arXiv subjects

Qiao Li

Publications and source records attributed to Qiao Li.

At least 73 records · Page 4Linked to original sources

arXiv2022

Tunable quantum criticalities in an isospin extended Hubbard model simulator

Studying strong electron correlations has been an essential driving force for pushing the frontiers of condensed matter physics. In particular, in the vicinity of correlation-driven quantum phase transitions (QPTs), quantum critical fluctuations of multiple degrees of freedom facilitate exotic many-body states and quantum critical behaviors beyond Landau's framework. Recently, moiré heterostructures of van der Waals materials have been demonstrated as a highly tunable quantum platform for exploring fascinating strongly correlated quantum physics. Here, we report the observation of tunable quantum criticalities in an experimental simulator of extended Hubbard model with spin-valley isospins arising in chiral-stacked twisted double bilayer graphene. Scaling analysis shows a quantum two-stage criticality manifesting two distinct quantum critical points as the generalized Wigner crystal transits to a Fermi liquid by varying the displacement field, suggesting the emergence of a critical intermediate phase. The quantum two-stage criticality evolves into a quantum pseudo criticality as a high parallel magnetic field is applied. In such pseudo criticality, we find that the quantum critical scaling is only valid above a critical temperature, indicating a weak first-order QPT therein. Our results demonstrate a highly tunable solid-state simulator with intricate interplay of multiple degrees of freedom for exploring exotic quantum critical states and behaviors.

cond-mat.mes-hall↗

arXiv2022

The Lyman-$α$ Emission in a C1.4 Solar Flare Observed by the Extreme Ultraviolet Imager aboard Solar Orbiter

The hydrogen Lyman-$α$ (H {\sc i} Ly$α$) emission during solar flares has rarely been studied in spatially resolved images and its physical origin has not been fully understood. In this paper, we present novel Ly$α$ images for a C1.4 solar flare (SOL2021-08-20T22:00) from the Extreme Ultraviolet Imager aboard Solar Orbiter, together with multi-waveband and multi-perspective observations from the Solar Terrestrial Relations Observatory Ahead and the Solar Dynamics Observatory spacecraft. It is found that the Ly$α$ emission has a good temporal correlation with the thermal emissions at 1--8 Å and 5--7 keV, indicating that the flaring Ly$α$ is mainly produced by a thermal process in this small event. However, nonthermal electrons play a minor role in generating Ly$α$ at flare ribbons during the rise phase of the flare, as revealed by the hard X-ray imaging and spectral fitting. Besides originating from flare ribbons, the Ly$α$ emission can come from flare loops, likely caused by plasma heating and also cooling that happen in different flare phases. It is also found that the Ly$α$ emission shows fairly similar features with the He {\sc ii} 304 Å emission in light curve and spatio-temporal variation along with small differences. These observational results improve our understanding of the Ly$α$ emission in solar flares and also provide some insights for investigating the Ly$α$ emission in stellar flares.

astro-ph.SR↗

arXiv2022

Variational Nested Dropout

Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training. It has been explored for: I. Constructing nested nets: the nested nets are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. The nested dropout implicitly ranks the network parameters, generating a set of sub-networks such that any smaller sub-network forms the basis of a larger one. II. Learning ordered representation: the nested dropout applied to the latent representation of a generative model (e.g., auto-encoder) ranks the features, enforcing explicit order of the dense representation over dimensions. However, the dropout rate is fixed as a hyper-parameter during the whole training process. For nested nets, when network parameters are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. For generative models, the importance of features is specified as a constant vector, restraining the flexibility of representation learning. To address the problem, we focus on the probabilistic counterpart of the nested dropout. We propose a variational nested dropout (VND) operation that draws samples of multi-dimensional ordered masks at a low cost, providing useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the parameter distributions. We further exploit the VND under different generative models for learning ordered latent distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related generative models on data generation tasks.

cs.LG↗

arXiv2021

Generalized Augmented Cellular Alternating Links in Thickened Surfaces are Hyperbolic

