arXiv subjects

Dong Li

Publications and source records attributed to Dong Li.

At least 325 records · Page 18Linked to original sources

arXiv2023

Energetics of a solar flare and a coronal mass ejection generated by a hot channel eruption

Hot channels (HCs) are prevalent in the solar corona and play a critical role in driving flares and CMEs. In this paper, we estimate the energy contents of an X1.4 eruptive flare with a fast CME generated by a HC eruption on 2011 September 22. Originating from NOAA AR11302, the HC is the most dramatic feature in 131 and 94 Å images observed by SDO/AIA. The flare is simultaneously observed by SDO/AIA, RHESSI, and STEREO-B/EUVI. The CME is simultaneously detected by the white-light coronagraphs of SOHO/LASCO and STEREO-B/COR1. Using multiwavelength and multiview observations of the eruption, various energy components of the HC, flare, and CME are calculated. The thermal and kinetic energies of the HC are (1.77$\pm$0.61)$\times10^{30}$ erg and (2.90$\pm$0.79)$\times10^{30}$ erg, respectively. The peak thermal energy of the flare and total radiative loss of SXR-emitting plasma are (1.63$\pm$0.04)$\times10^{31}$ erg and (1.03$-$1.31)$\times10^{31}$ erg, respectively. The ratio between the thermal energies of HC and flare is 0.11$\pm$0.03, suggesting that thermal energy of the HC is not negligible. The kinetic and potential energies of the CME are (3.43$\pm$0.94)$\times10^{31}$ erg and (2.66$\pm$0.49)$\times10^{30}$ erg, yielding a total energy of (3.69$\pm$0.98)$\times10^{31}$ erg for the CME. Continuous heating of the HC is required to balance the rapid cooling by heat conduction, which probably originate from intermittent magnetic reconnection at the flare current sheet. Our investigation may provide insight into the buildup, release, and conversion of energies in large-scale solar eruptions.

astro-ph.SR↗

arXiv2023

Towards Reliable Item Sampling for Recommendation Evaluation

Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are "inconsistent" with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation. Existing methods try either mapping the sampling-based metrics to their global counterparts or more generally, learning the empirical rank distribution to estimate the top-$K$ metrics. However, despite existing efforts, there is still a lack of rigorous theoretical understanding of the proposed metric estimators, and the basic item sampling also suffers from the "blind spot" issue, i.e., estimation accuracy to recover the top-$K$ metrics when $K$ is small can still be rather substantial. In this paper, we provide an in-depth investigation into these problems and make two innovative contributions. First, we propose a new item-sampling estimator that explicitly optimizes the error with respect to the ground truth, and theoretically highlight its subtle difference against prior work. Second, we propose a new adaptive sampling method which aims to deal with the "blind spot" problem and also demonstrate the expectation-maximization (EM) algorithm can be generalized for such a setting. Our experimental results confirm our statistical analysis and the superiority of the proposed works. This study helps lay the theoretical foundation for adopting item sampling metrics for recommendation evaluation, and provides strong evidence towards making item sampling a powerful and reliable tool for recommendation evaluation.

cs.IR↗

arXiv2023

Mutual Harmony: Sequential Recommendation with Dual Contrastive Network

With the outbreak of today's streaming data, the sequential recommendation is a promising solution to achieve time-aware personalized modeling. It aims to infer the next interacted item of a given user based on the historical item sequence. Some recent works tend to improve the sequential recommendation via random masking on the historical item so as to generate self-supervised signals. But such approaches will indeed result in sparser item sequence and unreliable signals. Besides, the existing sequential recommendation models are only user-centric, i.e., based on the historical items by chronological order to predict the probability of candidate items, which ignores whether the items from a provider can be successfully recommended. Such user-centric recommendation will make it impossible for the provider to expose their new items, failing to consider the accordant interactions between user and item dimensions. In this paper, we propose a novel Dual Contrastive Network (DCN) to achieve mutual harmony between user and item provider, generating ground-truth self-supervised signals for sequential recommendation by auxiliary user-sequence from an item-centric dimension. Specifically, we propose dual representation contrastive learning to refine the representation learning by minimizing the Euclidean distance between the representations of a given user/item and historical items/users of them. Before the second contrastive learning module, we perform the next user prediction to capture the trends of items preferred by certain types of users and provide personalized exploration opportunities for item providers. Finally, we further propose dual interest contrastive learning to self-supervise the dynamic interest from the next item/user prediction and static interest of matching probability. Experiments on four benchmark datasets verify the effectiveness of our proposed method.

