arXiv subjects

Xin Qian

Publications and source records attributed to Xin Qian.

At least 55 records · Page 3Linked to original sources

arXiv2021

Casimir force among spheres made of Weyl semimetals breaking Lorentz reciprocity

The Casimir force and thermal Casimir force originating from quantum electromagnetic fluctuations at zero and non-zero temperatures, respectively, are significant in nano- and microscale systems and are well-understood. Less understood, however, are the Casimir and thermal Casimir forces in systems breaking Lorentz reciprocity. In this work, we derive a formalism for thermal Casimir forces between an arbitrary number of spheres based on fluctuational electrodynamics and scattering theory without the assumption of Lorentz reciprocity. We study the total Casimir force in systems of two and three Weyl semimetal spheres with time-reversal symmetry breaking for different orientations of the momentum-space separation of Weyl nodes in both thermal equilibrium and nonequilibrium. In thermal nonequilibrium, we show that a net thermal Casimir force exists not only along the center-to-center displacements of the spheres, but also in the transverse direction to it due to thermal emission with non-zero angular momentum. Different symmetries of the system drive a variety of dynamics such as global rotations, self-propulsion, and spinning of the spheres. We also show that the Casimir energy in thermal equilibrium depends on the orientations of the Weyl node directions in the spheres and that the lateral Casimir force will act between the spheres even in thermal equilibrium to relax the system into the minimum energy state without transferring net energy and momentum to the environment. The developed framework opens a way for investigating many-body dynamics by Casimir and thermal Casimir forces among arbitrary number of spheres with arbitrary dielectric function tensors in both thermal equilibrium and nonequilibrium.

cond-mat.mes-hall↗

arXiv2021

Machine Learning for Predicting Thermal Transport Properties of Solids

Quantitative descriptions of the structure-thermal property correlation have been a bottleneck in designing materials with superb thermal properties. In the past decade, the first-principles phonon calculations using density functional theory and the Boltzmann transport equation have become a common practice for predicting the thermal conductivity of new materials. However, first-principles calculations are too costly for high-throughput material screening and multi-scale structural design. First-principles calculations also face several fundamental challenges in modeling thermal transport properties, e.g., of crystalline materials with defects, of amorphous materials, and for materials at high temperatures. In the past five years, machine learning started to play a role in solving these challenges. This review provides a comprehensive summary and discussion on the state-of-the-art, future opportunities, and the remaining challenges in implementing machine learning for studying thermal conductivity. After an introduction to the working principles of machine learning and descriptors of material structures, recent research using machine learning to study thermal transport is discussed. Three major applications of machine learning for predicting thermal properties are discussed. First, machine learning is applied to solve the challenges in modeling phonon transport of crystals with defects, in amorphous materials, and at high temperatures. Machine learning is used to build high-fidelity interatomic potentials to bridge the gap between first-principles calculations and molecular dynamics simulations. Second, machine learning can be used to study the correlation between thermal conductivity and other properties for high-throughput materials screening. Finally, machine learning is a powerful tool for structural design to achieve target thermal conductance or thermal conductivity.

cond-mat.mtrl-sci↗

arXiv2021

A Probabilistic Approach to Neural Network Pruning

Neural network pruning techniques reduce the number of parameters without compromising predicting ability of a network. Many algorithms have been developed for pruning both over-parameterized fully-connected networks (FCNs) and convolutional neural networks (CNNs), but analytical studies of capabilities and compression ratios of such pruned sub-networks are lacking. We theoretically study the performance of two pruning techniques (random and magnitude-based) on FCNs and CNNs. Given a target network {whose weights are independently sampled from appropriate distributions}, we provide a universal approach to bound the gap between a pruned and the target network in a probabilistic sense. The results establish that there exist pruned networks with expressive power within any specified bound from the target network.

cs.LG↗

arXiv2021

ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives

Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline which characterizes charts by their properties and similarity, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.

cs.HC↗

arXiv2021

Complex Factoid Question Answering with a Free-Text Knowledge Graph

We introduce DELFT, a factoid question answering system which combines the nuance and depth of knowledge graph question answering approaches with the broader coverage of free-text. DELFT builds a free-text knowledge graph from Wikipedia, with entities as nodes and sentences in which entities co-occur as edges. For each question, DELFT finds the subgraph linking question entity nodes to candidates using text sentences as edges, creating a dense and high coverage semantic graph. A novel graph neural network reasons over the free-text graph-combining evidence on the nodes via information along edge sentences-to select a final answer. Experiments on three question answering datasets show DELFT can answer entity-rich questions better than machine reading based models, bert-based answer ranking and memory networks. DELFT's advantage comes from both the high coverage of its free-text knowledge graph-more than double that of dbpedia relations-and the novel graph neural network which reasons on the rich but noisy free-text evidence.

