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Dan Li

Publications and source records attributed to Dan Li.

At least 199 records · Page 11Linked to original sources
arXiv2019

A Carleson problem for the Boussinesq operator

In this paper, Theorems 1.1- 1.2 show that the Boussinesq operator $\mathcal{B}_tf$ converges pointwise to its initial data $f\in H^s(\mathbb{R})$ as $t\to 0$ provided $s\geq\frac{1}{4}$ -- more precisely -- on the one hand, by constructing a counterexample in $\mathbb{R}$ we discover that the optimal convergence index $s_{c,1}=\frac14$; on the other hand, we find that the Hausdorff dimension of the disconvergence set for $\mathcal{B}_tf$ is \begin{align*} α_{1,\mathcal{B}}(s)&=\begin{cases} 1-2s&\ \ \text{as}\ \ \frac{1}{4}\leq s\leq\frac{1}{2};\\ 1 &\ \ \text{as}\ \ 0<s<\frac{1}{4}. \end{cases} \end{align*} Moreover, Theorem 1.3 presents a higher dimensional lift of Theorems 1.1- 1.2 under $f$ being radial.

math.CA↗
arXiv2019

Understanding Open Source Serverless Platforms: Design Considerations and Performance

Serverless computing is increasingly popular because of the promise of lower cost and the convenience it provides to users who do not need to focus on server management. This has resulted in the availability of a number of proprietary and open-source serverless solutions. We seek to understand how the performance of serverless computing depends on a number of design issues using several popular open-source serverless platforms. We identify the idiosyncrasies affecting performance (throughput and latency) for different open-source serverless platforms. Further, we observe that just having either resource-based (CPU and memory) or workload-based (request per second (RPS) or concurrent requests) auto-scaling is inadequate to address the needs of the serverless platforms.

cs.PF↗
arXiv2019

Nanoconfined, dynamic electrolyte gating and memory effects in multilayered graphene-based membranes

Multilayered graphene-based nanoporous membranes with electrolyte incorporated between individual sheets is a unique nano-heterostructure system in which nanoconfined electrons in graphene and ions confined in between sheets are intimately coupled throughout the entire membrane. In contrast to the general notion that the electrolyte gating is unlikely to appear in multilayered graphene stacks, it is demonstrated in this work that the electrolyte gating effect in monolayer graphene can be transferred to its corresponding multilayered porous membranes. This gating effect presented on each individual graphene sheets through electrolyte confined in nanopores provides a real-time, electrical approach for probing the complex dynamics of nanoconfined electrical double layer. This has enabled the observation of the ionic memory effect in supercapacitors and produces new insights into the charging dynamics of supercapacitors. Such discoveries may stimulate the design of novel nanoionic devices.

cond-mat.mes-hall↗
arXiv2019

ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment

Automatic Speech Recognition (ASR) is greatly developed in recent years, which expedites many applications on other fields. For the ASR research, speech corpus is always an essential foundation, especially for the vertical industry, such as Air Traffic Control (ATC). There are some speech corpora for common applications, public or paid. However, for the ATC, it is difficult to collect raw speeches from real systems due to safety issues. More importantly, for a supervised learning task like ASR, annotating the transcription is a more laborious work, which hugely restricts the prospect of ASR application. In this paper, a multilingual speech corpus (ATCSpeech) from real ATC systems, including accented Mandarin Chinese and English, is built and released to encourage the non-commercial ASR research in ATC domain. The corpus is detailly introduced from the perspective of data amount, speaker gender and role, speech quality and other attributions. In addition, the performance of our baseline ASR models is also reported. A community edition for our speech database can be applied and used under a special contrast. To our best knowledge, this is the first work that aims at building a real and multilingual ASR corpus for the air traffic related research.

cs.CL↗
arXiv2019

A Primal-dual weak Galerkin finite element method for linear convection equations in non-divergence form

A new primal-dual weak Galerkin (PD-WG) finite element method was developed and analyzed in this article for first-order linear convection equations in non-divergence form. The PD-WG method results in a symmetric discrete system involving not only the original equation for the primal variable, but also the dual/adjoint equation for the dual variable (also known as Lagrangian multiplier). Optimal order of error estimates in various discrete Sobolev norms are derived for the numerical solutions arising from the PD-WG scheme. Numerical results are produced and reported to illustrate the accuracy and effectiveness of the new PD-WG method.

math.NA↗
arXiv2019

Improving the Thermal Stability of a CCD Through Clocking

Modern precise radial velocity spectrometers are designed to infer the existence of planets orbiting other stars by measuring few-nm shifts in the positions of stellar spectral lines recorded at high spectral resolution on a large-area digital detector. While the spectrometer may be highly stabilized in terms of temperature, the detector itself may undergo changes in temperature during readout that are an order of magnitude or more larger than the other opto-mechanical components within the instrument. These variations in detector temperature can translate directly into systematic measurement errors. We explore a technique for reducing the amplitude of CCD temperature variations by shuffling charge within a pixel in the parallel direction during integration. We find that this "dither clocking" mode greatly reduces temperature variations in the CCDs being tested for the NEID spectrometer. We investigate several potential negative effects this clocking scheme could have on the underlying spectral data.