cs.IR↗

arXiv2023

Impression-Informed Multi-Behavior Recommender System: A Hierarchical Graph Attention Approach

While recommender systems have significantly benefited from implicit feedback, they have often missed the nuances of multi-behavior interactions between users and items. Historically, these systems either amalgamated all behaviors, such as \textit{impression} (formerly \textit{view}), \textit{add-to-cart}, and \textit{buy}, under a singular 'interaction' label, or prioritized only the target behavior, often the \textit{buy} action, discarding valuable auxiliary signals. Although recent advancements tried addressing this simplification, they primarily gravitated towards optimizing the target behavior alone, battling with data scarcity. Additionally, they tended to bypass the nuanced hierarchy intrinsic to behaviors. To bridge these gaps, we introduce the \textbf{H}ierarchical \textbf{M}ulti-behavior \textbf{G}raph Attention \textbf{N}etwork (HMGN). This pioneering framework leverages attention mechanisms to discern information from both inter and intra-behaviors while employing a multi-task Hierarchical Bayesian Personalized Ranking (HBPR) for optimization. Recognizing the need for scalability, our approach integrates a specialized multi-behavior sub-graph sampling technique. Moreover, the adaptability of HMGN allows for the seamless inclusion of knowledge metadata and time-series data. Empirical results attest to our model's prowess, registering a notable performance boost of up to 64\% in NDCG@100 metrics over conventional graph neural network methods.

cs.IR↗

arXiv2023

Contrastive Representation Learning Based on Multiple Node-centered Subgraphs

As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-centered subgraphs from the whole graph (e.g., one person in a social network has multiple social circles based on his different relationships). We study this intuition under the framework of graph contrastive learning, and propose a multiple node-centered subgraphs contrastive representation learning method to learn node representation on graphs in a self-supervised way. Specifically, we carefully design a series of node-centered regional subgraphs of the central node. Then, the mutual information between different subgraphs of the same node is maximized by contrastive loss. Experiments on various real-world datasets and different downstream tasks demonstrate that our model has achieved state-of-the-art results.

cs.AI↗

arXiv2023

Anharmonic Strong-Coupling Effects at the Origin of the Charge Density Wave in CsV$_3$Sb$_5$

The formation of charge density waves (CDW) is a long-standing open problem particularly in dimensions higher than one. Various observations in the vanadium antimonides discovered recently, such as the missing Kohn anomaly in the acoustic phonons or the latent heat at the transition $T_{\rm CDW}$ = 95 K , further underpin this notion. Here, we study the Kagome metal CsV$_3$Sb$_5$ using polarized inelastic light scattering. The electronic energy gap $2Δ$ as derived from the redistribution of the continuum is much larger than expected from mean-field theory and reaches values above 20 for $2Δ/k_{\rm B}T_{\rm CDW}$. The A$_{1g}$ phonon has a discontinuity at $T_{\rm CDW}$ and a precursor starting 20 K above $T_{\rm CDW}$. Density functional theory qualitatively reproduces the redistribution of the electronic continuum at the CDW transition and the phonon energies of the pristine and distorted structures. The linewidths of all A$_{1g}$ and E$_{2g}$ phonon lines including those emerging below $T_{\rm CDW}$ were analyzed in terms of anharmonic symmetric decay revealing strong phonon-phonon coupling. In addition, we observe two CDW amplitude modes (AMs): one in A$_{1g}$ symmetry and one in E$_{2g}$ symmetry. The temperature dependence of both modes deviates from the prediction of mean-field theory. The A$_{1g}$ AM displays an asymmetric Fano-type lineshape, suggestive of strong electron-phonon coupling. The asymmetric A$_{1g}$ AM, along with the discontinuity of the A$_{1g}$ phonon, the large phonon-phonon coupling parameters and the large gap ratio, indicate the importance of anharmonic strong phonon-phonon and electron-phonon coupling for the CDW formation in CsV$_3$Sb$_5$.

