arXiv subjects

Xi Chen

Publications and source records attributed to Xi Chen.

At least 865 records · Page 48Linked to original sources

arXiv2021

Phase-adaptive dynamical decoupling methods for robust spin-spin dynamics in trapped ions

Quantum platforms based on trapped ions are main candidates to build a quantum hardware with computational capacities that largely surpass those of classical devices. Among the available control techniques in these setups, pulsed dynamical decoupling (pulsed DD) revealed as a useful method to process the information encoded in ion registers, whilst minimising the environmental noise over them. In this work, we incorporate a pulsed DD technique that uses random pulse phases, or correlated pulse phases, to significantly enhance the robustness of entangling spin-spin dynamics in trapped ions. This procedure was originally conceived in the context of nuclear magnetic resonance for nuclear spin detection purposes, and here we demonstrate that the same principles apply for robust quantum information processing in trapped-ion settings.

quant-ph↗

arXiv2021

Broadband Polarization-Independent Achromatic Metalenses with Unintuitively-Designed Random-Shaped Meta-Atoms

Metasurface lenses, namely metalenses, are ultrathin planar nanostructures that are capable of manipulating the properties of incoming light and imparting lens-like wavefront to the output. Although they have shown promising potentials for the future miniaturization of optics, the chromatic aberration inherited from their diffractive nature plagues them towards many practical applications. Current solutions for creating achromatic metalenses usually require searching through a large number of meta-atoms to find designs that fulfill not only phase but phase dispersion requirements, which leads to intensive design efforts. Besides, most designs are based on regular-shaped antennas driven by the designers' intuition and experience, hence only cover a limited design space. Here, we present an inverse design approach that efficiently produces meta-atoms with unintuitive geometries required for broadband achromatic metalenses. We restricted the generated shapes to hold four-fold reflectional symmetry so that the resulting metalenses are polarization insensitive. In addition, meta-atoms generated by our method inheritably have round edges and corners, which make them nanofabrication-friendly. Our experimental characterization shows that our metalenses exhibit superior performance over a broad bandwidth of 465 nm in the near-infrared regime. Our method offers a fast and efficient way of designing high-performance achromatic metalenses and sheds new insights for unintuitive design of other metaphotonic devices.

physics.optics↗

arXiv2021

Lifelong Learning based Disease Diagnosis on Clinical Notes

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks. What is worse, the trained diagnosis system would be fixed once deployed but collecting training data that covers enough diseases is infeasible, which inspires us to develop a lifelong learning diagnosis system. In this work, we propose to adopt attention to combine medical entities and context, embedding episodic memory and consolidation to retain knowledge, such that the learned model is capable of adapting to sequential disease-diagnosis tasks. Moreover, we establish a new benchmark, named Jarvis-40, which contains clinical notes collected from various hospitals. Our experiments show that the proposed method can achieve state-of-the-art performance on the proposed benchmark.

cs.AI↗

arXiv2021

Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms

Reinforcement learning methods can achieve significant performance but require a large amount of training data collected on the same robotic platform. A policy trained with expensive data is rendered useless after making even a minor change to the robot hardware. In this paper, we address the challenging problem of adapting a policy, trained to perform a task, to a novel robotic hardware platform given only few demonstrations of robot motion trajectories on the target robot. We formulate it as a few-shot meta-learning problem where the goal is to find a meta-model that captures the common structure shared across different robotic platforms such that data-efficient adaptation can be performed. We achieve such adaptation by introducing a learning framework consisting of a probabilistic gradient-based meta-learning algorithm that models the uncertainty arising from the few-shot setting with a low-dimensional latent variable. We experimentally evaluate our framework on a simulated reaching and a real-robot picking task using 400 simulated robots generated by varying the physical parameters of an existing set of robotic platforms. Our results show that the proposed method can successfully adapt a trained policy to different robotic platforms with novel physical parameters and the superiority of our meta-learning algorithm compared to state-of-the-art methods for the introduced few-shot policy adaptation problem.

cs.RO↗

arXiv2021

Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference

This paper develops an efficient distributed inference algorithm, which is robust against a moderate fraction of Byzantine nodes, namely arbitrary and possibly adversarial machines in a distributed learning system. In robust statistics, the median-of-means (MOM) has been a popular approach to hedge against Byzantine failures due to its ease of implementation and computational efficiency. However, the MOM estimator has the shortcoming in terms of statistical efficiency. The first main contribution of the paper is to propose a variance reduced median-of-means (VRMOM) estimator, which improves the statistical efficiency over the vanilla MOM estimator and is computationally as efficient as the MOM. Based on the proposed VRMOM estimator, we develop a general distributed inference algorithm that is robust against Byzantine failures. Theoretically, our distributed algorithm achieves a fast convergence rate with only a constant number of rounds of communications. We also provide the asymptotic normality result for the purpose of statistical inference. To the best of our knowledge, this is the first normality result in the setting of Byzantine-robust distributed learning. The simulation results are also presented to illustrate the effectiveness of our method.