cs.CL↗

arXiv2021

Thermally Regenerative Flow Batteries with pH Neutral Electrolytes for Harvesting Low-Grade Heat

Harvesting waste heat with temperatures lower than 100 oC can improve system efficiency and reduce greenhouse gas emissions, yet it has been a longstanding and challenging task. Electrochemical methods for harvesting low-grade heat have attracted research interest in recent years due to the relatively high effective temperature coefficient of the electrolytes (> 1 mV/K) compared with the thermopower of traditional thermoelectric devices. Comparing with other electrochemical devices such as temperature-variation based thermally regenerative electrochemical cycle and temperature-difference based thermogalvanic cells, the thermally regenerative flow battery (TRFB) has the advantages of providing a continuous power output, decoupling the heat source and heat sink and recuperating heat, and compatible with stacking for scaling up. However, TRFB suffers from the issue of stable operation due to the challenge of pH matching between catholyte and anolyte solutions with desirable temperature coefficients. In this work, we demonstrate a PH-neutral TRFB based on KI/KI3 and K3Fe(CN)6/K4Fe(CN)6 as the catholyte and anolyte, respectively, with a cell temperature coefficient of 1.9 mV/K and a power density of 9 uW/cm2. This work also presents a comprehensive model with a coupled analysis of mass transfer and reaction kinetics in a porous electrode that can accurately capture the flow rate dependence of power density and energy conversion efficiency. We estimate that the efficiency of the pH-neutral TRFB can reach 11% of the Carnot efficiency at the maximum power output with a temperature difference of 37 K. Via analysis, we identify that the mass transfer overpotential inside the porous electrode and the resistance of the ion exchange membrane are the two major factors limiting the efficiency and power density, pointing to directions for future improvements.

physics.app-ph↗

arXiv2021

Modeling impurity concentrations in liquid argon detectors

Impurities in noble liquid detectors used for neutrino and dark matter experiments can significantly impact the quality of data. We present an experimentally verified model for describing the dynamics of impurity distributions in liquid argon (LAr) detectors. The model considers sources, sinks, and transport of impurities within and between the gas and liquid argon phases. Measurements of oxygen concentrations in a 20-L LAr multi-purpose test stand are compared to calculations made with this model to show that an accurate description of the concentrations under various operational conditions can be obtained. A result of this analysis is a determination of Henry's coefficient for oxygen in LAr. These calculations also show that some processes have small effects on the impurity dynamics and excluding them yields a solution as a sum of two exponential terms. This solution provides a simple way to extract Henry's coefficient with negligible approximation error. It is applied to the data and the Henry's coefficient for oxygen in LAr is obtained as 0.84$^{+0.09}_{-0.05}$, consistent with literature results. Based on the analysis of the data with the model, we further suggest that, for a large liquid argon detector, barriers to flow ("baffles") installed in the gas phase to restrict flow can help reduce the ultimate impurity concentration in the LAr.

physics.ins-det↗

arXiv2021

Personalized Visualization Recommendation

Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback. These systems recommend the same visualizations for every user, despite that the underlying user interests, intent, and visualization preferences are likely to be fundamentally different, yet vitally important. In this work, we formally introduce the problem of personalized visualization recommendation and present a generic learning framework for solving it. In particular, we focus on recommending visualizations personalized for each individual user based on their past visualization interactions (e.g., viewed, clicked, manually created) along with the data from those visualizations. More importantly, the framework can learn from visualizations relevant to other users, even if the visualizations are generated from completely different datasets. Experiments demonstrate the effectiveness of the approach as it leads to higher quality visualization recommendations tailored to the specific user intent and preferences. To support research on this new problem, we release our user-centric visualization corpus consisting of 17.4k users exploring 94k datasets with 2.3 million attributes and 32k user-generated visualizations.

cs.IR↗

arXiv2020

Augmented Signal Processing in Liquid Argon Time Projection Chambers with a Deep Neural Network

The Liquid Argon Time Projection Chamber (LArTPC) is an advanced neutrino detector technology widely used in recent and upcoming accelerator neutrino experiments. It features a low energy threshold and high spatial resolution that allow for comprehensive reconstruction of event topologies. In current-generation LArTPCs, the recorded data consist of digitized waveforms on wires produced by induced signal on wires of drifting ionization electrons, which can also be viewed as two-dimensional (2D) (time versus wire) projection images of charged-particle trajectories. For such an imaging detector, one critical step is the signal processing that reconstructs the original charge projections from the recorded 2D images. For the first time, we introduce a deep neural network in LArTPC signal processing to improve the signal region of interest detection. By combining domain knowledge (e.g., matching information from multiple wire planes) and deep learning, this method shows significant improvements over traditional methods. This work details the method, software tools, and performance evaluated with realistic detector simulations.