cond-mat.supr-con↗

arXiv2023

Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks

Federated learning (FL) can lead to significant communication overhead and reliance on a central server. To address these challenges, decentralized federated learning (DFL) has been proposed as a more resilient framework. DFL involves parameter exchange between devices through a wireless network. This study analyzes the performance of resource-constrained DFL using different communication schemes (digital and analog) over wireless networks to optimize communication efficiency. Specifically, we provide convergence bounds for both digital and analog transmission approaches, enabling analysis of the model performance trained on DFL. Furthermore, for digital transmission, we investigate and analyze resource allocation between computation and communication and convergence rates, obtaining its communication complexity and the minimum probability of correction communication required for convergence guarantee. For analog transmission, we discuss the impact of channel fading and noise on the model performance and the maximum errors accumulation with convergence guarantee over fading channels. Finally, we conduct numerical simulations to evaluate the performance and convergence rate of convolutional neural networks (CNNs) and Vision Transformer (ViT) trained in the DFL framework on fashion-MNIST and CIFAR-10 datasets. Our simulation results validate our analysis and discussion, revealing how to improve performance by optimizing system parameters under different communication conditions.

cs.LG↗

arXiv2023

Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation

Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem, i.e., recommend for few-interaction items. Existing GNN-based recommendation models to address the cold-start problem mainly focus on utilizing auxiliary features of users and items, leaving the user-item interactions under-utilized. However, embeddings distributions of cold and warm items are still largely different, since cold items' embeddings are learned from lower-popularity interactions, while warm items' embeddings are from higher-popularity interactions. Thus, there is a seesaw phenomenon, where the recommendation performance for the cold and warm items cannot be improved simultaneously. To this end, we proposed a Uncertainty-aware Consistency learning framework for Cold-start item recommendation (shorten as UCC) solely based on user-item interactions. Under this framework, we train the teacher model (generator) and student model (recommender) with consistency learning, to ensure the cold items with additionally generated low-uncertainty interactions can have similar distribution with the warm items. Therefore, the proposed framework improves the recommendation of cold and warm items at the same time, without hurting any one of them. Extensive experiments on benchmark datasets demonstrate that our proposed method significantly outperforms state-of-the-art methods on both warm and cold items, with an average performance improvement of 27.6%.

cs.IR↗

arXiv2023

Learning to Collaborate by Grouping: a Consensus-oriented Strategy for Multi-agent Reinforcement Learning

Multi-agent systems require effective coordination between groups and individuals to achieve common goals. However, current multi-agent reinforcement learning (MARL) methods primarily focus on improving individual policies and do not adequately address group-level policies, which leads to weak cooperation. To address this issue, we propose a novel Consensus-oriented Strategy (CoS) that emphasizes group and individual policies simultaneously. Specifically, CoS comprises two main components: (a) the vector quantized group consensus module, which extracts discrete latent embeddings that represent the stable and discriminative group consensus, and (b) the group consensus-oriented strategy, which integrates the group policy using a hypernet and the individual policies using the group consensus, thereby promoting coordination at both the group and individual levels. Through empirical experiments on cooperative navigation tasks with both discrete and continuous spaces, as well as Google research football, we demonstrate that CoS outperforms state-of-the-art MARL algorithms and achieves better collaboration, thus providing a promising solution for achieving effective coordination in multi-agent systems.

cs.MA↗

arXiv2023

Performance Analysis for Polar Codes under Successive Cancellation List Decoding with Fixed List Size

In this paper, we first indicate that the block error event of polar codes under successive cancellation list (SCL) decoding is composed of path loss (PL) error event and path selection (PS) error event, where the PL error event is that correct codeword is lost during the SCL decoding and the PS error event is that correct codeword is reserved in the decoded list but not selected as the decoded codeword. Then, we simplify the PL error event by assuming the all-zero codeword is transmitted and derive the probability lower bound via the joint probability density of the log-likelihood ratios of information bits. Meanwhile, the union bound calculated by the minimum weight distribution is used to evaluate the probability of the PS error event. With the performance analysis, we design a greedy bit-swapping (BS) algorithm to construct polar codes by gradually swapping information bit and frozen bit to reduce the performance lower bound of SCL decoding. The simulation results show that the BLER performance of SCL decoding is close to the lower bound in the medium to high signal-to-noise ratio region and we can optimize the lower bound to improve the BLER performance of SCL decoding by the BS algorithm.