stat.ML↗

arXiv2021

Polar In-Plane Surface Orientation of a Ferroelectric Nematic Liquid Crystal: Polar Monodomains and Twisted State Electro-Optics

We show that surface interactions can vectorially structure the three-dimensional polariza-tion field of a ferroelectric fluid. The contact between a ferroelectric nematic liquid crystal and a surface with in-plane polarity generates a preferred in-plane orientation of the polarization field at that interface. This is a route to the formation of fluid or glassy monodomains of high polarization without the need for electric field poling. For example, unidirectional buffing of polyimide films on planar surfaces to give quadrupolar in-plane anisotropy also induces mac-roscopic in-plane polar order at the surfaces, enabling the formation of a variety of azimuthal polar director structures in the cell interior, including uniform and twisted states. In a π-twist cell, obtained with antiparallel, unidirectional buffing on opposing surfaces, we demonstrate three distinct modes of ferroelectric nematic electro-optic response: intrinsic, viscosity-limited, field-induced molecular reorientation; field-induced motion of domain walls separating twist-ed states of opposite chirality; and propagation of polarization reorientation solitons from the cell plates to the cell center upon field reversal. Chirally doped ferroelectric nematics in anti-parallel-rubbed cells produce Grandjean textures of helical twist that can be unwound via field-induced polar surface reorientation transitions. Fields required are in the 3 V/mm range, indicating an in-plane polar anchoring energy of wP ~ 3x10-3 J/m2.

cond-mat.soft↗

arXiv2021

Superconducting FeSe monolayer with milli-electron volt Fermi energy

Iron selenide (FeSe) is an iron-based superconductor which shows unique properties, including strongly anisotropic superconducting gap, paramagnetism in undoped compound and extremely small Fermi pocket size. In this work, we demonstrate that the sizes of electron and hole pockets in FeSe monolayer become much smaller than those in bulk. The Fermi energy is in the order of a few meV and can be fine-tuned by the thickness of graphene layers underneath. Despite the low carrier density, the FeSe monolayers grown on trilayer or multi-layer graphene are superconducting. The superconducting gap size is sensitive to the Fermi energy of the hole band. Remarkably, the FeSe monolayer provides the opportunity to study the physics in the crossover regime where the Fermi energy and superconducting gap are comparable to each other.

cond-mat.supr-con↗

arXiv2021

Dynamic Assortment Selection under the Nested Logit Models

We study a stylized dynamic assortment planning problem during a selling season of finite length $T$. At each time period, the seller offers an arriving customer an assortment of substitutable products and the customer makes the purchase among offered products according to a discrete choice model. The goal of the seller is to maximize the expected revenue, or equivalently, to minimize the worst-case expected regret. One key challenge is that utilities of products are unknown to the seller and need to be learned. Although the dynamic assortment planning problem has received increasing attention in revenue management, most existing work is based on the multinomial logit choice models (MNL). In this paper, we study the problem of dynamic assortment planning under a more general choice model -- the nested logit model, which models hierarchical choice behavior and is ``the most widely used member of the GEV (generalized extreme value) family''. By leveraging the revenue-ordered structure of the optimal assortment within each nest, we develop a novel upper confidence bound (UCB) policy with an aggregated estimation scheme. Our policy simultaneously learns customers' choice behavior and makes dynamic decisions on assortments based on the current knowledge. It achieves the accumulated regret at the order of $\tilde{O}(\sqrt{MNT})$, where $M$ is the number of nests and $N$ is the number of products in each nest. We further provide a lower bound result of $Ω(\sqrt{MT})$, which shows the near optimality of the upper bound when $T$ is much larger than $M$ and $N$. When the number of items per nest $N$ is large, we further provide a discretization heuristic for better performance of our algorithm. Numerical results are presented to demonstrate the empirical performance of our proposed algorithms.

stat.ML↗

arXiv2021

Some continuous and discontinuous Galerkin methods and structure preservation for incompressible flows

In this paper, we present consistent and inconsistent discontinuous Galerkin methods for incompressible Euler and Navier-Stokes equations with the kinematic pressure, Bernoulli function and EMAC function. Semi- and fully discrete energy stability of the proposed dG methods are proved in a unified fashion. Conservation of total energy, linear and angular momentum is discussed with both central and upwind fluxes. Numerical experiments are presented to demonstrate our findings and compare our schemes with conventional schemes in the literature in both unsteady and steady problems. Numerical results show that global conservation of the physical quantities may not be enough to demonstrate the performance of the schemes, and our schemes are competitive and able to capture essential physical features in several benchmark problems.

