arXiv subjects

Jia Chen

Publications and source records attributed to Jia Chen.

At least 91 records · Page 5Linked to original sources

arXiv2023

An Adam-enhanced Particle Swarm Optimizer for Latent Factor Analysis

Digging out the latent information from large-scale incomplete matrices is a key issue with challenges. The Latent Factor Analysis (LFA) model has been investigated in depth to an alyze the latent information. Recently, Swarm Intelligence-related LFA models have been proposed and adopted widely to improve the optimization process of LFA with high efficiency, i.e., the Particle Swarm Optimization (PSO)-LFA model. However, the hyper-parameters of the PSO-LFA model have to tune manually, which is inconvenient for widely adoption and limits the learning rate as a fixed value. To address this issue, we propose an Adam-enhanced Hierarchical PSO-LFA model, which refines the latent factors with a sequential Adam-adjusting hyper-parameters PSO algorithm. First, we design the Adam incremental vector for a particle and construct the Adam-enhanced evolution process for particles. Second, we refine all the latent factors of the target matrix sequentially with our proposed Adam-enhanced PSO's process. The experimental results on four real datasets demonstrate that our proposed model achieves higher prediction accuracy with its peers.

cs.LG↗

arXiv2023

Video Captioning with Guidance of Multimodal Latent Topics

The topic diversity of open-domain videos leads to various vocabularies and linguistic expressions in describing video contents, and therefore, makes the video captioning task even more challenging. In this paper, we propose an unified caption framework, M&M TGM, which mines multimodal topics in unsupervised fashion from data and guides the caption decoder with these topics. Compared to pre-defined topics, the mined multimodal topics are more semantically and visually coherent and can reflect the topic distribution of videos better. We formulate the topic-aware caption generation as a multi-task learning problem, in which we add a parallel task, topic prediction, in addition to the caption task. For the topic prediction task, we use the mined topics as the teacher to train a student topic prediction model, which learns to predict the latent topics from multimodal contents of videos. The topic prediction provides intermediate supervision to the learning process. As for the caption task, we propose a novel topic-aware decoder to generate more accurate and detailed video descriptions with the guidance from latent topics. The entire learning procedure is end-to-end and it optimizes both tasks simultaneously. The results from extensive experiments conducted on the MSR-VTT and Youtube2Text datasets demonstrate the effectiveness of our proposed model. M&M TGM not only outperforms prior state-of-the-art methods on multiple evaluation metrics and on both benchmark datasets, but also achieves better generalization ability.

cs.CV↗

arXiv2023

Estimating Time-Varying Networks for High-Dimensional Time Series

We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages. Under the sparse structural assumption, we propose a penalised local linear method with time-varying weighted group LASSO to jointly estimate the transition matrices and identify their significant entries, and a time-varying CLIME method to estimate the precision matrices. The estimated transition and precision matrices are then used to determine the time-varying network structures. Under some mild conditions, we derive the theoretical properties of the proposed estimates including the consistency and oracle properties. In addition, we extend the methodology and theory to cover highly-correlated large-scale time series, for which the sparsity assumption becomes invalid and we allow for common factors before estimating the factor-adjusted time-varying networks. We provide extensive simulation studies and an empirical application to a large U.S. macroeconomic dataset to illustrate the finite-sample performance of our methods.

stat.ME↗

arXiv2022

Demonstration of electron-nuclear decoupling at a spin clock transition

The ability to design quantum systems that decouple from environmental noise sources is highly desirable for development of quantum technologies with optimal coherence. The chemical tunability of electronic states in magnetic molecules combined with advanced electron spin resonance techniques provides excellent opportunities to address this problem. Indeed, so-called clock transitions (CTs) have been shown to protect molecular spin qubits from magnetic noise, giving rise to significantly enhanced coherence. Here we conduct a spectroscopic and computational investigation of this physics, focusing on the role of the nuclear bath. Away from the CT, linear coupling to the nuclear degrees of freedom causes a modulation and decay of electronic coherence, as quantified via electron spin echo signals generated experimentally and $\textit{in silico}$. Meanwhile, the effective hyperfine interaction vanishes at the CT, resulting in electron-nuclear decoupling and an absence of quantum information leakage to the nuclear bath, providing opportunities to characterize other decoherence sources.