Menasco proved that nontrivial links in the 3-sphere with connected prime alternating non-2-braid projections are hyperbolic. This was further extended to augmented alternating links wherein non-isotopic trivial components bounding disks punctured twice by the alternating link were added. Lackenby proved that the first and second collections of links together form a closed subset of the set of all finite volume hyperbolic 3-manifolds in the geometric topology. Adams showed hyperbolicity for generalized augmented alternating links, which include additional trivial components that bound n-punctured disks for $n \geq 2$. Here we prove that generalized augmented cellular alternating links in I-bundles over closed surfaces are also hyperbolic and that in $S \times I$, the cellular alternating links and the augmented cellular alternating together form a closed subset of finite volume hyperbolic 3-manifolds in the geometric topology. Explicit examples of additional links in $S \times I$ to which these results apply are included.

math.GT↗

arXiv2021

Upper Bounds for Totally Symmetric Sets

Totally symmetric sets are a recently introduced tool for studying homomorphisms between groups. In this paper, we give full classifications of totally symmetric sets in certain families of groups and bound their sizes in others. As a consequence, we derive restrictions on possible homomorphisms between these groups. One sample application of our results is that any homomorphism of a braid group to a direct product of solvable groups must have cyclic image.

math.GR↗

arXiv2020

Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data

Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non-invasive techniques such as activity-based algorithms can help to identify successful early interventions. In this work, we used locomotor activity captured from 1113 individuals who wore a research grade smartwatch post-trauma. A convolutional variational autoencoder (VAE) architecture was used for unsupervised feature extraction from four weeks of actigraphy data. By using VAE latent variables and the participant's pre-trauma physical health status as features, a logistic regression classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.64 to estimate mental health outcomes. The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.

cs.LG↗

arXiv2019

Phase-controllable growth of ultrathin 2D magnetic FeTe crystals

Two-dimensional (2D) magnets with intrinsic ferromagnetic/antiferromagnetic (FM/AFM) ordering are highly desirable for future spintronics devices. However, the synthesis of 2D magnetic crystals, especially the direct growth on SiO2/Si substrate, is just in its infancy. Here, we report a chemical vapor deposition (CVD)-based rational growth approach for the synthesis of ultrathin FeTe crystals with controlled structural and magnetic phases. By precisely optimizing the growth temperature (Tgrowth), FeTe nanoplates with either layered tetragonal or non-layered hexagonal phase can be controlled with high-quality. The two controllable phases lead to square and triangular morphologies with a thickness down to 3.6 and 2.8 nm, respectively. More importantly, transport measurements reveal that tetragonal FeTe is antiferromagnetic with a Neel temperature (TN) about 71.8 K, while hexagonal FeTe is ferromagnetic with a Curie temperature (TC) around 220 K. Theoretical calculations indicate that the ferromagnetic order in hexagonal FeTe is originated from a concomitant lattice distortion and the spin-lattice coupling. This study represents a major step forward in the CVD growth of 2D magnetic materials on SiO2/Si substrates and highlights on their potential applications in the future spintronic devices.

cond-mat.mtrl-sci↗

arXiv2019

Finite quotients of braid groups

We derive a lower bound on the size of finite non-cyclic quotients of the braid group that is superexponential in the number of strands. We also derive a similar lower bound for nontrivial finite quotients of the commutator subgroup of the braid group.

math.GT↗

arXiv2019

Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex

Estimating the predictive uncertainty of a Bayesian learning model is critical in various decision-making problems, e.g., reinforcement learning, detecting adversarial attack, self-driving car. As the model posterior is almost always intractable, most efforts were made on finding an accurate approximation the true posterior. Even though a decent estimation of the model posterior is obtained, another approximation is required to compute the predictive distribution over the desired output. A common accurate solution is to use Monte Carlo (MC) integration. However, it needs to maintain a large number of samples, evaluate the model repeatedly and average multiple model outputs. In many real-world cases, this is computationally prohibitive. In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion. The aim is to approximate the true uncertainty of a specific Bayesian model, meanwhile alleviating the heavy workload of MC integration at testing time. The proposed method is universally applicable to Bayesian classification models that allow for posterior sampling. Theoretically, we show that the idea of amortization incurs no additional costs on approximation performance. Empirical results validate the strong practical performance of our approach.

