superior performance for precipitation nowcasting over existing algorithms, including the state-of-the-art one, on a natural radar echo dataset.

cs.CV↗

arXiv2019

Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

We study the training and generalization of deep neural networks (DNNs) in the over-parameterized regime, where the network width (i.e., number of hidden nodes per layer) is much larger than the number of training data points. We show that, the expected $0$-$1$ loss of a wide enough ReLU network trained with stochastic gradient descent (SGD) and random initialization can be bounded by the training loss of a random feature model induced by the network gradient at initialization, which we call a neural tangent random feature (NTRF) model. For data distributions that can be classified by NTRF model with sufficiently small error, our result yields a generalization error bound in the order of $\tilde{\mathcal{O}}(n^{-1/2})$ that is independent of the network width. Our result is more general and sharper than many existing generalization error bounds for over-parameterized neural networks. In addition, we establish a strong connection between our generalization error bound and the neural tangent kernel (NTK) proposed in recent work.

cs.LG↗

arXiv2019

Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks

We study the sample complexity of learning one-hidden-layer convolutional neural networks (CNNs) with non-overlapping filters. We propose a novel algorithm called approximate gradient descent for training CNNs, and show that, with high probability, the proposed algorithm with random initialization grants a linear convergence to the ground-truth parameters up to statistical precision. Compared with existing work, our result applies to general non-trivial, monotonic and Lipschitz continuous activation functions including ReLU, Leaky ReLU, Sigmod and Softplus etc. Moreover, our sample complexity beats existing results in the dependency of the number of hidden nodes and filter size. In fact, our result matches the information-theoretic lower bound for learning one-hidden-layer CNNs with linear activation functions, suggesting that our sample complexity is tight. Our theoretical analysis is backed up by numerical experiments.

cs.LG↗