astro-ph.IM↗
arXiv2019

LabelECG: A Web-based Tool for Distributed Electrocardiogram Annotation

Electrocardiography plays an essential role in diagnosing and screening cardiovascular diseases in daily healthcare. Deep neural networks have shown the potentials to improve the accuracies of arrhythmia detection based on electrocardiograms (ECGs). However, more ECG records with ground truth are needed to promote the development and progression of deep learning techniques in automatic ECG analysis. Here we propose a web-based tool for ECG viewing and annotating, LabelECG. With the facilitation of unified data management, LabelECG is able to distribute large cohorts of ECGs to dozens of technicians and physicians, who can simultaneously make annotations through web-browsers on PCs, tablets and cell phones. Along with the doctors from four hospitals in China, we applied LabelECG to support the annotations of about 15,000 12-lead resting ECG records in three months. These annotated ECGs have successfully supported the First China ECG intelligent Competition. La-belECG will be freely accessible on the Internet to support similar researches, and will also be upgraded through future works.

cs.DB↗
arXiv2019

Convergence of a Class of Schrödinger Equations

In this paper, we set up the selection conditions for time series $\{t_k\}_{k=1}^\infty$ which converge to 0 as $k\rightarrow\infty$ such that the solutions of a class of generalized Schrödinger equations almost everywhere pointwise converge to their initial data in $H^s(\mathbb{R}^n)$ for $s>0$. As it is known that the pointwise convergence can not be true for Schrödinger equation when $s<\frac{n}{2(n+1)}$ as $t\rightarrow0$.

math.CA↗
arXiv2019

The equations of motion for consistency of a post-Newtonian Lagrangian formulation

Equations of motion for a general relativistic post-Newtonian Lagrangian approach mainly refer to acceleration equations, i.e. differential equations of velocities. They are directly from the Euler-Lagrangian equations, and usually have higher-order terms truncated when they remain at the same post-Newtonian order of the Lagrangian. In this sense, they are incoherent equations of the Lagrangian and approximately conserve constants of motion in this system. In this paper, we show that the Euler-Lagrangian equations can also yield the equations of motion for consistency of the Lagrangian in the general case. The coherent equations are the \emph{differential} equations of generalized momenta rather than those of the velocities, and have no terms truncated. The velocities are not integration variables, but they can be solved from the \emph{algebraic} equations of the generalized momenta with an iterative method. Taking weak relativistic fields in the Solar System and strong relativistic fields of compact objects as examples, we numerically evaluate the accuracies of the constants of motion in the two sets of equations of motion. It is confirmed that these accuracies well satisfy the theoretical need if the chosen integrator can provide a high enough precision. The differences in the dynamical behavior of order and chaos between the two sets of equations are also compared. Unlike the incoherent post-Newtonian Lagrangian equations of motion, the coherent ones can theoretically, strictly conserve all integrals in some post-Newtonian Lagrangian problems, and therefore are worth recommending.

gr-qc↗
arXiv2019

Szegedy Quantum Walks with Memory on Regular Graphs

Quantum walks with memory(QWM) are a type of modified quantum walks that record the walker's latest path. The general model of coined QWM is presented in Phys. Rev. A 93, 042323 (2016). In this paper, we present general model of Szegedy QWM. Importantly, the relation of coined QWM and Szegedy QWM is revealed. By transforming coined QWM to Szegedy QWM, some amazing results about QWM are founded.

quant-ph↗
arXiv2019

Polarization in Disks

Polarized dust emission outside of disks reveal the magnetic field morphology of molecular clouds. Within disks, however, polarized dust emission can arise from very different mechanisms (e.g., self-scattering), and each of them are useful for constraining physical properties in the disk. For example, these mechanisms allow us to constrain the disk grain size distributions and grain/disk geometries, independent from current methods of measuring these parameters. To accurately model these features and disentangle the various polarization mechanisms, multiwavelength observations at very high resolution and sensitivity are required. With significant upgrades to current interferometric facilities, we can understand how grains evolve in disks during the planet formation process.

astro-ph.SR↗
arXiv2019

Quantum algorithm and quantum circuit for A-Optimal Projection: dimensionality reduction

Learning low dimensional representation is a crucial issue for many machine learning tasks such as pattern recognition and image retrieval. In this article, we present a quantum algorithm and a quantum circuit to efficiently perform A-Optimal Projection for dimensionality reduction. Compared with the best-know classical algorithms, the quantum A-Optimal Projection (QAOP) algorithm shows an exponential speedup in both the original feature space dimension $n$ and the reduced feature space dimension $k$. We show that the space and time complexity of the QAOP circuit are $O\left[ {{{\log }_2}\left( {nk} /ε \right)} \right]$ and $O[ {\log_2(nk)} {poly}\left({{\log }_2}ε^{-1} \right)]$ respectively, with fidelity at least $1-ε$. Firstly, a reformation of the original QAOP algorithm is proposed to help omit the quantum-classical interactions during the QAOP algorithm. Then the quantum algorithm and quantum circuit with performance guarantees are proposed. Specifically, the quantum circuit modules for preparing the initial quantum state and implementing the controlled rotation can be also used for other quantum machine learning algorithms.

