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Researcher profile

Dong Li

Dong Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine Learningastro-ph.SRArtificial IntelligenceComputer VisionDistributed, Parallel, and Cluster Computingmath.NANumerical Analysiscond-mat.supr-conmath.APComputation and Languageeess.SPInformation Retrievalquant-phInformation Theorymath.ITApplicationscond-mat.mes-hallcond-mat.str-elecon.EMeess.IVHardware Architecturemath.OCMethodologyPerformancephysics.comp-phphysics.opticsphysics.soc-phPopulations and EvolutionQuantitative MethodsRobotics

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Published work

59 published item(s)

preprint2026arXiv

When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling

Large Reasoning Models (LRMs) achieve strong performance on mathematical reasoning tasks but remain unreliable on challenging instances. Existing test-time scaling methods, such as repeated sampling, self-correction, and tree search, improve performance at the cost of increased computation, yet often exhibit diminishing returns on hard problems. We observe that output disagreement is strongly correlated with instance difficulty and prediction correctness, providing a useful signal for guiding instance-level strategy selection at test time. Based on this insight, we propose a training-free framework that formulates test-time scaling as an instance-level routing problem, rather than allocating more computation within a single strategy, dynamically selecting among different scaling strategies based on output disagreement. The framework applies lightweight resolution for consistent cases, majority voting for moderate disagreement, and rewriting-based reformulation for highly ambiguous instances. Experiments on seven mathematical benchmarks and three models show that our method improves accuracy by 3% - 7% while reducing sampling cost compared to existing approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Computation-Efficient Backscatter-Blessed MEC with User Reciprocity

This letter proposes a new user cooperative offloading protocol called user reciprocity in backscatter communication (BackCom)-aided mobile edge computing systems with efficient computation, whose quintessence is that each user can switch alternately between the active or the BackCom mode in different slots, and one user works in the active mode and the other user works in the BackCom mode in each time slot. In particular, the user in the BackCom mode can always use the signal transmitted by the user in the active mode for more data transmission in a spectrum-sharing manner. To evaluate the proposed protocol, a computation efficiency (CE) maximization-based optimization problem is formulated by jointly power control, time scheduling, reflection coefficient adjustment, and computing frequency allocation, while satisfying various physical constraints on the maximum energy budget, the computing frequency threshold, the minimum computed bits, and harvested energy threshold. To solve this non-convex problem, Dinkelbach's method and quadratic transform are first employed to transform the complex fractional forms into linear ones. Then, an iterative algorithm is designed by decomposing the resulting problem to obtain the suboptimal solution. The closed-form solutions for the transmit power, the RC, and the local computing frequency are provided for more insights. Besides, the analytical performance gain with the reciprocal mode is also derived. Simulation results demonstrate that the proposed scheme outperforms benchmark schemes regarding the CE.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Transformer in Transformer as Backbone for Deep Reinforcement Learning

Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network with several modules like CNN, LSTM and Attention. Recent methods combine the Transformer with these modules for better performance. However, it requires tedious optimization skills to train a network composed of mixed modules, making these methods inconvenient to be used in practice. In this paper, we propose to design \emph{pure Transformer-based networks} for deep RL, aiming at providing off-the-shelf backbones for both the online and offline settings. Specifically, the Transformer in Transformer (TIT) backbone is proposed, which cascades two Transformers in a very natural way: the inner one is used to process a single observation, while the outer one is responsible for processing the observation history; combining both is expected to extract spatial-temporal representations for good decision-making. Experiments show that TIT can achieve satisfactory performance in different settings consistently.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A disorder-sensitive emergent vortex phase identified in high-Tc superconductor (Li,Fe)OHFeSe

The magneto-transport properties are systematically measured under c-direction fields up to 33 T for a series of single-crystal films of intercalated iron-selenide superconductor (Li,Fe)OHFeSe. The film samples with varying degree of disorder are grown hydrothermally. We observe a magnetic-field-enhanced shoulder-like feature in the mixed state of the high-Tc (Li,Fe)OHFeSe films with weak disorder, while the feature fades away in the films with enhanced disorder. The irreversibility field is significantly suppressed to lower temperatures with the appearance of the shoulder feature. Based on the experiment and model analysis, we establish a new vortex phase diagram for the weakly disordered high-Tc (Li,Fe)OHFeSe, which features an emergent dissipative vortex phase intermediate between the common vortex glass and liquid phases. The reason for the emergence of this intermediate vortex state is further discussed based on related experiments and models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Survey on Interpretable Reinforcement Learning

Although deep reinforcement learning has become a promising machine learning approach for sequential decision-making problems, it is still not mature enough for high-stake domains such as autonomous driving or medical applications. In such contexts, a learned policy needs for instance to be interpretable, so that it can be inspected before any deployment (e.g., for safety and verifiability reasons). This survey provides an overview of various approaches to achieve higher interpretability in reinforcement learning (RL). To that aim, we distinguish interpretability (as a property of a model) and explainability (as a post-hoc operation, with the intervention of a proxy) and discuss them in the context of RL with an emphasis on the former notion. In particular, we argue that interpretable RL may embrace different facets: interpretable inputs, interpretable (transition/reward) models, and interpretable decision-making. Based on this scheme, we summarize and analyze recent work related to interpretable RL with an emphasis on papers published in the past 10 years. We also discuss briefly some related research areas and point to some potential promising research directions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture

Automated data augmentation, which aims at engineering augmentation policy automatically, recently draw a growing research interest. Many previous auto-augmentation methods utilized a Density Matching strategy by evaluating policies in terms of the test-time augmentation performance. In this paper, we theoretically and empirically demonstrated the inconsistency between the train and validation set of small-scale medical image datasets, referred to as in-domain sampling bias. Next, we demonstrated that the in-domain sampling bias might cause the inefficiency of Density Matching. To address the problem, an improved augmentation search strategy, named Augmented Density Matching, was proposed by randomly sampling policies from a prior distribution for training. Moreover, an efficient automatical machine learning(AutoML) algorithm was proposed by unifying the search on data augmentation and neural architecture. Experimental results indicated that the proposed methods outperformed state-of-the-art approaches on MedMNIST, a pioneering benchmark designed for AutoML in medical image analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users' multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior prediction. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. However, different behaviors may reflect different aspects of user preference, which means that some irrelevant interactions may play as noises to the target behavior to be predicted. To address the aforementioned limitations, we introduce multi-interest learning to the multi-behavior recommendation. More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors. CKML introduces two advanced modules, namely Coarse-grained Interest Extracting (CIE) and Fine-grained Behavioral Correlation (FBC), which work jointly to capture fine-grained behavioral dependencies. CIE uses knowledge-aware information to extract initial representations of each interest. FBC incorporates a dynamic routing scheme to further assign each behavior among interests. Additionally, we use the self-attention mechanism to correlate different behavioral information at the interest level. Empirical results on three real-world datasets verify the effectiveness and efficiency of our model in exploiting multi-behavior data. Further experiments demonstrate the effectiveness of each module and the robustness and superiority of the shared and specific modelling paradigm for multi-behavior data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Compare learning: bi-attention network for few-shot learning

Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only. One of the Few-shot learning methods called metric learning addresses this challenge by first learning a deep distance metric to determine whether a pair of images belong to the same category, then applying the trained metric to instances from other test set with limited labels. This method makes the most of the few samples and limits the overfitting effectively. However, extant metric networks usually employ Linear classifiers or Convolutional neural networks (CNN) that are not precise enough to globally capture the subtle differences between vectors. In this paper, we propose a novel approach named Bi-attention network to compare the instances, which can measure the similarity between embeddings of instances precisely, globally and efficiently. We verify the effectiveness of our model on two benchmarks. Experiments show that our approach achieved improved accuracy and convergence speed over baseline models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification

Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore how to extend self-attention modules to better learn subtle feature embeddings for recognizing fine-grained objects, e.g., different bird species or person identities. To this end, we propose a dual cross-attention learning (DCAL) algorithm to coordinate with self-attention learning. First, we propose global-local cross-attention (GLCA) to enhance the interactions between global images and local high-response regions, which can help reinforce the spatial-wise discriminative clues for recognition. Second, we propose pair-wise cross-attention (PWCA) to establish the interactions between image pairs. PWCA can regularize the attention learning of an image by treating another image as distractor and will be removed during inference. We observe that DCAL can reduce misleading attentions and diffuse the attention response to discover more complementary parts for recognition. We conduct extensive evaluations on fine-grained visual categorization and object re-identification. Experiments demonstrate that DCAL performs on par with state-of-the-art methods and consistently improves multiple self-attention baselines, e.g., surpassing DeiT-Tiny and ViT-Base by 2.8% and 2.4% mAP on MSMT17, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dynamic Sparse R-CNN

Sparse R-CNN is a recent strong object detection baseline by set prediction on sparse, learnable proposal boxes and proposal features. In this work, we propose to improve Sparse R-CNN with two dynamic designs. First, Sparse R-CNN adopts a one-to-one label assignment scheme, where the Hungarian algorithm is applied to match only one positive sample for each ground truth. Such one-to-one assignment may not be optimal for the matching between the learned proposal boxes and ground truths. To address this problem, we propose dynamic label assignment (DLA) based on the optimal transport algorithm to assign increasing positive samples in the iterative training stages of Sparse R-CNN. We constrain the matching to be gradually looser in the sequential stages as the later stage produces the refined proposals with improved precision. Second, the learned proposal boxes and features remain fixed for different images in the inference process of Sparse R-CNN. Motivated by dynamic convolution, we propose dynamic proposal generation (DPG) to assemble multiple proposal experts dynamically for providing better initial proposal boxes and features for the consecutive training stages. DPG thereby can derive sample-dependent proposal boxes and features for inference. Experiments demonstrate that our method, named Dynamic Sparse R-CNN, can boost the strong Sparse R-CNN baseline with different backbones for object detection. Particularly, Dynamic Sparse R-CNN reaches the state-of-the-art 47.2% AP on the COCO 2017 validation set, surpassing Sparse R-CNN by 2.2% AP with the same ResNet-50 backbone.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction

Precise congestion prediction from a placement solution plays a crucial role in circuit placement. This work proposes the lattice hypergraph (LH-graph), a novel graph formulation for circuits, which preserves netlist data during the whole learning process, and enables the congestion information propagated geometrically and topologically. Based on the formulation, we further developed a heterogeneous graph neural network architecture LHNN, jointing the routing demand regression to support the congestion spot classification. LHNN constantly achieves more than 35% improvements compared with U-nets and Pix2Pix on the F1 score. We expect our work shall highlight essential procedures using machine learning for congestion prediction.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Moving structures in ultraviolet bright points: observations from Solar Orbiter/EUI

Moving structures have been detected in coronal bright points and in a solar flare in active regions, which were bi-directional, symmetrical, simultaneous, and quasi-periodic (Ning & Guo 2014; Ning 2016; Li et al. 2016a). They could be regarded as observational evidence of plasma outflows via magnetic reconnection. In this article, we explored pairs of moving structures in fifteen ultraviolet bright points (UBPs), which were observed in the quiet Sun or inside a small active region on 19 November 2020, and measured by the High Resolution (HRI) Telescopes of the Extreme Ultraviolet Imager (EUI) on board the Solar Orbiter (SolO) in two passbands, HRIEUV 174 Å and HRILyα 1216 Å. Moving structures observed in ten UBPs as starting from their bright cores and propagating toward two ends, are interpreted as diverging motions of bi-directional moving structures. These moving structures are also characterized by simultaneity and symmetry and in the case of seven UBPs they exhibit quasi-periodicity. They could be generated by outflows after magnetic reconnections. Moving structures seen in another five UBPs as originating from double ends and moving closer, and merging together, are manifested as converging motions, which might be caused by inflows through the magnetic reconnection, or might be interpreted as upflows driven by the chromospheric evaporation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-Document Scientific Summarization from a Knowledge Graph-Centric View

Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers. This task requires precise understanding of paper content and accurate modeling of cross-paper relationships. Knowledge graphs convey compact and interpretable structured information for documents, which makes them ideal for content modeling and relationship modeling. In this paper, we present KGSum, an MDSS model centred on knowledge graphs during both the encoding and decoding process. Specifically, in the encoding process, two graph-based modules are proposed to incorporate knowledge graph information into paper encoding, while in the decoding process, we propose a two-stage decoder by first generating knowledge graph information of summary in the form of descriptive sentences, followed by generating the final summary. Empirical results show that the proposed architecture brings substantial improvements over baselines on the Multi-Xscience dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Neural Architecture Search on Efficient Transformers and Beyond

Recently, numerous efficient Transformers have been proposed to reduce the quadratic computational complexity of standard Transformers caused by the Softmax attention. However, most of them simply swap Softmax with an efficient attention mechanism without considering the customized architectures specially for the efficient attention. In this paper, we argue that the handcrafted vanilla Transformer architectures for Softmax attention may not be suitable for efficient Transformers. To address this issue, we propose a new framework to find optimal architectures for efficient Transformers with the neural architecture search (NAS) technique. The proposed method is validated on popular machine translation and image classification tasks. We observe that the optimal architecture of the efficient Transformer has the reduced computation compared with that of the standard Transformer, but the general accuracy is less comparable. It indicates that the Softmax attention and efficient attention have their own distinctions but neither of them can simultaneously balance the accuracy and efficiency well. This motivates us to mix the two types of attention to reduce the performance imbalance. Besides the search spaces that commonly used in existing NAS Transformer approaches, we propose a new search space that allows the NAS algorithm to automatically search the attention variants along with architectures. Extensive experiments on WMT' 14 En-De and CIFAR-10 demonstrate that our searched architecture maintains comparable accuracy to the standard Transformer with notably improved computational efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Persistent fast kink magnetohydrodynamic waves detected in a quiescent prominence

Small-scale, cyclic, transverse motions of plasma threads are usually seen in solar prominences, which are often interpreted as magnetohydrodynamic (MHD) waves. Here, we observed small-scale decayless transverse oscillations in a quiescent prominence, and they appear to be omnipresent. The oscillatory periods of the emission intensity and a proxy for the line-of-sight Doppler shift are about half period of the displacement oscillations. This feature agrees well with the fast kink-mode waves in a flux tube. All the moving threads oscillate transversally spatially in phase and exhibit no significant damping throughout the visible segments, indicating that the fast kink MHD waves are persistently powered and ongoing dissipating energy is transferred to the ambient plasma in the quiet corona. However, our calculations suggest that the energy taken by the fast kink MHD waves alone can not support the coronal heating on the quiet Sun.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Quasi-periodic Accelerations of Energetic Particles during a Solar Flare

