arXiv subjects

Xinyan Li

Publications and source records attributed to Xinyan Li.

31 records · Page 2Linked to original sources

arXiv2023

"Nice to meet you!": Expressing Emotions with Movement Gestures and Textual Content in Automatic Handwriting Robots

Text-writing robots have been used in assistive writing and drawing applications. However, robots do not convey emotional tones in the writing process due to the lack of behaviors humans typically adopt. To examine how people interpret designed robotic expressions of emotion through both movements and textual output, we used a pen-plotting robot to generate texts by performing human-like behaviors like stop-and-go, speed, and pressure variation. We examined how people convey emotion in the writing process by observing how they wrote in different emotional contexts. We then mapped these human expressions during writing to the handwriting robot and measured how well other participants understood the robot's affective expression. We found that textual output was the strongest determinant of participants' ability to perceive the robot's emotions, whereas parameters of gestural movements of the robots like speed, fluency, pressure, size, and acceleration could be useful for understanding the context of the writing expression.

cs.HC↗

arXiv2023

Miniature Magnetic Nano islands in a Morphotropic Cobaltite Matrix

High-density magnetic memories are key components in spintronics, quantum computing, and energy-efficient electronics. Reduced dimensionality and magnetic domain stability at the nanoscale are essential for the miniaturization of magnetic storage units. Yet, inducing magnetic order, and selectively tuning spin-orbital coupling at specific locations have remained challenging. Here we demonstrate the construction of switchable magnetic nano-islands in a nonmagnetic matrix based on cobaltite homo-structures. The magnetic and electronic states are laterally modified by epitaxial strain, which is regionally controlled by freestanding membranes. Atomically sharp grain boundaries isolate the crosstalk between magnetically distinct regions. The minimal size of magnetic nano-islands reaches 35 nm in diameter, enabling an areal density of 400 Gbit per inch square. Besides providing an ideal platform for precisely controlled read and write schemes, this methodology can enable scalable and patterned memories on silicon and flexible substrates for various applications.

cond-mat.mtrl-sci↗

arXiv2022

Stability Based Generalization Bounds for Exponential Family Langevin Dynamics

Recent years have seen advances in generalization bounds for noisy stochastic algorithms, especially stochastic gradient Langevin dynamics (SGLD) based on stability (Mou et al., 2018; Li et al., 2020) and information theoretic approaches (Xu and Raginsky, 2017; Negrea et al., 2019; Steinke and Zakynthinou, 2020). In this paper, we unify and substantially generalize stability based generalization bounds and make three technical contributions. First, we bound the generalization error in terms of expected (not uniform) stability which arguably leads to quantitatively sharper bounds. Second, as our main contribution, we introduce Exponential Family Langevin Dynamics (EFLD), a substantial generalization of SGLD, which includes noisy versions of Sign-SGD and quantized SGD as special cases. We establish data-dependent expected stability based generalization bounds for any EFLD algorithm with a O(1/n) sample dependence and dependence on gradient discrepancy rather than the norm of gradients, yielding significantly sharper bounds. Third, we establish optimization guarantees for special cases of EFLD. Further, empirical results on benchmarks illustrate that our bounds are non-vacuous, quantitatively sharper than existing bounds, and behave correctly under noisy labels.

cs.LG↗

arXiv2022

An Empirical Study on How Well Do COVID-19 Information Dashboards Service Users' Information Needs

The ongoing COVID-19 pandemic highlights the importance of dashboards for providing critical real-time information. In order to enable people to obtain information in time and to understand complex statistical data, many developers have designed and implemented public-oriented COVID-19 "information dashboards" during the pandemic. However, development often takes a long time and developers are not clear about many people's information needs, resulting in gaps between information needs and supplies. According to our empirical study and observations with popular developed COVID-19 dashboards, this seriously impedes information acquirement. Our study compares people's needs on Twitter with existing information suppliers. We determine that despite the COVID-19 information that is currently on existing dashboards, people are also interested in the relationship between COVID-19 and other viruses, the origin of COVID-19, vaccine development, fake new about COVID-19, impact on women, impact on school/university, and impact on business. Most of these have not yet been well addressed. We also summarise the visualization and interaction patterns commonly applied in dashboards, finding key patterns between data and visualization as well as visualization and interaction. Our findings can help developers to better optimize their dashboard to meet people's needs and make improvements to future crisis management dashboard development.

cs.HC↗

arXiv2021

Wafer-Scale Epitaxy of Flexible Nitride Films with Superior Plasmonic and Superconducting Performance