math.NA↗

arXiv2021

Experimental quantum simulation of superradiant phase transition beyond no-go theorem via antisqueezing

Superradiant phase transition (SPT) in thermal equilibrium, as a fundamental concept bridging the statistical physics and electrodynamics, can offer the key resources for quantum information science. Notwithstanding its fundamental and practical significances, equilibrium SPT has never been observed in experiments since the first proposal in the 1970s. Furthermore, the existence of equilibrium SPT in the cavity quantum electrodynamics (QED) systems is still subject of ongoing debates, due to the no-go theorem induced by the so-called A2 term. Based on the platform of nuclear magnetic resonance (NMR), here we experimentally demonstrate the occurrence of equilibrium SPT beyond no-go theorem by introducing the antisqueezing effect. The mechanism relies on the antisqueezing that recovers the singularity of the ground state via exponentially enhancing the zero point fluctuation (ZPF) of system. The strong entanglement and squeezed Schrodinger cat states of spins are achieved experimentally in the superradiant phase, which may play an important role in fundamental tests of quantum theory, implementing quantum metrology and high-efficient quantum information processing. Our experiment also shows the antisqueezing-enhanced signal-to-noise rate (SNR) of NMR spectrum, providing a general method for implementing high-precision NMR experiments.

quant-ph↗

arXiv2021

Fast-forward scaling of atom-molecule conversion in Bose-Einstein condensates

Robust stimulated Raman exact passages are requisite for controlling nonlinear quantum systems, with the wide applications ranging from ultracold molecules, non-linear optics to superchemistry. Inspired by shortcuts to adiabaticity, we propose the fast-forward scaling of stimulated Raman adiabatic processes with the nonlinearity involved, describing the transfer from an atomic Bose-Einstein condensate to a molecular one by controllable external fields. The fidelity and robustness of atom-molecule conversion are shown to surpass those of conventional adiabatic passages, assisted by fast-forward driving field. Finally, our results are extended to the fractional stimulated Raman adiabatic processes for the coherent superposition of atomic and molecular states.

cond-mat.quant-gas↗

arXiv2021

Learning a Product Relevance Model from Click-Through Data in E-Commerce

The search engine plays a fundamental role in online e-commerce systems, to help users find the products they want from the massive product collections. Relevance is an essential requirement for e-commerce search, since showing products that do not match search query intent will degrade user experience. With the existence of vocabulary gap between user language of queries and seller language of products, measuring semantic relevance is necessary and neural networks are engaged to address this task. However, semantic relevance is different from click-through rate prediction in that no direct training signal is available. Most previous attempts learn relevance models from user click-through data that are cheap and abundant. Unfortunately, click behavior is noisy and misleading, which is affected by not only relevance but also factors including price, image and attractive titles. Therefore, it is challenging but valuable to learn relevance models from click-through data. In this paper, we propose a new relevance learning framework that concentrates on how to train a relevance model from the weak supervision of click-through data. Different from previous efforts that treat samples as either relevant or irrelevant, we construct more fine-grained samples for training. We propose a novel way to consider samples of different relevance confidence, and come up with a new training objective to learn a robust relevance model with desirable score distribution. The proposed model is evaluated on offline annotated data and online A/B testing, and it achieves both promising performance and high computational efficiency. The model has already been deployed online, serving the search traffic of Taobao for over a year.

cs.IR↗

arXiv2021

Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main challenges are focused in this paper for online disease diagnosis: (1) serving cold-start users via graph convolutional networks and (2) handling scarce clinical description via a symptom retrieval system. To this end, we first organize the EHR data into a heterogeneous graph that is capable of modeling complex interactions among users, symptoms and diseases, and tailor the graph representation learning towards disease diagnosis with an inductive learning paradigm. Then, we build a disease self-diagnosis system with a corresponding EHR Graph-based Symptom Retrieval System (GraphRet) that can search and provide a list of relevant alternative symptoms by tracing the predefined meta-paths. GraphRet helps enrich the seed symptom set through the EHR graph when confronting users with scarce descriptions, hence yield better diagnosis accuracy. At last, we validate the superiority of our model on a large-scale EHR dataset.

