arXiv subjects

Lan Wang

Publications and source records attributed to Lan Wang.

At least 91 records · Page 5Linked to original sources

arXiv2022

Role of Variable Renewable Energy Penetration on Electricity Price and its Volatility Across Independent System Operators in the United States

The U.S. electrical grid has undergone substantial transformation with increased penetration of wind and solar -- forms of variable renewable energy (VRE). Despite the benefits of VRE for decarbonization, it has garnered some controversy for inducing unwanted effects in regional electricity markets. In this study, the role of VRE penetration is examined on the system electricity price and price volatility based on hourly, real-time, historical data from six Independent System Operators (ISOs) in the U.S. using quantile and skew t-distribution regressions. After correcting for temporal effects, we found an increase in VRE penetration is associated with decrease in system electricity price in all ISOs studied. The increase in VRE penetration is associated with decrease in temporal price volatility in five out of six ISOs studied. The relationships are non-linear. These results are consistent with the modern portfolio theory where diverse volatile assets may lead to more stable and less risky portfolios.

q-fin.GN↗

arXiv2022

HI content of massive red spiral galaxies observed by FAST

A sample of 279 massive red spirals was selected optically by Guo et al. (2020), among which 166 galaxies have been observed by the ALFALFA survey. In this work, we observe HI content of the rest 113 massive red spiral galaxies using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). 75 of the 113 galaxies have HI detection with a signal-to-noise ratio (S/N) greater than 4.7. Compared with the red spirals in the same sample that have been observed by the ALFALFA survey, galaxies observed by FAST have on average a higher S/N, and reach to a lower HI mass. To investigate why many red spirals contain a significant amount of HI mass, we check color profiles of the massive red spirals using images observed by the DESI Legacy Imaging Surveys. We find that galaxies with HI detection have bluer outer disks than the galaxies without HI detection, for both ALFALFA and FAST samples. For galaxies with HI detection, there exists a clear correlation between galaxy HI mass and g-r color at outer radius: galaxies with higher HI masses have bluer outer disks. The results indicate that optically selected massive red spirals are not fully quenched, and the HI gas observed in many of the galaxies may exist in their outer blue disks.

astro-ph.GA↗

arXiv2022

A Multi-level Acoustic Feature Extraction Framework for Transformer Based End-to-End Speech Recognition

Transformer based end-to-end modelling approaches with multiple stream inputs have been achieved great success in various automatic speech recognition (ASR) tasks. An important issue associated with such approaches is that the intermediate features derived from each stream might have similar representations and thus it is lacking of feature diversity, such as the descriptions related to speaker characteristics. To address this issue, this paper proposed a novel multi-level acoustic feature extraction framework that can be easily combined with Transformer based ASR models. The framework consists of two input streams: a shallow stream with high-resolution spectrograms and a deep stream with low-resolution spectrograms. The shallow stream is used to acquire traditional shallow features that is beneficial for the classification of phones or words while the deep stream is used to obtain utterance-level speaker-invariant deep features for improving the feature diversity. A feature correlation based fusion strategy is used to aggregate both features across the frequency and time domains and then fed into the Transformer encoder-decoder module. By using the proposed multi-level acoustic feature extraction framework, state-of-the-art word error rate of 21.7% and 2.5% were obtained on the HKUST Mandarin telephone and Librispeech speech recognition tasks respectively.

eess.AS↗

arXiv2022

Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes

In many important applications of precision medicine, the outcome of interest is time to an event (e.g., death, relapse of disease) and the primary goal is to identify the optimal individualized decision rule (IDR) to prolong survival time. Existing work in this area have been mostly focused on estimating the optimal IDR to maximize the We propose a new robust framework for estimating an optimal static or dynamic IDR with time-to-event outcomes based on an easy-to-interpret quantile criterion. The new method does not need to specify an outcome regression model and is robust for heavy-tailed distribution. The estimation problem corresponds to a nonregular M-estimation problem with both finite and infinite-dimensional nuisance parameters. Employing advanced empirical process techniques, we establish the statistical theory of the estimated parameter indexing the optimal IDR. Furthermore, we prove a novel result that the proposed approach can consistently estimate the optimal value function under mild conditions even when the optimal IDR is non-unique, which happens in the challenging setting of exceptional laws. We also propose a smoothed resampling procedure for inference. The proposed methods are implemented in the R-package QTOCen. We demonstrate the performance of the proposed new methods via extensive Monte Carlo studies and a real data application.restricted mean survival time in the population.

stat.ME↗

arXiv2022

Do learned representations respect causal relationships?