cs.IT↗

arXiv2023

Differentiable Logic Machines

The integration of reasoning, learning, and decision-making is key to build more general artificial intelligence systems. As a step in this direction, we propose a novel neural-logic architecture, called differentiable logic machine (DLM), that can solve both inductive logic programming (ILP) and reinforcement learning (RL) problems, where the solution can be interpreted as a first-order logic program. Our proposition includes several innovations. Firstly, our architecture defines a restricted but expressive continuous relaxation of the space of first-order logic programs by assigning weights to predicates instead of rules, in contrast to most previous neural-logic approaches. Secondly, with this differentiable architecture, we propose several (supervised and RL) training procedures, based on gradient descent, which can recover a fully-interpretable solution (i.e., logic formula). Thirdly, to accelerate RL training, we also design a novel critic architecture that enables actor-critic algorithms. Fourthly, to solve hard problems, we propose an incremental training procedure that can learn a logic program progressively. Compared to state-of-the-art (SOTA) differentiable ILP methods, DLM successfully solves all the considered ILP problems with a higher percentage of successful seeds (up to 3.5$\times$). On RL problems, without requiring an interpretable solution, DLM outperforms other non-interpretable neural-logic RL approaches in terms of rewards (up to 3.9%). When enforcing interpretability, DLM can solve harder RL problems (e.g., Sorting, Path) Moreover, we show that deep logic programs can be learned via incremental supervised training. In addition to this excellent performance, DLM can scale well in terms of memory and computational time, especially during the testing phase where it can deal with much more constants ($>$2$\times$) than SOTA.

cs.AI↗

arXiv2023

Observational signatures of electron-driven chromospheric evaporation in a white-light flare

We investigate observational signatures of explosive chromospheric evaporation during a white-light flare (WLF) that occurred on 2022 August 27. Using the moment analysis, bisector techniques, and the Gaussian fitting method, red-shifted velocities of less than 20 km/s are detected in low-temperature spectral lines of Ha, C I and Si IV at the conjugated flare kernels, which could be regarded as downflows caused by chromospheric condensation. Blue-shifted velocities of about 30-40 km/s are found in the high-temperature line of Fe XXI, which can be interpreted as upflows driven by chromospheric evaporation. A nonthermal hard X-ray (HXR) source is co-spatial with one of the flare kernels, and the Doppler velocities are temporally correlated with the HXR fluxes. The nonthermal energy flux is estimated to be at least (1.3+-0.2)*10^10 erg/s/cm^2. The radiation enhancement at Fe I 6569.2 A and 6173 A suggests that the flare is a WLF. Moreover, the while-light emission at Fe I 6569.2 A is temporally and spatially correlated with the blue shift of Fe XXI line, suggesting that both the white-light enhancement and the chromospheric evaporation are triggered and driven by nonthermal electrons. All our observations support the scenario of an electron-driven explosive chromospheric evaporation in the WLF.

astro-ph.SR↗

arXiv2023

Event Detection from Social Media Stream: Methods, Datasets and Opportunities

Social media streams contain large and diverse amount of information, ranging from daily-life stories to the latest global and local events and news. Twitter, especially, allows a fast spread of events happening real time, and enables individuals and organizations to stay informed of the events happening now. Event detection from social media data poses different challenges from traditional text and is a research area that has attracted much attention in recent years. In this paper, we survey a wide range of event detection methods for Twitter data stream, helping readers understand the recent development in this area. We present the datasets available to the public. Furthermore, a few research opportunities

cs.SI↗

arXiv2023

Steganographic Capacity of Deep Learning Models

As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a steganographic-based attack, information could be hidden in a learning model, which might then be used to distribute malware, or for other malicious purposes. In this research, we consider the steganographic capacity of several learning models. Specifically, we train a Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Transformer model on a challenging malware classification problem. For each of the resulting models, we determine the number of low-order bits of the trained parameters that can be altered without significantly affecting the performance of the model. We find that the steganographic capacity of the learning models tested is surprisingly high, and that in each case, there is a clear threshold after which model performance rapidly degrades.