quant-ph↗

arXiv2022

Spade: A Real-Time Fraud Detection Framework on Evolving Graphs (Complete Version)

Real-time fraud detection is a challenge for most financial and electronic commercial platforms. To identify fraudulent communities, Grab, one of the largest technology companies in Southeast Asia, forms a graph from a set of transactions and detects dense subgraphs arising from abnormally large numbers of connections among fraudsters. Existing dense subgraph detection approaches focus on static graphs without considering the fact that transaction graphs are highly dynamic. Moreover, detecting dense subgraphs from scratch with graph updates is time consuming and cannot meet the real-time requirement in industry. To address this problem, we introduce an incremental real-time fraud detection framework called Spade. Spade can detect fraudulent communities in hundreds of microseconds on million-scale graphs by incrementally maintaining dense subgraphs. Furthermore, Spade supports batch updates and edge grouping to reduce response latency. Lastly, Spade provides simple but expressive APIs for the design of evolving fraud detection semantics. Developers plug their customized suspiciousness functions into Spade which incrementalizes their semantics without recasting their algorithms. Extensive experiments show that Spade detects fraudulent communities in real time on million-scale graphs. Peeling algorithms incrementalized by Spade are up to a million times faster than the static version.

cs.DB↗

arXiv2022

Tunable Dirac Semimetals with Higher-order Fermi Arcs in Kagome Lattices Pd$_3$Pb$_2$X$_2$ (X = S, Se)

Bulk-boundary correspondence has achieved a great success in the identification of topological states. However, this elegant strategy doesn't apply to the Dirac semimetals (DSMs). Here, we propose that kagome lattices Pd$_3$Pb$_2$X$_2$ (X = S, Se) are unique type-I DSMs without surface Fermi arc states, which are different from the previous well-known DSMs, such as Na$_3$Bi and Cd$_3$As$_2$. Pd$_3$Pb$_2$X$_2$ are characterized by nontrivial topological invariant Z$_3$, guaranteeing a higher-order bulk-hinge correspondence and the existence of higher-order Fermi arcs, as well as fractional corner charges on the hinges. The type-I DSMs are located at the phase boundaries of several topological phases, including type-II DSMs and three-dimensional weak topological insulators. The phase transitions can be easily manipulated by external strain. Our results provide feasible platforms for the study of these unique DSMs and the related phase transitions.

cond-mat.mtrl-sci↗

arXiv2022

An Adam-adjusting-antennae BAS Algorithm for Refining Latent Factors

Extracting the latent information in high-dimensional and incomplete matrices is an important and challenging issue. The Latent Factor Analysis (LFA) model can well handle the high-dimensional matrices analysis. Recently, Particle Swarm Optimization (PSO)-incorporated LFA models have been proposed to tune the hyper-parameters adaptively with high efficiency. However, the incorporation of PSO causes the premature problem. To address this issue, we propose a sequential Adam-adjusting-antennae BAS (A2BAS) optimization algorithm, which refines the latent factors obtained by the PSO-incorporated LFA model. The A2BAS algorithm consists of two sub-algorithms. First, we design an improved BAS algorithm which adjusts beetles' antennae and step-size with Adam; Second, we implement the improved BAS algorithm to optimize all the row and column latent factors sequentially. With experimental results on two real high-dimensional matrices, we demonstrate that our algorithm can effectively solve the premature convergence issue.