We report the observation of non-stationary Quasi-Periodic Pulsations (QPPs) in high-energy particles during the impulsive phase of an X4.8 flare on 2002 July 23 (SOL2002-07-23T00:35). The X4.8 flare was simultaneously measured by the Reuven Ramaty High Energy Solar Spectroscopic Imager, Nobeyama Radio Polarimeters, and Nobeyama Radioheliograph. The quasi-period of about 50 s, determined by the wavelet transform, is detected in the Gamma-ray line emission. Using the same method, a quasi-period of about 90 s is found in Gamma-ray continuum, hard X-ray (HXR) and radio emissions during almost the same time. Our observations suggest that the flare QPPs should be associated with energetic ions and nonthermal electrons that quasi-periodically accelerated by the repetitive magnetic reconnection. The different quasi-periods between Gamma-ray line and continuum/HXR/radio emissions indicate an apparent difference in acceleration or propagation between energetic ions and nonthermal electrons of this solar flare.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Representing Videos as Discriminative Sub-graphs for Action Recognition

Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-graphs to represent and encode the discriminative patterns of each action in the videos. Specifically, we present MUlti-scale Sub-graph LEarning (MUSLE) framework that novelly builds space-time graphs and clusters the graphs into compact sub-graphs on each scale with respect to the number of nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each video clip, as graph nodes and takes dense connectivity as graph edges between tubelets. For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition. Extensive experiments are conducted on both Something-Something V1 & V2 and Kinetics-400 datasets, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best reported accuracy of 65.0% on Something-Something V2 validation set.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Rethinking Reinforcement Learning based Logic Synthesis

Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process. However, through extensive experiments, we find out that the learned policy makes decisions independent from the circuit features (i.e., states) and yields an operator sequence that is permutation invariant to some extent in terms of operators. Based on these findings, we develop a new RL-based method that can automatically recognize critical operators and generate common operator sequences generalizable to unseen circuits. Our algorithm is verified on both the EPFL benchmark, a private dataset and a circuit at industrial scale. Experimental results demonstrate that it achieves a good balance among delay, area and runtime, and is practical for industrial usage.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates

In federated learning (FL), a number of devices train their local models and upload the corresponding parameters or gradients to the base station (BS) to update the global model while protecting their data privacy. However, due to the limited computation and communication resources, the number of local trainings (a.k.a. local update) and that of aggregations (a.k.a. global update) need to be carefully chosen. In this paper, we investigate and analyze the optimal trade-off between the number of local trainings and that of global aggregations to speed up the convergence and enhance the prediction accuracy over the existing works. Our goal is to minimize the global loss function under both the delay and the energy consumption constraints. In order to make the optimization problem tractable, we derive a new and tight upper bound on the loss function, which allows us to obtain closed-form expressions for the number of local trainings and that of global aggregations. Simulation results show that our proposed scheme can achieve a better performance in terms of the prediction accuracy, and converge much faster than the baseline schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SEREN: Knowing When to Explore and When to Exploit

Efficient reinforcement learning (RL) involves a trade-off between "exploitative" actions that maximise expected reward and "explorative'" ones that sample unvisited states. To encourage exploration, recent approaches proposed adding stochasticity to actions, separating exploration and exploitation phases, or equating reduction in uncertainty with reward. However, these techniques do not necessarily offer entirely systematic approaches making this trade-off. Here we introduce SElective Reinforcement Exploration Network (SEREN) that poses the exploration-exploitation trade-off as a game between an RL agent -- \exploiter, which purely exploits known rewards, and another RL agent -- \switcher, which chooses at which states to activate a pure exploration policy that is trained to minimise system uncertainty and override Exploiter. Using a form of policies known as impulse control, \switcher is able to determine the best set of states to switch to the exploration policy while Exploiter is free to execute its actions everywhere else. We prove that SEREN converges quickly and induces a natural schedule towards pure exploitation. Through extensive empirical studies in both discrete (MiniGrid) and continuous (MuJoCo) control benchmarks, we show that SEREN can be readily combined with existing RL algorithms to yield significant improvement in performance relative to state-of-the-art algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Stability and convergence of Strang splitting. Part I: Scalar Allen-Cahn equation

We consider a class of second-order Strang splitting methods for Allen-Cahn equations with polynomial or logarithmic nonlinearities. For the polynomial case both the linear and the nonlinear propagators are computed explicitly. We show that this type of Strang splitting scheme is unconditionally stable regardless of the time step. Moreover we establish strict energy dissipation for a judiciously modified energy which coincides with the classical energy up to $\mathcal O(τ)$ where $τ$ is the time step. For the logarithmic potential case, since the continuous-time nonlinear propagator no longer enjoys explicit analytic treatments, we employ a second order in time two-stage implicit Runge--Kutta (RK) nonlinear propagator together with an efficient Newton iterative solver. We prove a maximum principle which ensures phase separation and establish energy dissipation law under mild restrictions on the time step. These appear to be the first rigorous results on the energy dissipation of Strang-type splitting methods for Allen-Cahn equations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Stability and convergence of Strang splitting. Part II: tensorial Allen-Cahn equations

We consider the second-order in time Strang-splitting approximation for vector-valued and matrix-valued Allen-Cahn equations. Both the linear propagator and the nonlinear propagator are computed explicitly. For the vector-valued case, we prove the maximum principle and unconditional energy dissipation for a judiciously modified energy functional. The modified energy functional is close to the classical energy up to $\mathcal O(τ)$ where $τ$ is the splitting step. For the matrix-valued case, we prove a sharp maximum principle in the matrix Frobenius norm. We show modified energy dissipation under very mild splitting step constraints. We exhibit several numerical examples to show the efficiency of the method as well as the sharpness of the results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Strain-dependent structural and electronic reconstructions in long-wavelength WS$_{2}$ moiré superlattices

In long-wavelength moiré superlattices of stacked transition metal dichalcogenides (TMDs), structural reconstruction ubiquitously occurs, which has reported to impact significantly their electronic properties. However, complete microscopic understandings of the interplay between the lattice reconstruction and alteration of electronic properties, and their further response to external perturbations in the reconstructed TMDs moiré superlattice are still lacking. Here, using scanning tunneling microscopy (STM) and scanning tunneling spectroscopy (STS) combined with first-principles calculation, we study the strain-dependent structural reconstruction and its correlated electronic reconstruction in long-wavelength H-type WS$_{2}$ moiré superlattice at nanometer scale. We observe that the long-wavelength WS$_{2}$ moiré superlattices experiencing strong atomic reconstruction transform into a hexagonal array of screw dislocations separating large-sized H-stacked domains. Both the geometry and the moiré wavelength of the moiré superlattice are dramatically tuned by external intralayer heterostrain in our experiment. Remarkably, the STS measurements further demonstrate that the location of the K point in conduction band is modulated sensitively by strain-induced lattice deformation at nanometer scale in this system, with the maximum energy shift reaching up to 300 meV. Our results highlight that intralayer strain plays a vital role in determining structural and electronic properties in TMD moiré superlattice.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Three New Arnoldi-Type Methods for the Quadratic Eigenvalue Problem in Rotor Dynamics

Three new Arnoldi-type methods are presented to accelerate the modal analysis and critical speed analysis of the damped rotor dynamics finite element (FE) model. They are the linearized quadratic eigenvalue problem (QEP) Arnoldi method, the QEP Arnoldi method, and the truncated generalized standard eigenvalue problem (SEP) Arnoldi method. And, they correspond to three reduction subspaces, including the linearized QEP Krylov subspace, the QEP Krylov subspace, and the truncated generalized SEP Krylov subspace, where the first subspace is also used in the existing Arnoldi-type methods. The numerical examples constructed by a turbofan engine low-pressure (LP) rotor demonstrate that our proposed three Arnoldi-type methods are more accurate than the existing Arnoldi-type methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals

The peer merit review of research proposals has been the major mechanism to decide grant awards. Nowadays, research proposals have become increasingly interdisciplinary. It has been a longstanding challenge to assign proposals to appropriate reviewers. One of the critical steps in reviewer assignment is to generate accurate interdisciplinary topic labels for proposals. Existing systems mainly collect topic labels manually reported by discipline investigators. However, such human-reported labels can be non-accurate and incomplete. What role can AI play in developing a fair and precise proposal review system? In this evidential study, we collaborate with the National Science Foundation of China to address the task of automated interdisciplinary topic path detection. For this purpose, we develop a deep Hierarchical Interdisciplinary Research Proposal Classification Network (HIRPCN). We first propose a hierarchical transformer to extract the textual semantic information of proposals. We then design an interdisciplinary graph and leverage GNNs to learn representations of each discipline in order to extract interdisciplinary knowledge. After extracting the semantic and interdisciplinary knowledge, we design a level-wise prediction component to fuse the two types of knowledge representations and detect interdisciplinary topic paths for each proposal. We conduct extensive experiments and expert evaluations on three real-world datasets to demonstrate the effectiveness of our proposed model.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Detections of Multi-Periodic Oscillations during a Circular Ribbon Flare

We present the analysis of three kinds of oscillating behavior using multi-wavelength observations of the 10 November 2013 (SOL2013-11-10T05:14) circular-ribbon flare. This event is a typical circular-ribbon flare with an outer spine structure and homologous jets. We found three kinds of oscillations (or perturbations): i) flux oscillation (or QPP) with a dominant period of about 20 seconds in X-ray, EUV, and microwave emissions, ii) periodic jets with an intermittent cadence of around 72 seconds, iii) an outer loop perturbing half a cycle with a duration of about 168 seconds. Similar to the periodic jets that could be produced by a nonthermal process, like repeated magnetic reconnection, the flare QPP detected in the thermal emissions could have the same origin as the oscillation seen in the nonthermal emissions. The outer-loop perturbation is possibly triggered by a blast wave driven by the circular-ribbon flare, or it might be modulated by the sausage wave or the slow magnetoacoustic wave. The results obtained provide data for further numerical studies on the physical origin of the flare oscillations.

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preprint2021arXiv

Diagnosing a Solar Flaring Core with Bidirectional Quasi-Periodic Fast Propagating Magnetoacoustic Waves

Quasi-periodic fast propagating (QFP) waves are often excited by solar flares, and could be trapped in the coronal structure with low Alfvén speed, so they could be used as a diagnosing tool for both the flaring core and magnetic waveguide. As the periodicity of a QFP wave could originate from a periodic source or be dispersively waveguided, it is a key parameter for diagnosing the flaring core and waveguide. In this paper, we study two QFP waves excited by a GOES-class C1.3 solar flare occurring at active region NOAA 12734 on 8 March 2019. Two QFP waves were guided by two oppositely oriented coronal funnel. The periods of two QFP waves were identical and were roughly equal to the period of the oscillatory signal in the X-ray and 17 GHz radio emission released by the flaring core. It is very likely that the two QFP waves could be periodically excited by the flaring core. Many features of this QFP wave event is consistent with the magnetic tuning fork model. We also investigated the seismological application with QFP waves, and found that the magnetic field inferred with magnetohydrodynamic seismology was consistent with that obtained in magnetic extrapolation model. Our study suggest that the QFP wave is a good tool for diagnosing both the flaring core and the magnetic waveguide.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Dispersionless orbital excitations in (Li,Fe)OHFeSe superconductors

The superconducting critical temperature $T_{\mathrm{c}}$ of intercalated iron-selenide superconductor (Li,Fe)OHFeSe (FeSe11111) can be increased to 42 K from 8 K of bulk FeSe. It shows remarkably similar electronic properties as the high-$T_{\mathrm{c}}$ monolayer FeSe and provides a bulk counterpart to investigate the origin of enhanced superconductivity. Unraveling the nature of excitations is crucial for understanding the pairing mechanism in high-$T_{\mathrm{c}}$ iron selenides. Here we use resonant inelastic x-ray scattering (RIXS) to investigate the excitations in FeSe11111. Our high-quality data exhibit several Raman-like excitations, which are dispersionless and isotropic in momentum transfer and robust against varying $T_{\mathrm{c}}$. Using atomic multiplet calculations, we assign the low-energy $\sim 0.3$ and 0.7 eV Raman peaks as local $e_g-e_g$ and $e_g-t_{2g}$ orbital excitations. The intensity of these two features decreases with increasing temperature, suggesting a primary contribution of the orbital fluctuations. Our results highlight the importance of orbital degree of freedom for high-$T_{\mathrm{c}}$ iron selenides.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Excess-noise suppression for a squeezed state propagating through random amplifying media via wave-front shaping

After propagating through a random amplifying medium, a squeezed state commonly shows excess noise above the shot-noise level. Since large noise can significantly reduce the signal-to-noise ratio, it is detrimental for precision measurement. To circumvent this problem, we propose a noise-reduction scheme using wavefront shaping. It is demonstrated that the average output quantum noise can be effectively suppressed even beyond the shot-noise limit. Both the decrease on amplification strength and the increase on input squeezing strength can give rise to a decrease in the suppressed average quantum noise. Our results not only show the feasibility of manipulating the output quantum noise of random amplifying media, but also indicate potential applications in quantum information processing in complex environments, such as, quantum imaging, quantum communication, and quantum key distribution.

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preprint2021arXiv

MATCH: An MPI Fault Tolerance Benchmark Suite

MPI has been ubiquitously deployed in flagship HPC systems aiming to accelerate distributed scientific applications running on tens of hundreds of processes and compute nodes. Maintaining the correctness and integrity of MPI application execution is critical, especially for safety-critical scientific applications. Therefore, a collection of effective MPI fault tolerance techniques have been proposed to enable MPI application execution to efficiently resume from system failures. However, there is no structured way to study and compare different MPI fault tolerance designs, so to guide the selection and development of efficient MPI fault tolerance techniques for distinct scenarios. To solve this problem, we design, develop, and evaluate a benchmark suite called MATCH to characterize, research, and comprehensively compare different combinations and configurations of MPI fault tolerance designs. Our investigation derives useful findings: (1) Reinit recovery in general performs better than ULFM recovery; (2) Reinit recovery is independent of the scaling size and the input problem size, whereas ULFM recovery is not; (3) Using Reinit recovery with FTI checkpointing is a highly efficient fault tolerance design. MATCH code is available at https://github.com/kakulo/MPI- FT- Bench.

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preprint2021arXiv

MOARD: Modeling Application Resilience to Transient Faults on Data Objects

Understanding application resilience (or error tolerance) in the presence of hardware transient faults on data objects is critical to ensure computing integrity and enable efficient application-level fault tolerance mechanisms. However, we lack a method and a tool to quantify application resilience to transient faults on data objects. The traditional method, random fault injection, cannot help, because of losing data semantics and insufficient information on how and where errors are tolerated. In this paper, we introduce a method and a tool (called MOARD) to model and quantify application resilience to transient faults on data objects. Our method is based on systematically quantifying error masking events caused by application-inherent semantics and program constructs. We use MOARD to study how and why errors in data objects can be tolerated by the application. We demonstrate tangible benefits of using MOARD to direct a fault tolerance mechanism to protect data objects.