Data often has many semantic attributes that are causally associated with each other. But do attribute-specific learned representations of data also respect the same causal relations? We answer this question in three steps. First, we introduce NCINet, an approach for observational causal discovery from high-dimensional data. It is trained purely on synthetically generated representations and can be applied to real representations, and is specifically designed to mitigate the domain gap between the two. Second, we apply NCINet to identify the causal relations between image representations of different pairs of attributes with known and unknown causal relations between the labels. For this purpose, we consider image representations learned for predicting attributes on the 3D Shapes, CelebA, and the CASIA-WebFace datasets, which we annotate with multiple multi-class attributes. Third, we analyze the effect on the underlying causal relation between learned representations induced by various design choices in representation learning. Our experiments indicate that (1) NCINet significantly outperforms existing observational causal discovery approaches for estimating the causal relation between pairs of random samples, both in the presence and absence of an unobserved confounder, (2) under controlled scenarios, learned representations can indeed satisfy the underlying causal relations between their respective labels, and (3) the causal relations are positively correlated with the predictive capability of the representations.

cs.CV↗

arXiv2022

Gate-tunable exchange bias effect in FePS3-Fe5GeTe2 van der Waals heterostructures

Electrical gate-manipulated exchange bias (EB) effect is a long-term goal for spintronics applications. Meanwhile, the emergence of van der Waals (vdW) magnetic heterostructures provides ideal platforms for the study of interlayer magnetic coupling. However, to date, the electrical gate-controlled EB effect has yet to be realized in vdW heterostructures. Here, for the first time, we realized electrically-controllable EB effects in a vdW antiferromagnetic (AFM)-ferromagnetic (FM) heterostructure, FePS3-Fe5GeTe2. For pristine FePS3-Fe5GeTe2 heterostructures, sizable EB effects can be generated due to the strong interface coupling, which also depend on the thickness of the ferromagnetic layers. By applying a solid protonic gate, the EB effects can be electrically tuned largely by proton intercalations and deintercalations. The EB field reaches up to 23% of the coercive field and the blocking temperature exceeds 50 K at Vg= -3.15 V. The proton intercalations not only tune the average magnetic exchange coupling, but also change the AFM configurations and transform the heterointerface between an uncompensated AFM-FM interface and a compensated AFM-FM interface. These alterations result in a dramatic modulation of the total interface exchange coupling and the resultant EB effects. The study is a significant step towards vdW heterostructure-based magnetic logic for future low-energy electronics.

cond-mat.mtrl-sci↗

arXiv2022

Electrically controlled superconductor-insulator transition and giant anomalous Hall effect in kagome metal CsV3Sb5 nanoflakes

The electronic correlations (e.g. unconventional superconductivity (SC), chiral charge order and nematic order) and giant anomalous Hall effect (AHE) in topological kagome metals AV3Sb5 (A= K, Rb, and Cs) have attracted great interest. Electrical control of those correlated electronic states and AHE allows us to resolve their own nature and origin and to discover new quantum phenomena. Here, we show that a protonic gate can largely modulate the effective disorders and carrier density in CsV3Sb5 nanoflakes, leading to significant modifications of SC, unusual charge density wave (CDW) and giant AHE. Notably, we observed a direct superconductor-insulator transition (SIT) driven by superconducting phase fluctuation due to the doping-enhanced disorders, in addition to a large suppression of CDW. Meanwhile, the carrier density modulation shifts the Fermi level across the CDW gap and gives rise to a nontrivial evolution of AHE, in line with the asymmetric density of states of CDW sub-bands near the saddle point. With the first-principles calculations, we suggest the extrinsic skew scattering of holes in the nearly flat bands with finite Berry curvature by multiple impurities accounts for the giant AHE. Our work uncovers a disorder-driven bosonic SIT, outlines a global picture of the giant AHE and reveals its correlation with the unconventional CDW in the AV3Sb5 family.