cs.LG↗

arXiv2022

Pre-training Methods in Information Retrieval

The core of information retrieval (IR) is to identify relevant information from large-scale resources and return it as a ranked list to respond to the user's information need. In recent years, the resurgence of deep learning has greatly advanced this field and leads to a hot topic named NeuIR (i.e., neural information retrieval), especially the paradigm of pre-training methods (PTMs). Owing to sophisticated pre-training objectives and huge model size, pre-trained models can learn universal language representations from massive textual data, which are beneficial to the ranking task of IR. Recently, a large number of works, which are dedicated to the application of PTMs in IR, have been introduced to promote the retrieval performance. Considering the rapid progress of this direction, this survey aims to provide a systematic review of pre-training methods in IR. To be specific, we present an overview of PTMs applied in different components of an IR system, including the retrieval component, the re-ranking component, and other components. In addition, we also introduce PTMs specifically designed for IR, and summarize available datasets as well as benchmark leaderboards. Moreover, we discuss some open challenges and highlight several promising directions, with the hope of inspiring and facilitating more works on these topics for future research.

cs.IR↗

arXiv2022

A Differential Evolution-Enhanced Latent Factor Analysis Model for High-dimensional and Sparse Data

High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications. A Position-transitional Latent Factor Analysis (PLFA) model can accurately and efficiently represent an HiDS matrix. However, its involved latent factors are optimized by stochastic gradient descent with the specific gradient direction step-by-step, which may cause a suboptimal solution. To address this issue, this paper proposes a Sequential-Group-Differential- Evolution (SGDE) algorithm to refine the latent factors optimized by a PLFA model, thereby achieving a highly-accurate SGDE-PLFA model to HiDS matrices. As demonstrated by the experiments on four HiDS matrices, a SGDE-PLFA model outperforms the state-of-the-art models.

cs.LG↗

arXiv2021

Dzyaloshinskii-Moriya Interaction-Induced Magnetoelectric Coupling in a tetrahedral Molecular Spin-Frustrated System

We have investigated magnetoelectric coupling in the single-molecule magnet $\mathrm{Mn}_{4}\mathrm{Te}_{4}(\mathrm{P}\mathrm{Et}_{3})_{4}$ with tetrahedral spin frustration. Our density functional studies found that an electric dipole moment can emerge with various non-collinear spin orderings. The forms of spin-dependent dipole are determined and consistent with that in non-centrosymmetric magnets driven by the Dzyaloshinskii-Moriya interaction. Writing a parameterized spin Hamiltonian, after solving for eigenvalues and eigenstates we quantified the magnetoelectric coupling by calculating the thermal average of the electric and magnetic susceptibilities, which can be influenced by external magnetic and electric fields, respectively. The quadratic relations are expected to be observable in experiments.

cond-mat.mtrl-sci↗

arXiv2021

Deep Fraud Detection on Non-attributed Graph

Fraud detection problems are usually formulated as a machine learning problem on a graph. Recently, Graph Neural Networks (GNNs) have shown solid performance on fraud detection. The successes of most previous methods heavily rely on rich node features and high-fidelity labels. However, labeled data is scarce in large-scale industrial problems, especially for fraud detection where new patterns emerge from time to time. Meanwhile, node features are also limited due to privacy and other constraints. In this paper, two improvements are proposed: 1) We design a graph transformation method capturing the structural information to facilitate GNNs on non-attributed fraud graphs. 2) We propose a novel graph pre-training strategy to leverage more unlabeled data via contrastive learning. Experiments on a large-scale industrial dataset demonstrate the effectiveness of the proposed framework for fraud detection.