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preprint2021arXiv

On Estimating Recommendation Evaluation Metrics under Sampling

Since the recent study (Krichene and Rendle 2020) done by Krichene and Rendle on the sampling-based top-k evaluation metric for recommendation, there has been a lot of debates on the validity of using sampling to evaluate recommendation algorithms. Though their work and the recent work (Li et al.2020) have proposed some basic approaches for mapping the sampling-based metrics to their global counterparts which rank the entire set of items, there is still a lack of understanding and consensus on how sampling should be used for recommendation evaluation. The proposed approaches either are rather uninformative (linking sampling to metric evaluation) or can only work on simple metrics, such as Recall/Precision (Krichene and Rendle 2020; Li et al. 2020). In this paper, we introduce a new research problem on learning the empirical rank distribution, and a new approach based on the estimated rank distribution, to estimate the top-k metrics. Since this question is closely related to the underlying mechanism of sampling for recommendation, tackling it can help better understand the power of sampling and can help resolve the questions of if and how should we use sampling for evaluating recommendation. We introduce two approaches based on MLE (MaximalLikelihood Estimation) and its weighted variants, and ME(Maximal Entropy) principals to recover the empirical rank distribution, and then utilize them for metrics estimation. The experimental results show the advantages of using the new approaches for evaluating recommendation algorithms based on top-k metrics.

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preprint2021arXiv

Simultaneous Observations of Chromospheric Evaporation and Condensation during a C-class Flare

We explored simultaneous observations of chromospheric evaporation and condensation during the impulsive phase of a C6.7 flare on 9 May 2019. The solar flare was simultaneously observed by multiple instruments, i.e., the New Vacuum Solar Telescope (NVST), the Interface Region Imaging Spectrograph, the Atmospheric Imaging Assembly (AIA), the Fermi, the Mingantu Spectral Radioheliograph, and the Nobeyama Radio Polarimeters. Using the single Gaussian fitting and the moment analysis technique, redshifted velocities at slow speeds of 15-19 km/s are found in the cool lines of C II and Si IV at one flare footpoint location. Red shifts are also seen in the H-alpha line-of-sight (LOS) velocity image measured by the NVST at double footpoints. Those red shifts with slow speeds can be regarded as the low-velocity downflows driven by the chromospheric condensation. Meanwhile, the converging motions from double footpoints to the loop top are found in the high-temperature EUV images, such as AIA 131 A, 94 A, and 335 A. Their apparent speeds are estimated to be roughly 126-210 km/s, which could be regarded as the high-velocity upflows caused by the chromospheric evaporation. The nonthermal energy flux is estimated to be about 5.7x10^10 erg/s/cm^2. The characteristic timescale is roughly equal to 1 minute. All these observational results suggest an explosive chromospheric evaporation during the flare impulsive phase. While a HXR/microwave pulse and a type III radio burst are found simultaneously, indicating that the explosive chromospheric evaporation is driven by the nonthermal electron.

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preprint2021arXiv

Solving phase retrieval with random initial guess is nearly as good as by spectral initialization

The problem of recovering a signal $\mathbf{x}\in \mathbb{R}^n$ from a set of magnitude measurements $y_i=|\langle \mathbf{a}_i, \mathbf{x} \rangle |, \; i=1,\ldots,m$ is referred as phase retrieval, which has many applications in fields of physical sciences and engineering. In this paper we show that the smoothed amplitude flow model for phase retrieval has benign geometric structure under the optimal sampling complexity. In particular, we show that when the measurements $\mathbf{a}_i\in \mathbb{R}^n$ are Gaussian random vectors and the number of measurements $m\ge Cn$, our smoothed amplitude flow model has no spurious local minimizers with high probability, ie., the target solution $\mathbf{x}$ is the unique global minimizer (up to a global phase) and the loss function has a negative directional curvature around each saddle point. Due to this benign geometric landscape, the phase retrieval problem can be solved by the gradient descent algorithms without spectral initialization. Numerical experiments show that the gradient descent algorithm with random initialization performs well even comparing with state-of-the-art algorithms with spectral initialization in empirical success rate and convergence speed.

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preprint2021arXiv

ZeRO-Offload: Democratizing Billion-Scale Model Training

Large-scale model training has been a playing ground for a limited few requiring complex model refactoring and access to prohibitively expensive GPU clusters. ZeRO-Offload changes the large model training landscape by making large model training accessible to nearly everyone. It can train models with over 13 billion parameters on a single GPU, a 10x increase in size compared to popular framework such as PyTorch, and it does so without requiring any model change from the data scientists or sacrificing computational efficiency. ZeRO-Offload enables large model training by offloading data and compute to CPU. To preserve compute efficiency, it is designed to minimize the data movement to/from GPU, and reduce CPU compute time while maximizing memory savings on GPU. As a result, ZeRO-Offload can achieve 40 TFlops/GPU on a single NVIDIA V100 GPU for 10B parameter model compared to 30TF using PyTorch alone for a 1.4B parameter model, the largest that can be trained without running out of memory. ZeRO-Offload is also designed to scale on multiple-GPUs when available, offering near linear speedup on up to 128 GPUs. Additionally, it can work together with model parallelism to train models with over 70 billion parameters on a single DGX-2 box, a 4.5x increase in model size compared to using model parallelism alone. By combining compute and memory efficiency with ease-of-use, ZeRO-Offload democratizes large-scale model training making it accessible to even data scientists with access to just a single GPU.

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preprint2020arXiv

Adaptive Neural Network-Based Approximation to Accelerate Eulerian Fluid Simulation

The Eulerian fluid simulation is an important HPC application. The neural network has been applied to accelerate it. The current methods that accelerate the fluid simulation with neural networks lack flexibility and generalization. In this paper, we tackle the above limitation and aim to enhance the applicability of neural networks in the Eulerian fluid simulation. We introduce Smartfluidnet, a framework that automates model generation and application. Given an existing neural network as input, Smartfluidnet generates multiple neural networks before the simulation to meet the execution time and simulation quality requirement. During the simulation, Smartfluidnet dynamically switches the neural networks to make the best efforts to reach the user requirement on simulation quality. Evaluating with 20,480 input problems, we show that Smartfluidnet achieves 1.46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model.

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preprint2020arXiv

An ultra-long and quite thin coronal loop without significant expansion

Context. Coronal loops are the basic building blocks of the solar corona, which are related to the mass supply and heating of solar plasmas in the corona. However, their fundamental magnetic structures are still not well understood. Most coronal loops do not expand significantly, whereas the diverging magnetic field would have an expansion factor of about 5-10 over one pressure scale height. Aims. In this study, we investigate a unique coronal loop with a roughly constant cross section, it is ultra long and quite thin. A coronal loop model with magnetic helicity is presented to explain the small expansion of the loop width. Methods. This coronal loop was predominantly detectable in the 171 A channel of the Atmospheric Imaging Assembly (AIA). Then, the local magnetic field line was extrapolated by a Potential-Field-Source-Surface model. Finally, the differential emission measure analysis made from six AIA bandpasses was applied to obtain the thermal properties of this loop. Results. This coronal loop has a projected length of roughly 130 Mm, a width of about 1.5 +(-) 0.5 Mm and a lifetime of around 90 minutes. It follows an open magnetic field line. The cross section expanded very little (i.e., 1.5-2.0) along the loop length during its whole lifetime. This loop has a nearly constant temperature at about 0.7 +(-) 0.2 MK, whereas its density exhibits the typical structure of a stratified atmosphere. Conclusions. We use a thin twisted flux tube theory to construct a model for this non-expanding loop, and find that indeed with sufficient twist a coronal loop can attain equilibrium. However, we can not rule out other possibilities such as footpoint heating by small-scale reconnection, elevated scale height by a steady flow along the loop etc.