cond-mat.mtrl-sci↗

arXiv2022

A Robust Statistical Analysis of the Role of Hydropower on the System Electricity Price and Price Volatility

Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage) for providing energy in times of high demand on the grid. This study examines the impact of hydropower on system electricity price and price volatility in the region served by the New England Independent System Operator (ISONE) from 2014 - 2020. We perform a robust holistic analysis of the mean and quantile effects, as well as the marginal contributing effects of hydropower in the presence of solar and wind resources. First, the price data is adjusted for deterministic temporal trends, correcting for seasonal, weekend, and diurnal effects that may obscure actual representative trends in the data. Using multiple linear regression and quantile regression, we observe that hydropower contributes to a reduction in the system electricity price and price volatility. While hydropower has a weak impact on decreasing price and volatility at the mean, it has greater impact at extreme quantiles (> 70th percentile). At these higher percentiles, we find that hydropower provides a stabilizing effect on price volatility in the presence of volatile resources such as wind. We conclude with a discussion of the observed relationship between hydropower and system electricity price and volatility.

q-fin.GN↗

arXiv2022

Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection

Today's cyber-world is vastly multivariate. Metrics collected at extreme varieties demand multivariate algorithms to properly detect anomalies. However, forecast-based algorithms, as widely proven approaches, often perform sub-optimally or inconsistently across datasets. A key common issue is they strive to be one-size-fits-all but anomalies are distinctive in nature. We propose a method that tailors to such distinction. Presenting FMUAD - a Forecast-based, Multi-aspect, Unsupervised Anomaly Detection framework. FMUAD explicitly and separately captures the signature traits of anomaly types - spatial change, temporal change and correlation change - with independent modules. The modules then jointly learn an optimal feature representation, which is highly flexible and intuitive, unlike most other models in the category. Extensive experiments show our FMUAD framework consistently outperforms other state-of-the-art forecast-based anomaly detectors.

cs.LG↗

arXiv2021

Analysis of animal-related electric outages using species distribution models and community science data

Animal-related outages (AROs) are a prevalent form of outages in electrical distribution systems. Animal-infrastructure interactions vary across focal species and regions, underlining the need to study the animal-outage relationship in more species and diverse systems. Animal activity has been used as an indicator of reliability in the electrical grid system and to describe temporal patterns in AROs. However, these ARO models have been limited by a lack of available estimates of species activity, instead approximating activity based on seasonal and weather patterns in animal-related outage records and characteristics of broad taxonomic groups, e.g., squirrels. We highlight publicly available resources to fill the ecological data gap that is limiting joint analyses between ecology and energy sectors. Species distribution models (SDMs), a common technique to model the distribution of a species across geographic space and time, paired with data sourced from eBird, a community science database for bird observations, provided us with species-specific estimates of activity to model spatio-temporal patterns of AROs. These flexible, species-specific estimates can allow future animal-indicators of grid reliability to be investigated in more diverse regions and ecological communities, providing a better understanding of the variation that exists in animal-outage relationship. AROs were best modeled by accounting for multiple outage-prone species activity patterns and their unique relationships with seasonality and habitat availability. Different species were important for modeling outages in different landscapes and seasons depending on their distribution and migration behavior. We recommend that future models of AROs include species-specific activity data that account for the diverse spectrum of spatio-temporal activity patterns that outage-prone animals exhibit.