cs.CR↗

arXiv2023

Traveling kink oscillations of coronal loops launched by a solar flare

We investigate the traveling kink oscillation triggered by a solar flare on 2022 September 29. The observational data is mainly measured by the Solar Upper Transition Region Imager (SUTRI), the Atmospheric Imaging Assembly (AIA), and the Spectrometer/Telescope for Imaging X-rays (STIX). The transverse oscillations with apparent decaying in amplitudes, which are nearly perpendicular to the oscillating loop, are observed in passbands of SUTRI 465 A, AIA 171 A, and 193 A. The decaying oscillation is launched by a solar flare erupted closely to one footpoint of coronal loops, and then it propagates along several loops. Next, the traveling kink wave is evolved to a standing kink oscillation. To the best of our knowledge, this is the first report of the evolution of a traveling kink pulse to a standing kink wave along coronal loops. The standing kink oscillation along one coronal loop has a similar period of about 6.3 minutes at multiple wavelengths, and the decaying time is estimated to about 9.6-10.6 minutes. Finally, two dominant periods of 5.1 minutes and 2.0 minutes are detected in another oscillating loop, suggesting the coexistence of the fundamental and third harmonics.

astro-ph.SR↗

arXiv2023

Matrix GARCH Model: Inference and Application

Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroskedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use of a univariate GARCH specification for the trace of conditional row or column covariance matrix, which allows for the identification of conditional row and column covariance matrices. Moreover, we introduce a quasi maximum likelihood estimator (QMLE) for model estimation and develop a portmanteau test for model diagnostic checking. Simulation studies are conducted to assess the finite-sample performance of the QMLE and portmanteau test. To handle large dimensional matrix time series, we also propose a matrix factor GARCH model. Finally, we demonstrate the superiority of the matrix GARCH and matrix factor GARCH models over existing multivariate GARCH-type models in volatility forecasting and portfolio allocations using three applications on credit default swap prices, global stock sector indices, and future prices.

stat.ME↗

arXiv2023

Uniform estimates for 2D quasilinear wave

We consider two-dimensional quasilinear wave equations with standard null-type quadratic nonlinearities. In 2001 Alinhac proved that such systems possess global in time solutions for compactly supported initial data with sufficiently small Sobolev norm. The highest norm of the constructed solution grows polynomially in time. In this work we develop a new strategy and prove uniform boundedness of the highest order norm of the solution for all time.

math.AP↗

arXiv2023

Exploiting Constructive Interference for Backscatter Communication Systems

Backscatter communication (BackCom), one of the core technologies to realize zero-power communication, is expected to be a pivotal paradigm for the next generation of the Internet of Things (IoT). However, the "strong" direct link (DL) interference (DLI) is traditionally assumed to be harmful, and generally drowns out the "weak" backscattered signals accordingly, thus deteriorating the performance of BackCom. In contrast to the previous efforts to eliminate the DLI, in this paper, we exploit the constructive interference (CI), in which the DLI contributes to the backscattered signal. To be specific, our objective is to maximize the received signal power by jointly optimizing the receive beamforming vectors and tag selection factors, which is, however, non-convex and difficult to solve due to constraints on the Kullback-Leibler (KL) divergence. In order to solve this problem, we first decompose the original problem, and then propose two algorithms to solve the sub-problem with beamforming design via a change of variables and semi-definite programming (SDP) and a greedy algorithm to solve the sub-problem with tag selection. In order to gain insight into the CI, we consider a special case with the single-antenna reader to reveal the channel angle between the backscattering link (BL) and the DL, in which the DLI will become constructive. Simulation results show that significant performance gain can always be achieved in the proposed algorithms compared with the traditional algorithms without the DL in terms of the strength of the received signal. The derived constructive channel angle for the BackCom system with the single-antenna reader is also confirmed by simulation results.

cs.IT↗