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preprint2020arXiv

Anomalies of weight-based coherence measure and mixed maximally coherent states

As an analogy of best separable approximation (BSA) in the framework of entanglement theory, here we concentrate on the notion of best incoherent approximation, with application to characterizing and quantifying quantum coherence. From both analytical and numerical perspectives, we have demonstrated that the weight-based coherence measure displays some unusual properties, in sharp contrast to other popular coherence quantifiers. First, by deriving a closed formula for qubit states, we have showed the weight-based coherence measure exhibits a rich (geometrical) structure even in this simplest case. Second, we have identified the existence of mixed maximally coherent states (MMCS) with respect to this coherence measure and discussed the characteristic feature of MMCS in high-dimensional Hilbert spaces. Especially, we present several important families of MMCS by gaining insights from the numerical simulations. Moreover, it is pointed out that some considerations in this work can be generalized to general convex resource theories and a numerical method of improving the computational efficiency for finding the BSA is also discussed.

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preprint2020arXiv

Asymptotic Behaviour of Time Stepping Methods for Phase Field Models

Adaptive time stepping methods for metastable dynamics of the Allen Cahn and Cahn Hilliard equations are investigated in the spatially continuous, semi-discrete setting. We analyse the performance of a number of first and second order methods, formally predicting step sizes required to satisfy specified local truncation error $σ$ in the limit of small order parameter $ε\rightarrow 0$ during meta-stable dynamics. The formal predictions are made under stability assumptions that include the preservation of the asymptotic structure of the diffuse interface, a concept we call profile fidelity. In this setting, definite statements about the relative behaviour of time stepping methods can be made. Some methods, including all so-called energy stable methods but also some fully implicit methods, require asymptotically more time steps than others.The formal analysis is confirmed in computational studies. We observe that some provably energy stable methods popular in the literature perform worse than some more standard schemes. We show further that when Backward Euler is applied to meta-stable Allen Cahn dynamics, the energy decay and profile fidelity properties for these discretizations are preserved for much larger time steps than previous analysis would suggest. The results are established asymptotically for general interfaces, with a rigorous proof for radial interfaces. It is shown analytically and computationally that for most reaction terms, Eyre type time stepping performs asymptotically worse due to loss of profile fidelity.

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preprint2020arXiv

Deep Phase Shifter for Quantitative Phase Imaging

A single intensity-only holographic interferogram can records the full amplitude and phase information of optical field. However, current digital holography technologies cannot recover the lossless phase information from a single interferogram. In this paper, we provide an entirely new approach for the full-field quantitative phase imaging technology. We demonstrate that deep learning can be used to replace the entitative phase shifter, and quantitative phase imaging can obtain quantitative phase from a single interferogram in in-line holography. A deep-phase-shift network (DPS-net) is reported, which can be trained with simulation training data. The trained DPS-net can be used to generate multiple interferograms with arbitrary phase shift from a single interferogram as an artificial intelligence phase shifter. The ability and the accuracy of generating arbitrary phase shifts are verified, and the performance of the proposed method is also verified by the experimental interferogram. The results demonstrate that the proposed method can provide a full digital phase shifter with high-accuracy for the technology of dynamic quantitative phase measurement.

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preprint2020arXiv

Demystifying the Performance of HPC Scientific Applications on NVM-based Memory Systems

The emergence of high-density byte-addressable non-volatile memory (NVM) is promising to accelerate data- and compute-intensive applications. Current NVM technologies have lower performance than DRAM and, thus, are often paired with DRAM in a heterogeneous main memory. Recently, byte-addressable NVM hardware becomes available. This work provides a timely evaluation of representative HPC applications from the "Seven Dwarfs" on NVM-based main memory. Our results quantify the effectiveness of DRAM-cached-NVM for accelerating HPC applications and enabling large problems beyond the DRAM capacity. On uncached-NVM, HPC applications exhibit three tiers of performance sensitivity, i.e., insensitive, scaled, and bottlenecked. We identify write throttling and concurrency control as the priorities in optimizing applications. We highlight that concurrency change may have a diverging effect on read and write accesses in applications. Based on these findings, we explore two optimization approaches. First, we provide a prediction model that uses datasets from a small set of configurations to estimate performance at various concurrency and data sizes to avoid exhaustive search in the configuration space. Second, we demonstrate that write-aware data placement on uncached-NVM could achieve $2$x performance improvement with a 60% reduction in DRAM usage.

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preprint2020arXiv

Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models

The special epistemic characteristics of the COVID-19, such as the long incubation period and the infection through asymptomatic cases, put severe challenge to the containment of its outbreak. By the end of March 2020, China has successfully controlled the within-spreading of COVID-19 at a high cost of locking down most of its major cities, including the epicenter, Wuhan. Since the low accuracy of outbreak data before the mid of Feb. 2020 forms a major technical concern on those studies based on statistic inference from the early outbreak. We apply the supervised learning techniques to identify and train NP-Net-SIR model which turns out robust under poor data quality condition. By the trained model parameters, we analyze the connection between population flow and the cross-regional infection connection strength, based on which a set of counterfactual analysis is carried out to study the necessity of lock-down and substitutability between lock-down and the other containment measures. Our findings support the existence of non-lock-down-typed measures that can reach the same containment consequence as the lock-down, and provide useful guideline for the design of a more flexible containment strategy.

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preprint2020arXiv

Feedback Graph Convolutional Network for Skeleton-based Action Recognition

Skeleton-based action recognition has attracted considerable attention in computer vision since skeleton data is more robust to the dynamic circumstance and complicated background than other modalities. Recently, many researchers have used the Graph Convolutional Network (GCN) to model spatial-temporal features of skeleton sequences by an end-to-end optimization. However, conventional GCNs are feedforward networks which are impossible for low-level layers to access semantic information in the high-level layers. In this paper, we propose a novel network, named Feedback Graph Convolutional Network (FGCN). This is the first work that introduces the feedback mechanism into GCNs and action recognition. Compared with conventional GCNs, FGCN has the following advantages: (1) a multi-stage temporal sampling strategy is designed to extract spatial-temporal features for action recognition in a coarse-to-fine progressive process; (2) A dense connections based Feedback Graph Convolutional Block (FGCB) is proposed to introduce feedback connections into the GCNs. It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions. In the early stages, the model receives partial information about actions. Naturally, its predictions are relatively coarse. The coarse predictions are treated as the prior to guide the feature learning of later stages for a accurate prediction. Extensive experiments on the datasets, NTU-RGB+D, NTU-RGB+D120 and Northwestern-UCLA, demonstrate that the proposed FGCN is effective for action recognition. It achieves the state-of-the-art performance on the three datasets.

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preprint2020arXiv

FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processors

How to accurately and efficiently label data on a mobile device is critical for the success of training machine learning models on mobile devices. Auto-labeling data on mobile devices is challenging, because data is usually incrementally generated and there is possibility of having unknown labels. Furthermore, the rich hardware heterogeneity on mobile devices creates challenges on efficiently executing auto-labeling workloads. In this paper, we introduce Flame, an auto-labeling system that can label non-stationary data with unknown labels. Flame includes a runtime system that efficiently schedules and executes auto-labeling workloads on heterogeneous mobile processors. Evaluating Flame with eight datasets on a smartphone, we demonstrate that Flame enables auto-labeling with high labeling accuracy and high performance.