quant-ph↗
arXiv2019

Ultra-Stable Environment Control for the NEID Spectrometer: Design and Performance Demonstration

Two key areas of emphasis in contemporary experimental exoplanet science are the detailed characterization of transiting terrestrial planets, and the search for Earth analog planets to be targeted by future imaging missions. Both of these pursuits are dependent on an order-of-magnitude improvement in the measurement of stellar radial velocities (RV), setting a requirement on single-measurement instrumental uncertainty of order 10 cm/s. Achieving such extraordinary precision on a high-resolution spectrometer requires thermo-mechanically stabilizing the instrument to unprecedented levels. Here, we describe the Environment Control System (ECS) of the NEID Spectrometer, which will be commissioned on the 3.5 m WIYN Telescope at Kitt Peak National Observatory in 2019, and has a performance specification of on-sky RV precision < 50 cm/s. Because NEID's optical table and mounts are made from aluminum, which has a high coefficient of thermal expansion, sub-milliKelvin temperature control is especially critical. NEID inherits its ECS from that of the Habitable-zone Planet Finder (HPF), but with modifications for improved performance and operation near room temperature. Our full-system stability test shows the NEID system exceeds the already impressive performance of HPF, maintaining vacuum pressures below $10^{-6}$ Torr and an RMS temperature stability better than 0.4 mK over 30 days. Our ECS design is fully open-source; the design of our temperature-controlled vacuum chamber has already been made public, and here we release the electrical schematics for our custom Temperature Monitoring and Control (TMC) system.

astro-ph.IM↗
arXiv2019

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. The approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data.

cs.LG↗
arXiv2019

Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks

Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is known that the performance of supervised learning approaches highly relies on the availability and quality of labeled training data. In Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient fault data has posed a major challenge to applying these supervised learning techniques to commercial buildings. To overcome this challenge, this paper proposes using Monte Carlo dropout (MC-dropout) to enhance the supervised learning pipeline, so that the resulting neural network is able to detect and diagnose unseen incipient fault examples. We also examine the proposed MC-dropout method on the RP-1043 dataset to demonstrate its effectiveness in indicating the most likely incipient fault types.

cs.LG↗
arXiv2019

The relative efficiency of time-to-progression and continuous measures of cognition in pre-symptomatic Alzheimer's

Pre-symptomatic (or Preclinical) Alzheimer's Disease is defined by biomarker evidence of fibrillar amyloid beta pathology in the absence of clinical symptoms. Clinical trials in this early phase of disease are challenging due to the slow rate of disease progression as measured by periodic cognitive performance tests or by transition to a diagnosis of Mild Cognitive Impairment. In a multisite study, experts provide diagnoses by central chart review without the benefit of in-person assessment. We use a simulation study to demonstrate that models of repeated cognitive assessments detect treatment effects more efficiently compared to models of time-to-progression to an endpoint such as change in diagnosis. Multivariate continuous data are simulated from a Bayesian joint mixed effects model fit to data from the Alzheimer's Disease Neuroimaging Initiative. Simulated progression events are algorithmically derived from the continuous assessments using a random forest model fit to the same data. We find that power is approximately doubled with models of repeated continuous outcomes compared to the time-to-progression analysis. The simulations also demonstrate that a plausible informative missing data pattern can induce a bias which inflates treatment effects, yet 5% Type I error is maintained.

stat.AP↗
arXiv2019

Efficient quantum circuit for singular value thresholding

Singular value thresholding (SVT) operation is a fundamental core module in many mathematical models in computer vision and machine learning, particularly for many nuclear norm minimizing-based problems. We presented a quantum SVT (QSVT) algorithm which was used as a subroutine to address an image classification problem. This algorithm runs in $O\left[\log\left(pq\right)\right]$, an exponential speed improvement over the classical algorithm which runs in $O\left[poly\left(pq\right)\right]$. In this study, we investigate this algorithm and design a scalable quantum circuit for QSVT. In the circuit design, we introduce an adjustable parameter $α$ to ensure the high probability of obtaining the final result and the high fidelity of actual and ideal final states. We also show that the value of $α$ can be computed ahead of implementing the quantum circuit when the inputs of the QSVT algorithm, i.e. matrix ${\bf{A}}$ and constant $τ$, are given. In addition, we propose a small-scale quantum circuit for QSVT. We numerically simulate and demonstrate the performance of this circuit, verifying its capability to solve the intended SVT. The quantum circuit for QSVT implies a tempting possibility for experimental realization on a quantum computer.

quant-ph↗