cs.LG↗

arXiv2021

Shape-Enforcing Operators for Point and Interval Estimators

A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions. For example, distribution functions are nondecreasing and range between zero and one, height growth charts are nondecreasing in age, and production functions are nondecreasing and quasi-concave in input quantities. We propose a method to enforce these restrictions ex post on point and interval estimates of the target function by applying functional operators. If an operator satisfies certain properties that we make precise, the shape-enforced point estimates are closer to the target function than the original point estimates and the shape-enforced interval estimates have greater coverage and shorter length than the original interval estimates. We show that these properties hold for six different operators that cover commonly used shape restrictions in practice: range, convexity, monotonicity, monotone convexity, quasi-convexity, and monotone quasi-convexity. We illustrate the results with two empirical applications to the estimation of a height growth chart for infants in India and a production function for chemical firms in China.

econ.EM↗

arXiv2021

Does Probabilistic Constellation Shaping Benefit IM-DD Systems without Optical Amplifiers?

Probabilistic constellation shaping (PCS) has been widely applied to amplified coherent optical transmissions owing to its shaping gain over the uniform signaling and fine-grained rate adaptation to the underlying fiber channel condition. These merits stimulate the study of applying PCS to short-reach applications dominated by intensity modulation (IM) direct detection (DD) systems. As commercial IM-DD systems typically do not employ optical amplification to save the cost and power consumption, they are no longer subject to an average power constraint (APC) but a peak power constraint (PPC), which poses unique challenges to take full advantages of PCS. This paper provides a comprehensive investigation of PCS in IM-DD systems without optical amplifiers. In particular, we reveal that if the transmitter enhances the peak-to-average power ratio of the signal, a PPC system can be partially or even fully converted to an APC system in which the classical PCS offers its merits. The findings are verified through an IM-DD experiment using 4- and 8-ary pulse amplitude modulations.

eess.SP↗

arXiv2021

Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning

We give a nearly-optimal algorithm for testing uniformity of distributions supported on $\{-1,1\}^n$, which makes $\tilde O (\sqrt{n}/\varepsilon^2)$ queries to a subcube conditional sampling oracle (Bhattacharyya and Chakraborty (2018)). The key technical component is a natural notion of random restriction for distributions on $\{-1,1\}^n$, and a quantitative analysis of how such a restriction affects the mean vector of the distribution. Along the way, we consider the problem of mean testing with independent samples and provide a nearly-optimal algorithm.

cs.DS↗

arXiv2021

First-order Newton-type Estimator for Distributed Estimation and Inference

This paper studies distributed estimation and inference for a general statistical problem with a convex loss that could be non-differentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward Divide-and-Conquer Stochastic Gradient Descent (DC-SGD) approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes more stringent when the dimension $p$ is large. To overcome this limitation, this paper proposes a new multi-round distributed estimation procedure that approximates the Newton step only using stochastic subgradient. The key component in our method is the proposal of a computationally efficient estimator of $Σ^{-1} w$, where $Σ$ is the population Hessian matrix and $w$ is any given vector. Instead of estimating $Σ$ (or $Σ^{-1}$) that usually requires the second-order differentiability of the loss, the proposed First-Order Newton-type Estimator (FONE) directly estimates the vector of interest $Σ^{-1} w$ as a whole and is applicable to non-differentiable losses. Our estimator also facilitates the inference for the empirical risk minimizer. It turns out that the key term in the limiting covariance has the form of $Σ^{-1} w$, which can be estimated by FONE.

stat.ML↗

arXiv2021

Distributed Estimation for Principal Component Analysis: an Enlarged Eigenspace Analysis

The growing size of modern data sets brings many challenges to the existing statistical estimation approaches, which calls for new distributed methodologies. This paper studies distributed estimation for a fundamental statistical machine learning problem, principal component analysis (PCA). Despite the massive literature on top eigenvector estimation, much less is presented for the top-$L$-dim ($L>1$) eigenspace estimation, especially in a distributed manner. We propose a novel multi-round algorithm for constructing top-$L$-dim eigenspace for distributed data. Our algorithm takes advantage of shift-and-invert preconditioning and convex optimization. Our estimator is communication-efficient and achieves a fast convergence rate. In contrast to the existing divide-and-conquer algorithm, our approach has no restriction on the number of machines. Theoretically, the traditional Davis-Kahan theorem requires the explicit eigengap assumption to estimate the top-$L$-dim eigenspace. To abandon this eigengap assumption, we consider a new route in our analysis: instead of exactly identifying the top-$L$-dim eigenspace, we show that our estimator is able to cover the targeted top-$L$-dim population eigenspace. Our distributed algorithm can be applied to a wide range of statistical problems based on PCA, such as principal component regression and single index model. Finally, We provide simulation studies to demonstrate the performance of the proposed distributed estimator.

cs.DC↗