cs.LG↗

arXiv2019

Fast Scenario Reduction for Power Systems by Deep Learning

Scenario reduction is an important topic in stochastic programming problems. Due to the random behavior of load and renewable energy, stochastic programming becomes a useful technique to optimize power systems. Thus, scenario reduction gets more attentions in recent years. Many scenario reduction methods have been proposed to reduce the scenario set in a fast speed. However, the speed of scenario reduction is still very slow, in which it takes at least several seconds to several minutes to finish the reduction. This limitation of speed prevents stochastic programming to be implemented in real-time optimal control problems. In this paper, a fast scenario reduction method based on deep learning is proposed to solve this problem. Inspired by the deep learning based image process, recognition and generation methods, the scenario data are transformed into a 2D image-like data and then to be fed into a deep convolutional neural network (DCNN). The output of the DCNN will be an "image" of the reduced scenario set. Since images can be processed in a very high speed by neural networks, the scenario reduction by neural network can also be very fast. The results of the simulation show that the scenario reduction with the proposed DCNN method can be completed in very high speed.

eess.SP↗

arXiv2019

Fully Distributed DC Optimal Power Flow Based on Distributed Economic Dispatch and Distributed State Estimation

Optimal power flow (OPF) is an important technique for power systems to achieve optimal operation while satisfying multiple constraints. The traditional OPF are mostly centralized methods which are executed in the centralized control center. This paper introduces a totally Distributed DC Optimal Power Flow (DDCOPF) method for future power systems which have more and more distributed generators. The proposed method is based on the Distributed Economic Dispatch (DED) method and the Distributed State Estimation (DSE) method. In this proposed scheme, the DED method is used to achieve the optimal power dispatch with the lowest cost, and the DSE method provides power flow information of the power system to the proposed DDCOPF algorithm. In the proposed method, the Auto-Regressive (AR) model is used to predict the load variation so that the proposed algorithm can prevent overflow. In addition, a method called constraint algorithm is developed to correct the results of DED with the proposed correction algorithm and penalty term so that the constraints for the power system will not be violated. Different from existing research, the proposed method is completely distributed without need for any centralized facility.

eess.SY↗

arXiv2019

S-type Negative Differential Resistance in Semiconducting Transition-Metal Dichalcogenides

Current-controlled (also known as "S-type") negative differential resistance (NDR) is of crucial importance to many emerging applications including neuromorphic computing and high-density memristors integration. However, the experimental realization of S-type NDR based on conventional mechanisms poses demanding requirements on materials, which greatly limits their potential applications. Here, we experimentally identify that semiconducting transition metal dichalcogenides (TMDs) can host a bipolar S-type NDR devices. Theoretical simulations indicate that the origin of the NDR in these devices arises from a thermal feedback mechanism. Furthermore, we demonstrate the potential applications of TMDs based S-type NDR device in signal processing and neuromorphic electronics.

cond-mat.mes-hall↗

arXiv2018

Proximity-Induced Superconductivity with Subgap Anomaly in Type II Weyl Semi-Metal WTe2

Due to the non-trivial topological band structure in type-II Weyl semimetal Tungsten ditelluride (WTe2), unconventional properties may emerge in its superconducting phase. While realizing intrinsic superconductivity has been challenging in the type-II Weyl semimetal WTe2, proximity effect may open an avenue for the realization of superconductivity. Here, we report the observation of proximity-induced superconductivity with a long coherence length along c axis in WTe2 thin flakes based on a WTe2/NbSe2 van der Waals heterostructure. Interestingly, we also observe anomalous oscillations of the differential resistance during the transition from superconducting to normal state. Theoretical calculations show excellent agreement with experimental results, revealing that such a sub-gap anomaly is the intrinsic property of WTe2 in superconducting state induced by the proximity effect. Our findings enrich the understanding of superconducting phase of type-II Weyl semimetals, and pave the way for their future applications in topological quantum computing.

cond-mat.supr-con↗

arXiv2016

Energy Allocation Policy for Cellular Networks Powered by Renewable Energy

The explosive wireless data service requirement accompanied with carbon dioxide emission and consumption of traditional energy has put pressure on both industry and academia. Wireless networks powered with the uneven and intermittent generated renewable energy have been widely researched and lead to a new research paradigm called green communication. In this paper, we comprehensively consider the total generated renewable energy, QoS requirement and channel quality, then propose a utility based renewable energy allocation policy. The utility here means the satisfaction degree of users with a certain amount allocated renewable energy. The energy allocation problem is formulated as a constraint optimization problem and a heuristic algorithm with low complexity is derived to solve the raised problem. Numerical results show that the renewable energy allocation policy is applicable for any situation. When the renewable energy is very scarce, only users with good channel quality can achieve allocated energy.