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preprint2020arXiv

Large-scale Real-time Personalized Similar Product Recommendations

Similar product recommendation is one of the most common scenes in e-commerce. Many recommendation algorithms such as item-to-item Collaborative Filtering are working on measuring item similarities. In this paper, we introduce our real-time personalized algorithm to model product similarity and real-time user interests. We also introduce several other baseline algorithms including an image-similarity-based method, item-to-item collaborative filtering, and item2vec, and compare them on our large-scale real-world e-commerce dataset. The algorithms which achieve good offline results are also tested on the online e-commerce website. Our personalized method achieves a 10% improvement on the add-cart number in the real-world e-commerce scenario.

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preprint2020arXiv

Long Short-Term Relation Networks for Video Action Detection

It has been well recognized that modeling human-object or object-object relations would be helpful for detection task. Nevertheless, the problem is not trivial especially when exploring the interactions between human actor, object and scene (collectively as human-context) to boost video action detectors. The difficulty originates from the aspect that reliable relations in a video should depend on not only short-term human-context relation in the present clip but also the temporal dynamics distilled over a long-range span of the video. This motivates us to capture both short-term and long-term relations in a video. In this paper, we present a new Long Short-Term Relation Networks, dubbed as LSTR, that novelly aggregates and propagates relation to augment features for video action detection. Technically, Region Proposal Networks (RPN) is remoulded to first produce 3D bounding boxes, i.e., tubelets, in each video clip. LSTR then models short-term human-context interactions within each clip through spatio-temporal attention mechanism and reasons long-term temporal dynamics across video clips via Graph Convolutional Networks (GCN) in a cascaded manner. Extensive experiments are conducted on four benchmark datasets, and superior results are reported when comparing to state-of-the-art methods.

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preprint2020arXiv

Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning

Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation. Meanwhile, only cognitive consistency within a neighborhood matters because humans only interact directly with their neighbors. Inspired by these observations, we take the first step to introduce \emph{neighborhood cognitive consistency} (NCC) into multi-agent reinforcement learning (MARL). Our NCC design is quite general and can be easily combined with existing MARL methods. As examples, we propose neighborhood cognition consistent deep Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations. Extensive experiments on several challenging tasks (i.e., packet routing, wifi configuration, and Google football player control) justify the superior performance of our methods compared with state-of-the-art MARL approaches.

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preprint2020arXiv

Observations of a quasi-periodic pulsation in the coronal loop and microwave flux during a solar preflare phase

We report a quasi-periodic pulsation (QPP) event simultaneously detected from the spatial displacements of coronal loop at both EUV images and microwave emission during the preflare phase of a C1.1 flare on 2016 March 23. Using the motion magnification technique, a low-amplitude transverse oscillation with the growing period is discovered in a diffuse coronal loop in Atmospheric Imaging Assembly (AIA) image sequences at wavelength of 171 A, and the initial oscillation period is estimated to be ~397 s with a slow growth rate of 0.045. At the same time, a QPP with growing periods from roughly 300 s to nearly 500 s is discovered in the microwave flux in the same active region. Based on the imaging observations measured at EUV wavelengths by the AIA and at microwave 17 GHz by Nobeyama Radioheliograph, the diffuse coronal loop and the microwave radiation source are found to be connected through a hot loop seen in AIA images at wavelength of 94 A. The growing period of the QPP should be related to the modulation of LRC-circuit oscillating process in a current-carrying plasma loop. The existence of electric currents may imply the non-potentialities in the source region during the preflare phase.

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preprint2020arXiv

Preflare very long-periodic pulsations observed in Halpha emission before the onset of a solar flare

Very long-periodic pulsations during preflare phases (preflare-VLPs) have been detected in the full-disk solar soft X-Ray (SXR) flux (see Tan et al. 2016). They may be regarded as precursors to solar flares and may help us better understand the trigger mechanism of solar flares. In this letter, we report a preflare-VLP event before the onset of an M1.1 circular-ribbon flare on 2015 October 16. It was simultaneously observed in Halpha, SXR, and extreme ultraviolet (EUV) wavelengths, which were recorded by the NVST, GOES, EVE, and AIA respectively. The preflare-VLP is identified as the repeat and quasi-periodic pulses in light curves during preflare phase, which might be modulated by LRC-circuit oscillation in the current-carrying plasma loop. The quasi-periodicity can be determined from the Fourier power spectrum with Markov chain Monte Carlo (MCMC)-based Bayesian (e.g., Liang et al. 2020), such as ~9.3 minutes. We present the first report of a preflare-VLP event in the local Halpha line and EUV wavelength, which could be considered a precursor of a solar flare. This finding should therefore prove useful for the prediction of solar flares, especially for powerful flares.

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preprint2020arXiv

Quasi-periodic pulsation detected in Lyman-alpha emission during solar flares

We investigated the quasi-periodic pulsation (QPP) in Lyman-alpha, X-ray and extreme-ultraviolet (EUV) emissions during two solar flares, i.e., an X-class (SOL2012-01-27T) and a C-class (SOL2016-02-08T). The full-disk Lyman-alpha and X-Ray flux during these solar flares were recorded by the EUV Sensor and X-Ray Sensor on board the Geostationary Operational Environmental Satellite. The °are regions were located from the EUV images measured by the Atmospheric Imaging Assembly. The QPP could be identified as a series of regular and periodic peaks in the light curves, and its quasi-periodicity was determined from the global wavelet and Fourier power spectra. A quasi-periodicity at about 3 minutes is detected during the impulsive phase of the X-class flare, which could be explained as the acoustic wave in the chromosphere (e.g., Milligan et al. 2017). Interestingly, a quasi-periodicity at roughly 1 minute is discovered during the entire evolutionary phases of solar flares, including the precursor, impulsive, and gradual phases. This is the first report of 1-minute QPP in the Lyman-alpha emission during solar flares, in particular during the flare precursor. It may be interpreted as a self-oscillatory regime of the magnetic reconnection, such as magnetic dripping.

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preprint2020arXiv

Remarks on a nonlocal transport

We consider a one dimensional nonlocal transport equation and its natural multi-dimensional analogues. By using a new pointwise inequality for the Hilbert transform, we give a short proof of a nonlinear inequality first proved by Córdoba, Córdoba and Fontelos in 2005. We also prove several new weighted inequalities for the Hilbert transform and various nonlinear versions. Some of these results generalize to a related family of nonlocal models.

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preprint2020arXiv

Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation

The optimal power flow (OPF) problem is one of the most important optimization problems for the operation of the power grid. It calculates the optimum scheduling of the committed generation units. In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution. The high quality of the initial solution and guidance of other outputs generated by the neural network enables faster convergence to the solution without losing optimality of final solution as computed by traditional methods. Smart-PGSim generates a novel multitask-learning neural network model to accelerate the AC-OPF simulation. Smart-PGSim also imposes the physical constraints of the simulation on the neural network automatically. Smart-PGSim brings an average of 49.2% performance improvement (up to 91%), computed over 10,000 problem simulations, with respect to the original AC-OPF implementation, without losing the optimality of the final solution.