q-bio.OT↗

arXiv2021

Non-linear ballistic response of quantum spin-Hall edge states

Topological edge states exhibit dissipationless transport and electrically-driven topological phase transitions, making them ideal for next-generation transistors that are not constrained by Moore's law. Nevertheless, their dispersion has never been probed and is often assumed to be simply linear, without any rigorous justification. Here we determine the non-linear electrical response of topological edge states in the ballistic regime and demonstrate the way this response ascertains the presence of symmetry breaking terms in the edge dispersion, such as deviations from non-linearity and tilted spin quantization axes. The non-linear response stems from discontinuities in the band occupation on either side of a Zeeman gap, and its direction is set by the spin orientation with respect to the Zeeman field. We determine the edge dispersion for several classes of topological materials and discuss experimental measurement.

cond-mat.mes-hall↗

arXiv2021

Non-linear antidamping spin-orbit torque originating from intra-band transport on the warped surface of a topological insulator

Motivated by recent experiments observing a large antidamping spin-orbit torque (SOT) on the surface of a three-dimensional topological insulator, we investigate the origin of the current-induced SOT beyond linear-response theory. We find that a strong antidamping SOT arises from intraband transitions in non-linear response, and does not require interband transitions as is the case in linear transport mechanisms. The joint effect of warping and an in-plane magnetization generates a non-linear antidamping SOT which can exceed the intrinsic one by several orders of magnitude, depending on warping parameter and the position of Fermi energy, and exhibits a complex dependence on the azimuthal angle of the magnetization. This nonlinear SOT provides an alternative explanation of the observed giant SOT in recent experiments.

cond-mat.mes-hall↗

arXiv2021

FDN: Finite Difference Network with Hierarchical Convolutional Features for Text-independent Speaker Verification

In recent years, using raw waveforms as input for deep networks has been widely explored for the speaker verification system. For example, RawNet and RawNet2 extracted speaker's feature embeddings from waveforms automatically for recognizing their voice, which can vastly reduce the front-end computation and obtain state-of-the-art performance. However, these models do not consider the speaker's high-level behavioral features, such as intonation, indicating each speaker's universal style, rhythm, \textit{etc}. This paper presents a novel network that can handle the intonation information by computing the finite difference of different speakers' utterance variations. Furthermore, a hierarchical way is also designed to enhance the intonation property from coarse to fine to improve the system accuracy. The high-level intonation features are then fused with the low-level embedding features. Experimental results on official VoxCeleb1 test data, VoxCeleb1-E, and VoxCeleb-H protocols show our method outperforms and robustness existing state-of-the-art systems. To facilitate further research, code is available at https://github.com/happyjin/FDN

eess.AS↗

arXiv2021

Unsupervised Cross-Lingual Speech Emotion Recognition Using Pseudo Multilabel

Speech Emotion Recognition (SER) in a single language has achieved remarkable results through deep learning approaches in the last decade. However, cross-lingual SER remains a challenge in real-world applications due to a great difference between the source and target domain distributions. To address this issue, we propose an unsupervised cross-lingual Neural Network with Pseudo Multilabel (NNPM) that is trained to learn the emotion similarities between source domain features inside an external memory adjusted to identify emotion in cross-lingual databases. NNPM introduces a novel approach that leverages external memory to store source domain features and generates pseudo multilabel for each target domain data by computing the similarities between the external memory and the target domain features. We evaluate our approach on multiple different languages of speech emotion databases. Experimental results show our proposed approach significantly improves the weighted accuracy (WA) across multiple low-resource languages on Urdu, Skropus, ShEMO, and EMO-DB corpus. To facilitate further research, code is available at https://github.com/happyjin/NNPM

eess.AS↗

arXiv2021

Enhanced Memory Network: The novel network structure for Symbolic Music Generation