cs.NI↗

arXiv2016

The Dynamic Spectrum Aggregation Strategy for Cognitive Networks Based on Markov Model

In order to meet the constantly increasing demand by mobile terminals for higher data rates with limited wireless spectrum resource, cognitive radio and spectrum aggregation technologies have attracted much attention due to its capacity in improving spectrum efficiency. Combing cognitive relay and spectrum aggregation technologies, in this paper, we propose a dynamic spectrum aggregation strategy based on the Markov Prediction of the state of spectrum for the cooperatively relay networks on a multi-user and multi-relay scenario aiming at ensuring the user channel capacity and maximizing the network throughput. The spectrum aggregation strategy is executed through two steps. First, predict the state of spectrum through Markov prediction. Based on the prediction results of state of spectrum, a spectrum aggregation strategy is proposed. Simulation results show that the spectrum prediction process can observably lower the outage rate, and the spectrum aggregation strategy can greatly improve the network throughput.

cs.NI↗

arXiv2016

Green Wireless Sensor Networks with Wireless Power Transfer

An energy cooperation policy for energy harvesting wireless sensor networks (WSNs) with wireless power transfer is proposed in this paper to balance the energy at each sensor node and increase the total energy utilization ratio of the whole WSNs. Considering the unbalanced spatio-temporal properties of the energy supply across the deployment terrain of energy harvesting WSNs and the dynamic traffic load at each sensor node, the energy cooperation problem among sensor nodes is decomposed into two steps: the local energy storage at each sensor node based on its traffic load to meet its own needs; within the energy storage procedure sensor nodes with excess energy transmit a part of their energy to nodes with energy shortage through the energy trading. Inventory theory and game theory are respectively applied to solving the local energy storage problem at each sensor node and the energy trading problem among multiple sensor nodes. Numerical results show that compared with the static energy cooperation method without energy trading, the Stackelberg Model based Game we design in this paper can significantly improve the trading volume of energy thereby increasing the utilization ratio of the harvested energy which is unevenly distributed in the WSNs.

cs.NI↗

arXiv2014

High-T_c superconductivity in ultrathin Bi_2Sr_2CaCu_2O_8+x down to halfunit-cell thickness by protection with graphene

High-T_c superconductors confined to two dimension exhibit novel physical phenomena, such as superconductor-insulator transition. In the Bi_2Sr_2CaCu_2O_8+x (Bi2212) model system, despite extensive studies, the intrinsic superconducting properties at the thinness limit have been difficult to determine. Here we report a method to fabricate high quality single-crystal Bi2212 films down to half-unit-cell thickness in the form of graphene/Bi2212 van der Waals heterostructure, in which sharp superconducting transitions are observed. The heterostructure also exhibits a nonlinear current-voltage characteristic due to the Dirac nature of the graphene band structure. More interestingly, although the critical temperature remains essentially the same with reduced thickness of Bi2212, the slope of the normal state T-linear resistivity varies by a factor of 4-5, and the sheet resistance increases by three orders of magnitude, indicating a surprising decoupling of the normal state resistance and superconductivity. The developed technique is versatile, applicable to investigate other two-dimensional (2D) superconducting materials.

cond-mat.supr-con↗

arXiv2012

A Queueing Based Scheduling Approach to Plug-In Electric Vehicle Dispatch in Distribution Systems

Large-scale integration of plug-in electric vehicles (PEV) in power systems can cause severe issues to the existing distribution system, such as branch congestions and significant voltage drops. As a consequence, smart charging strategies are crucial for the secure and reliable operation of the power system. This paper tries to achieve high penetration level of PEVs with the existing distribution system infrastructure by proposing a smart charging algorithm that can optimally utilize the distribution system capacity. Specifically, the paper proposes a max-weight PEV dispatch algorithm to control the PEV charging rates, subject to power system physical limits. The proposed max-weight PEV dispatch algorithm is proved to be throughput optimal under very mild assumptions on the stochastic dynamics in the system. This suggests that the costly distribution system infrastructure upgrade can be avoided, or failing that, at least successfully deferred. The proposed PEV dispatch algorithm is particularly attractive in integrating the renewable energy sources in the distribution system, by successfully absorbing their intermittency.

math.OC↗