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preprint2020arXiv

Stability analysis for the Implicit-Explicit discretization of the Cahn-Hilliard equation

Implicit-Explicit methods have been widely used for the efficient numerical simulation of phase field problems such as the Cahn-Hilliard equation or thin film type equations. Due to the lack of maximum principle and stiffness caused by the effect of small dissipation coefficient, most existing theoretical analysis relies on adding additional stabilization terms, mollifying the nonlinearity or introducing auxiliary variables which implicitly either changes the structure of the problem or trades accuracy for stability in a subtle way. In this work we introduce a robust theoretical framework to analyze directly the stability of the standard implicit-explicit approach without stabilization or any other modification. We take the Cahn-Hilliard equation as a model case and prove energy stability under natural time step constraints which are optimal with respect to energy scaling. These settle several questions which have been open since the work of Chen and Shen \cite{CS98}.

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preprint2020arXiv

Testing error distribution by kernelized Stein discrepancy in multivariate time series models

Knowing the error distribution is important in many multivariate time series applications. To alleviate the risk of error distribution mis-specification, testing methodologies are needed to detect whether the chosen error distribution is correct. However, the majority of the existing tests only deal with the multivariate normal distribution for some special multivariate time series models, and they thus can not be used to testing for the often observed heavy-tailed and skewed error distributions in applications. In this paper, we construct a new consistent test for general multivariate time series models, based on the kernelized Stein discrepancy. To account for the estimation uncertainty and unobserved initial values, a bootstrap method is provided to calculate the critical values. Our new test is easy-to-implement for a large scope of multivariate error distributions, and its importance is illustrated by simulated and real data.

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preprint2019arXiv

A Compact Source for Quasi-Periodic Pulsation in an M-class Solar Flare

Quasi-periodic pulsations (QPP) are usually found in the light curves of solar and stellar flares, they carry the features of time characteristics and plasma emission of the flaring core, and could be used to diagnose the coronas of the Sun and remote stars. In this study, we combined the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory and the Nobeyama Radioheliograph (NoRH) to observe an M7.7 class flare occurred at active region 11520 on 19 July 2012. A QPP was detected both in the AIA $131\unit{Å}$ bandpass and the NoRH $17\unit{GHz}$ channel, it had a period of about four minutes. In the spatial distribution of Fourier power, we found that this QPP originated from a compact source and that it overlapped with the X-ray source above the loop top. The plasma emission intensities in the AIA $131\unit{Å}$ bandpass were highly correlated within this region. The source region is further segmented into stripes that oscillated with distinctive phases. Evidence in this event suggests that this QPP was likely to be generated by intermittent energy injection into the reconnection region.

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preprint2019arXiv

Emergent superconductivity in single crystalline $\mathrm{MgTi}_2\mathrm{O}_4$ films via structural engineering

Spinel compounds have demonstrated rich functionalities but rarely shown superconductivity. Here, we report the emergence of superconductivity in the spinel $\mathrm{MgTi}_2\mathrm{O}_4$, known to be an insulator with a complicated order. The superconducting transition is achieved by engineering a superlattice of $\mathrm{MgTi}_2\mathrm{O}_4$ and $\mathrm{SrTiO}_3$. The onset transition temperature in the $\mathrm{MgTi}_2\mathrm{O}_4$ layer can be tuned from 0 to 5 K in such geometry, concurrently with a stretched $c$-axis (from 8.51 to 8.53 Å) compared to the bulk material. Such a positive correlation without saturation suggests ample room for the further enhancement. Intriguingly, the superlattice exhibits isotropic upper critical field $H_{\mathrm{c}2}$ that breaks the Pauli limit, distinct from the highly anisotropic feature of interface superconductivity. The origin of superconductivity in the $\mathrm{MgTi}_2\mathrm{O}_4$ layer is understood in combination with the electron energy loss spectra and the first-principles electronic structure calculations, which point to the birth of superconductivity in the $\mathrm{MgTi}_2\mathrm{O}_4$ layer by preventing the Ti-Ti dimerization. Our discovery not only provides a platform to explore the interplay between the superconductivity and other exotic states, but also opens a new window to realize superconductivity in the spinel compounds as well as other titanium oxides.

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preprint2019arXiv

Modulating quantum fluctuations of scattered lights in disordered media via wavefront shaping

After multiple scattering of quadrature-squeezed lights in a disordered medium, the quadrature amplitudes of the scattered modes present an excess noise above the shot-noise level [Opt. Expr. 14, 6919 (2006)]. A natural question is raised whether there exists a method of suppressing the quadrature fluctuation of the output mode. The answer is affirmative. In this work, we prove that wavefront shaping is a promising method to reduce the quantum noise of quadrature amplitudes of the scattered modes. This reduction is owing to the destructive interference of quantum noise. Specifically, when the single-mode squeezed states are considered as inputs, the quantum fluctuation can always be reduced, even below the shot-noise level. These results may have potential applications in quantum information processing, for instance, sub-wavelength imaging using the scattering superlens with squeezed-state sources.

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preprint2019arXiv

Non-Volatile Superconductivity in an Insulating Copper Oxide Induced via Ionic Liquid Gating

Manipulating the superconducting states of high-T_c cuprate superconductors in an efficient and reliable way is of great importance for their applications in next-generation electronics. Traditional methods are mostly based on a trial-and-error method that is difficult to implement and time consuming. Here, employing ionic liquid gating, a selective control of volatile and non-volatile superconductivity is achieved in pristine insulating Pr_2CuO_{4\pmδ} film, based on two distinct mechanisms: 1) with positive electric fields, the film can be reversibly switched between non-superconducting and superconducting states, attributed to the carrier doping effect. 2) The film becomes more resistive by applying negative bias voltage up to -4 V, but strikingly, a non-volatile superconductivity is achieved once the gate voltage is removed. Such a persistent superconducting state represents a novel phenomenon in copper oxides, resulting from the doping healing of oxygen vacancies in copper-oxygen planes as unraveled by high-resolution scanning transmission electron microscope and in-situ x-ray diffraction experiments. The effective manipulation and mastering of volatile/non-volatile superconductivity in the same parent cuprate opens the door to more functionalities for superconducting electronics, as well as supplies flexible samples for investigating the nature of quantum phase transitions in high-T_c superconductors.

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preprint2019arXiv

Spectroscopic and Stereoscopic Observations of the Solar Jets

We present a comprehensive study of a series of recurrent jets that occurred at the periphery of the NOAA active region 12114 on 2014 July 7. These jets were found to share the same source region and exhibited rotational motions as they propagated outward. The multi-wavelength imaging observations made by the AIA and {\it IRIS} telescopes reveal that some of the jets contain cool plasma only, while some others contain not only cool but also hot plasma. The Doppler velocities calculated from the {\it IRIS} spectra show a continuous evolution from blue to red shifts as the jet motions change from upward to downward. Additionally, some jets exhibit opposite Doppler shifts on their both sides, indicative of rotating motions along their axes. The inclination angle and three-dimensional velocity of the largest jet were inferred from the imaging and spectroscopic observations, which show a high consistence with those derived from the stereoscopic analysis using dual-perspective observations by {\it SDO}/AIA and {\it STEREO}-B/EUVI. By relating the jets to the local UV/EUV and full-disk {\it GOES} X-ray emission enhancements, we found that the previous five small-scale jets were triggered by five bright points while the last/largest one was triggered by a C1.6 solar flare. Together with a number of type III radio bursts generated during the jet eruptions as well as a weak CME that was observed in association with the last jet, our observations provide evidences in support of multi-scale magnetic reconnection processes being responsible for the production of jet events.

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