Transition-metal nitrides (e.g., TiN, ZrN, TaN) are incredible materials with excellent complementary-metal-oxide-semiconductor compatibility and remarkable performance in refractory plasmonics and superconducting quantum electronics. Epitaxial growth of flexible transition-metal nitride films, especially at wafer-scale, is fundamentally important for developing high-performance flexible photonics and superconducting electronics, but the study is rare thus far. This work reports the high-quality epitaxy of 2-inch titanium nitride (TiN) films on flexible fluorophlogopite-mica (F-mica) substrates via reactive magnetron sputtering. Combined measurements of spectroscopic ellipsometer and electrical transport reveal the superior plasmonic and superconducting performance of TiN/F-mica films owing to the high single crystallinity. More interestingly, the superconductivity of these flexible TiN films can be manipulated by the bending states, and enhanced superconducting critical temperature TC is observed in convex TiN films with in-plane tensile strain. Density-functional-theory calculations uncover that the strain can tune the electron-phonon interaction strength and resultant superconductivity of TiN films. This study provides a promising route towards integrating scalable single-crystalline conductive transition-metal nitride films with flexible electronics for high-performance plasmonics and superconducting electronics.

cond-mat.supr-con↗

arXiv2021

Dynamics of anisotropic oxygen-ion migration in strained cobaltites

Orientation control of oxygen vacancy channel (OVC) is a highly desirable for tailoring oxygen diffusion as it serves fast transport channel in ion conductors, which is widespread exploited in solid-state fuel cells, catalysts, and ion-batteries. Direct observation of oxygen-ions hopping towards preferential vacant sites is a key to clarifying migration pathways. Here we report the anisotropic oxygen-ion migration mediated by strain in ultrathin cobaltites via in-situ thermal activation in an atomic-resolved transmission electron microscopy. Oxygen migration pathways are constructed on the basis of the atomic structure during the OVC switching, which is manifested as the vertical-to-horizontal OVC switching under tensile strain, but the horizontal-to-diagonal switching under compression. We evaluate the topotactic structural changes to OVC, determine the crucial role of tolerance factor for OVC stability and establish the strain-dependent phase diagram. Our work provides a practical guide for engineering OVC orientation that is applicable ionic-oxide electronics.

cond-mat.mtrl-sci↗

arXiv2021

Noisy Truncated SGD: Optimization and Generalization

Recent empirical work on stochastic gradient descent (SGD) applied to over-parameterized deep learning has shown that most gradient components over epochs are quite small. Inspired by such observations, we rigorously study properties of Truncated SGD (T-SGD), that truncates the majority of small gradient components to zeros. Considering non-convex optimization problems, we show that the convergence rate of T-SGD matches the order of vanilla SGD. We also establish the generalization error bound for T-SGD. Further, we propose Noisy Truncated SGD (NT-SGD), which adds Gaussian noise to the truncated gradients. We prove that NT-SGD has the same convergence rate as T-SGD for non-convex optimization problems. We demonstrate that with the help of noise, NT-SGD can provably escape from saddle points and requires less noise compared to previous related work. We also prove that NT-SGD achieves better generalization error bound compared to T-SGD because of the noise. Our generalization analysis is based on uniform stability and we show that additional noise in the gradient update can boost the stability. Our experiments on a variety of benchmark datasets (MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100) with various networks (VGG and ResNet) validate the theoretical properties of NT-SGD, i.e., NT-SGD matches the speed and accuracy of vanilla SGD while effectively working with sparse gradients, and can successfully escape poor local minima.

cs.LG↗

arXiv2021

Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration

Sub-seasonal climate forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to advance SSF. In this paper, for the first time, we perform a fine-grained comparison of a suite of modern ML models with start-of-the-art physics-based dynamical models from the Subseasonal Experiment (SubX) project for SSF in the western contiguous United States. Additionally, we explore mechanisms to enhance the ML models by using forecasts from dynamical models. Empirical results illustrate that, on average, ML models outperform dynamical models while the ML models tend to be conservatives in their forecasts compared to the SubX models. Further, we illustrate that ML models make forecasting errors under extreme weather conditions, e.g., cold waves due to the polar vortex, highlighting the need for separate models for extreme events. Finally, we show that suitably incorporating dynamical model forecasts as inputs to ML models can substantially improve the forecasting performance of the ML models. The SSF dataset constructed for the work, dynamical model predictions, and code for the ML models are released along with the paper for the benefit of the broader machine learning community.

physics.ao-ph↗

arXiv2021

One-step epitaxy of high-mobility La-doped BaSnO3 films by high-pressure magnetron sputtering