physics.ins-det↗

arXiv2020

ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data

Visualization recommendation seeks to generate, score, and recommend to users useful visualizations automatically, and are fundamentally important for exploring and gaining insights into a new or existing dataset quickly. In this work, we propose the first end-to-end ML-based visualization recommendation system that takes as input a large corpus of datasets and visualizations, learns a model based on this data. Then, given a new unseen dataset from an arbitrary user, the model automatically generates visualizations for that new dataset, derive scores for the visualizations, and output a list of recommended visualizations to the user ordered by effectiveness. We also describe an evaluation framework to quantitatively evaluate visualization recommendation models learned from a large corpus of visualizations and datasets. Through quantitative experiments, a user study, and qualitative analysis, we show that our end-to-end ML-based system recommends more effective and useful visualizations compared to existing state-of-the-art rule-based systems. Finally, we observed a strong preference by the human experts in our user study towards the visualizations recommended by our ML-based system as opposed to the rule-based system (5.92 from a 7-point Likert scale compared to only 3.45).

cs.IR↗

arXiv2020

Large nonreciprocal absorption and emission of radiation in type-I Weyl semimetals with time reversal symmetry breaking

The equality between the spectral, directional emittance and absorptance of an object under local thermal equilibrium is known as Kirchhoff's law of radiation. The breakdown of Kirchhoff's law of radiation is physically allowed by breaking time reversal symmetry and can open opportunities for nonreciprocal light emitters and absorbers. Large anomalous Hall conductivity and angle recently observed in topological Weyl semimetals, particularly type-I magnetic Weyl semimetals and type-II Weyl semimetals, are expected to create large nonreciprocal electromagnetic wave propagation. In this work, we focus on type-I magnetic Weyl semimetals and show via modeling and simulation that nonreciprocal surface plasmons polaritons can result in pronounced nonreciprocity without an external magnetic field. The modeling in this work begins with a single pair of Weyl nodes, followed by a more realistic model with multiple paired Weyl nodes. Fermi-arc surface states are also taken into account through the surface conductivity. This work points to the promising applicability of topological Weyl semimetals for magneto-optical and energy applications.

cond-mat.mes-hall↗

arXiv2020

The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent

The mini-batch stochastic gradient descent (SGD) algorithm is widely used in training machine learning models, in particular deep learning models. We study SGD dynamics under linear regression and two-layer linear networks, with an easy extension to deeper linear networks, by focusing on the variance of the gradients, which is the first study of this nature. In the linear regression case, we show that in each iteration the norm of the gradient is a decreasing function of the mini-batch size $b$ and thus the variance of the stochastic gradient estimator is a decreasing function of $b$. For deep neural networks with $L_2$ loss we show that the variance of the gradient is a polynomial in $1/b$. The results back the important intuition that smaller batch sizes yield lower loss function values which is a common believe among the researchers. The proof techniques exhibit a relationship between stochastic gradient estimators and initial weights, which is useful for further research on the dynamics of SGD. We empirically provide further insights to our results on various datasets and commonly used deep network structures.

math.OC↗

arXiv2020

Combined Neyman-Pearson Chi-square: An Improved Approximation to the Poisson-likelihood Chi-square

We describe an approximation to the widely-used Poisson-likelihood chi-square using a linear combination of Neyman's and Pearson's chi-squares, namely "combined Neyman-Pearson chi-square" ($χ^2_{\mathrm{CNP}}$). Through analytical derivations and toy model simulations, we show that $χ^2_\mathrm{CNP}$ leads to a significantly smaller bias on the best-fit model parameters compared to those using either Neyman's or Pearson's chi-square. When the computational cost of using the Poisson-likelihood chi-square is high, $χ^2_\mathrm{CNP}$ provides a good alternative given its natural connection to the covariance matrix formalism.

physics.data-an↗

arXiv2019

Thermal Conductivity Modeling using Machine Learning Potentials: Application to Crystalline and Amorphous Silicon