Symbolic melodies generation is one of the essential tasks for automatic music generation. Recently, models based on neural networks have had a significant influence on generating symbolic melodies. However, the musical context structure is complicated to capture through deep neural networks. Although long short-term memory (LSTM) is attempted to solve this problem through learning order dependence in the musical sequence, it is not capable of capturing musical context with only one note as input for each time step of LSTM. In this paper, we propose a novel Enhanced Memory Network (EMN) with several recurrent units, named Enhanced Memory Unit (EMU), to explicitly modify the internal architecture of LSTM for containing music beat information and reinforces the memory of the latest musical beat through aggregating beat inside the memory gate. In addition, to increase the diversity of generated musical notes, cosine distance among adjacent time steps of hidden states is considered as part of loss functions to avoid a high similarity score that harms the diversity of generated notes. Objective and subjective evaluation results show that the proposed method achieves state-of-the-art performance. Code and music demo are available at https://github.com/qrqrqrqr/EMU

eess.AS↗

arXiv2021

Adversarial Representation Learning With Closed-Form Solvers

Adversarial representation learning aims to learn data representations for a target task while removing unwanted sensitive information at the same time. Existing methods learn model parameters iteratively through stochastic gradient descent-ascent, which is often unstable and unreliable in practice. To overcome this challenge, we adopt closed-form solvers for the adversary and target task. We model them as kernel ridge regressors and analytically determine an upper-bound on the optimal dimensionality of representation. Our solution, dubbed OptNet-ARL, reduces to a stable one one-shot optimization problem that can be solved reliably and efficiently. OptNet-ARL can be easily generalized to the case of multiple target tasks and sensitive attributes. Numerical experiments, on both small and large scale datasets, show that, from an optimization perspective, OptNet-ARL is stable and exhibits three to five times faster convergence. Performance wise, when the target and sensitive attributes are dependent, OptNet-ARL learns representations that offer a better trade-off front between (a) utility and bias for fair classification and (b) utility and privacy by mitigating leakage of private information than existing solutions.

cs.LG↗

arXiv2021

High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization

$\ell_1$-penalized quantile regression is widely used for analyzing high-dimensional data with heterogeneity. It is now recognized that the $\ell_1$-penalty introduces non-negligible estimation bias, while a proper use of concave regularization may lead to estimators with refined convergence rates and oracle properties as the signal strengthens. Although folded concave penalized $M$-estimation with strongly convex loss functions have been well studied, the extant literature on quantile regression is relatively silent. The main difficulty is that the quantile loss is piecewise linear: it is non-smooth and has curvature concentrated at a single point. To overcome the lack of smoothness and strong convexity, we propose and study a convolution-type smoothed quantile regression with iteratively reweighted $\ell_1$-regularization. The resulting smoothed empirical loss is twice continuously differentiable and (provably) locally strongly convex with high probability. We show that the iteratively reweighted $\ell_1$-penalized smoothed quantile regression estimator, after a few iterations, achieves the optimal rate of convergence, and moreover, the oracle rate and the strong oracle property under an almost necessary and sufficient minimum signal strength condition. Extensive numerical studies corroborate our theoretical results.

stat.ME↗

arXiv2021

Formation of massive disk galaxies in the IllustrisTNG simulation

We investigate the formation history of massive disk galaxies in hydro-dynamical simulation--the IllustrisTNG, to study why massive disk galaxies survive through cosmic time. 83 galaxies in the simulation are selected with M$_{*,z=0}$ $>8\times10^{10}$ M$_\odot$ and kinematic bulge-to-total ratio less than $0.3$. We find that 8.4 percent of these massive disk galaxies have quiet merger histories and preserve disk morphology since formed. 54.2 percent have a significant increase in bulge components in history, then become disks again till present time. The rest 37.3 percent experience prominent mergers but survive to remain disky. While mergers and even major mergers do not always turn disk galaxies into ellipticals, we study the relations between various properties of mergers and the morphology of merger remnants. We find a strong dependence of remnant morphology on the orbit type of major mergers. Specifically, major mergers with a spiral-in falling orbit mostly lead to disk-dominant remnants, and major mergers of head-on galaxy-galaxy collision mostly form ellipticals. This dependence of remnant morphology on orbit type is much stronger than the dependence on cold gas fraction or orbital configuration of merger system as previously studied.

astro-ph.GA↗