As a unique perovskite transparent oxide semiconductor, high-mobility La-doped BaSnO3 films have been successfully synthesized by molecular beam epitaxy and pulsed laser deposition. However, it remains a big challenge for magnetron sputtering, a widely applied technique suitable for large-scale fabrication, to grow high-mobility La-doped BaSnO3 films. Here, we developed a method to synthesize high-mobility epitaxial La-doped BaSnO3 films (mobility up to 121 cm2V-1s-1 at the carrier density ~ 4.0 x 10^20 cm-3 at room temperature) directly on SrTiO3 single crystal substrates using high-pressure magnetron sputtering. The structural and electrical properties of the La-doped BaSnO3 films were characterized by combined high-resolution X-ray diffraction, X-ray photoemission spectroscopy, and temperature-dependent electrical transport measurements. The room temperature electron mobility of La-doped BaSnO3 films in this work is 2 to 4 times higher than the reported values of the films grown by magnetron sputtering. Moreover, in the high carrier density range (n > 3 x 10^20 cm-3), the electron mobility value of 121 cm2V-1s-1 in our work is among the highest values for all reported doped BaSnO3 films. It is revealed that high argon pressure during sputtering plays a vital role in stabilizing the fully relaxed films and inducing oxygen vacancies, which benefit the high mobility at room temperature. Our work provides an easy and economical way to massively synthesize high-mobility transparent conducting films for transparent electronics.

cond-mat.mtrl-sci↗

arXiv2021

Experiments with Rich Regime Training for Deep Learning

In spite of advances in understanding lazy training, recent work attributes the practical success of deep learning to the rich regime with complex inductive bias. In this paper, we study rich regime training empirically with benchmark datasets, and find that while most parameters are lazy, there is always a small number of active parameters which change quite a bit during training. We show that re-initializing (resetting to their initial random values) the active parameters leads to worse generalization. Further, we show that most of the active parameters are in the bottom layers, close to the input, especially as the networks become wider. Based on such observations, we study static Layer-Wise Sparse (LWS) SGD, which only updates some subsets of layers. We find that only updating the top and bottom layers have good generalization and, as expected, only updating the top layers yields a fast algorithm. Inspired by this, we investigate probabilistic LWS-SGD, which mostly updates the top layers and occasionally updates the full network. We show that probabilistic LWS-SGD matches the generalization performance of vanilla SGD and the back-propagation time can be 2-5 times more efficient.

cs.LG↗

arXiv2020

Cloud detection in Landsat-8 imagery in Google Earth Engine based on a deep neural network

Google Earth Engine (GEE) provides a convenient platform for applications based on optical satellite imagery of large areas. With such data sets, the detection of cloud is often a necessary prerequisite step. Recently, deep learning-based cloud detection methods have shown their potential for cloud detection but they can only be applied locally, leading to inefficient data downloading time and storage problems. This letter proposes a method to directly perform cloud detection in Landsat-8 imagery in GEE based on deep learning (DeepGEE-CD). A deep neural network (DNN) was first trained locally, and then the trained DNN was deployed in the JavaScript client of GEE. An experiment was undertaken to validate the proposed method with a set of Landsat-8 images and the results show that DeepGEE-CD outperformed the widely used function of mask (Fmask) algorithm. The proposed DeepGEE-CD approach can accurately detect cloud in Landsat-8 imagery without downloading it, making it a promising method for routine cloud detection of Landsat-8 imagery in GEE.

eess.IV↗

arXiv2020

Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances

Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would have immense societal value, in areas such as agricultural productivity, water resource management, transportation and aviation systems, and emergency planning for extreme weather events. However, SSF is considered more challenging than either weather prediction or even seasonal prediction. In this paper, we carefully study a variety of machine learning (ML) approaches for SSF over the US mainland. While atmosphere-land-ocean couplings and the limited amount of good quality data makes it hard to apply black-box ML naively, we show that with carefully constructed feature representations, even linear regression models, e.g., Lasso, can be made to perform well. Among a broad suite of 10 ML approaches considered, gradient boosting performs the best, and deep learning (DL) methods show some promise with careful architecture choices. Overall, suitable ML methods are able to outperform the climatological baseline, i.e., predictions based on the 30-year average at a given location and time. Further, based on studying feature importance, ocean (especially indices based on climatic oscillations such as El Nino) and land (soil moisture) covariates are found to be predictive, whereas atmospheric covariates are not considered helpful.

cs.LG↗

arXiv2019

Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization

While stochastic gradient descent (SGD) and variants have been surprisingly successful for training deep nets, several aspects of the optimization dynamics and generalization are still not well understood. In this paper, we present new empirical observations and theoretical results on both the optimization dynamics and generalization behavior of SGD for deep nets based on the Hessian of the training loss and associated quantities. We consider three specific research questions: (1) what is the relationship between the Hessian of the loss and the second moment of stochastic gradients (SGs)? (2) how can we characterize the stochastic optimization dynamics of SGD with fixed and adaptive step sizes and diagonal pre-conditioning based on the first and second moments of SGs? and (3) how can we characterize a scale-invariant generalization bound of deep nets based on the Hessian of the loss, which by itself is not scale invariant? We shed light on these three questions using theoretical results supported by extensive empirical observations, with experiments on synthetic data, MNIST, and CIFAR-10, with different batch sizes, and with different difficulty levels by synthetically adding random labels.

cs.LG↗