First-principles based modeling on phonon dynamics and transport using density functional theory and Boltzmann transport equation has proven powerful in predicting thermal conductivity of crystalline materials, but it remains unfeasible for modeling complex crystals and disordered solids due to the prohibitive computational cost to capture the disordered structure, especially when the quasiparticle "phonon" model breaks down. Recently, machine-learning regression algorithms show great promises for building high-accuracy potential fields for atomistic modeling with length and time scales far beyond those achievable by first-principles calculations. In this work, using both crystalline and amorphous silicon as examples, we develop machine learning based potential fields for predicting thermal conductivity. The machine learning based interatomic potential is derived from density functional theory calculations by stochastically sampling the potential energy surface in the configurational space. The thermal conductivities of both amorphous and crystalline silicon are then calculated using equilibrium molecular dynamics, which agree well with experimental measurements. This work documents the procedure for training the machine-learning based potentials for modeling thermal conductivity, and demonstrates that machine-learning based potential can be a promising tool for modeling thermal conductivity of both crystalline and amorphous materials with strong disorder.

cond-mat.mtrl-sci↗

arXiv2019

Clustering Degree-Corrected Stochastic Block Model with Outliers

For the degree corrected stochastic block model in the presence of arbitrary or even adversarial outliers, we develop a convex-optimization-based clustering algorithm that includes a penalization term depending on the positive deviation of a node from the expected number of edges to other inliers. We prove that under mild conditions, this method achieves exact recovery of the underlying clusters. Our synthetic experiments show that our algorithm performs well on heterogeneous networks, and in particular those with Pareto degree distributions, for which outliers have a broad range of possible degrees that may enhance their adversarial power. We also demonstrate that our method allows for recovery with significantly lower error rates compared to existing algorithms.

cs.LG↗

arXiv2019

Dynamic Cell Structure via Recursive-Recurrent Neural Networks

In a recurrent setting, conventional approaches to neural architecture search find and fix a general model for all data samples and time steps. We propose a novel algorithm that can dynamically search for the structure of cells in a recurrent neural network model. Based on a combination of recurrent and recursive neural networks, our algorithm is able to construct customized cell structures for each data sample and time step, allowing for a more efficient architecture search than existing models. Experiments on three common datasets show that the algorithm discovers high-performance cell architectures and achieves better prediction accuracy compared to the GRU structure for language modelling and sentiment analysis.

cs.LG↗

arXiv2018

Temperature Effect on Phonon Dispersion Stability of Zirconium by Machine Learning-driven Atomistic Simulations

It is well known that conventional harmonic lattice dynamics cannot be applied to energetically unstable crystals at 0 K, such as high temperature body centered cubic (BCC) phase of crystalline Zr. Predicting phonon spectra at finite temperature requires the calculation of force constants to the third, fourth and even higher orders, however, it remains challenging to determine to which order the Taylor expansion of the potential energy surface for different materials should be cut off. Molecular dynamics, on the other hand, intrinsically includes arbitrary orders of phonon anharmonicity, however, its accuracy is severely limited by the empirical potential field used. Using machine learning method, we developed an inter-atomic potential for Zirconium crystals for both the hexagonal closed-packed (HCP) phase and the body centered cubic phase. The developed potential field accurately captures energy-volume relationship, elastic constants and phonon dispersions. The instability of BCC structure is found to originate from the double-well shape of the potential energy surface where the local maxima is located in an unstable equilibrium position. The stabilization of the BCC phase at high temperature is due to the dynamical average of the low-symmetry minima of the double well due to atomic vibrations. Molecular dynamics simulations are then performed to stochastically sample the potential energy surface and to calculate the phonon dispersion at elevated temperature. The phonon renormalization in BCC-Zr is successfully captured by the molecular dynamics simulation at 1188 K.

cond-mat.mtrl-sci↗

arXiv2018

Three-Dimensional Anisotropic Thermal Conductivity Tensor of Single Crystalline \b{eta}-Ga2O3

\b{eta}-Ga2O3 has attracted considerable interest in recent years for high power electronics, where thermal properties of \b{eta}-Ga2O3 play a critical role. The thermal conductivity of \b{eta}-Ga2O3 is expected to be three-dimensionally (3D) anisotropic due to the monoclinic lattice structure. In this work, the 3D anisotropic thermal conductivity tensor of a (010)-oriented \b{eta}-Ga2O3 single crystal was measured by using a novel time-domain thermoreflectance (TDTR) method with a highly elliptical pump beam. Our measured results suggest that at room temperature, the highest in-plane thermal conductivity is along a direction between [001] and [102], with a value of 13.3+/-1.8 W/mK, and the lowest in-plane thermal conductivity is close to the [100] direction, with a value of 9.5+/-1.8 W/mK. The through-plane thermal conductivity, which is along the [010] direction, has the highest value of 22+/-2.5 W/mK among all the directions. Temperature-dependent thermal conductivity of \b{eta}-Ga2O3 was also measured and compared with a modified Callaway model calculation to understand the temperature dependence and the role of impurity scattering.

cond-mat.mtrl-sci↗