cs.LG↗

arXiv2021

Low-Depth Unitary Coupled Cluster Theory for Quantum Computation

The unitary coupled cluster (UCC) approximation is one of the more promising wave-function ansätze for electronic structure calculations on quantum computers via the variational quantum eigensolver algorithm. However, for large systems with many orbitals, the required number of UCC factors still leads to very deep quantum circuits, which can be challenging to implement. Based on the observation that most UCC amplitudes are small for weakly correlated molecules, we devise an algorithm that employs a Taylor expansion in the small amplitudes, trading off circuit depth for extra measurements. Strong correlations can be taken into account by performing the expansion about a small set of UCC factors, which are treated exactly. Near equilibrium, the Taylor series expansion often works well without the need to include any exact factors; as the molecule is stretched and correlations increase, we find only a small number of factors need to be treated exactly.

quant-ph↗

arXiv2021

Flexibility of the factorized form of the unitary coupled cluster ansatz

The factorized form of the unitary coupled cluster ansatz is a popular state preparation ansatz for electronic structure calculations of molecules on quantum computers. It often is viewed as an approximation (based on the Trotter product formula) for the conventional unitary coupled cluster operator. In this work, we show that the factorized form is quite flexible, allowing one to range from conventional configuration interaction, to conventional unitary coupled cluster, to efficient approximations that lie in between these two. The variational minimization of the energy often allows simpler factorized unitary coupled cluster approximations to achieve high accuracy, even if they do not accurately approximate the Trotter product formula. This is similar to how quantum approximate optimization algorithms can achieve high accuracy with a small number of levels.

physics.chem-ph↗

arXiv2021

Nonequilibrium kinetic freeze-out properties in relativistic heavy ion collisions from energies employed at the RHIC beam energy scan to those available at the LHC

In this paper, we investigate the kinetic freeze-out properties in relativistic heavy ion collisions at different collision energies. We present a study of standard Boltzmann-Gibbs Blast-Wave (BGBW) fits and Tsallis Blast-Wave (TBW) fits performed on the transverse momentum spectra of identified hadrons produced in Au + Au collisions at collision energies of $\sqrt{s_{\rm{NN}}}=$ 7.7 - 200 GeV at the Relativistic Heavy Ion Collider (RHIC), and in Pb + Pb collisions at collision energies of $\sqrt{s_{\rm{NN}}}=$ 2.76 and 5.02 TeV at the Large Hadron Collider (LHC). The behavior of strange and multi-strange particles is also investigated. We found that the TBW model describes data better than the BGBW one overall, and the contrast is more prominent as the collision energy increases as the degree of non-equilibrium of the produced system is found to increase. From TBW fits, the kinetic freeze-out temperature at the same centrality shows a weak dependence of collision energy between 7.7 and 39 GeV, while it decreases as collision energy continues to increase up to 5.02 TeV. The radial flow is found to be consistent with zero in peripheral collisions at RHIC energies but sizable at LHC energies and central collisions at all RHIC energies. We also observed that the strange hadrons, with higher temperature and similar radial flow, approach equilibrium more quickly from peripheral to central collisions than light hadrons. The dependence of temperature and flow velocity on non-equilibrium parameter ($q-1$) is characterized by two second-order polynomials. Both $a$ and $dξ$ from the polynomials fit, related to the influence of the system bulk viscosity, increase toward lower RHIC energies.

nucl-th↗

arXiv2021

Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation

Self-attention mechanism recently achieves impressive advancement in Natural Language Processing (NLP) and Image Processing domains. And its permutation invariance property makes it ideally suitable for point cloud processing. Inspired by this remarkable success, we propose an end-to-end architecture, dubbed Cross-Level Cross-Scale Cross-Attention Network (CLCSCANet), for point cloud representation learning. First, a point-wise feature pyramid module is introduced to hierarchically extract features from different scales or resolutions. Then a cross-level cross-attention is designed to model long-range inter-level and intra-level dependencies. Finally, we develop a cross-scale cross-attention module to capture interactions between-and-within scales for representation enhancement. Compared with state-of-the-art approaches, our network can obtain competitive performance on challenging 3D object classification, point cloud segmentation tasks via comprehensive experimental evaluation.

cs.CV↗

arXiv2021

Dynamic Network Embedding Survey

Since many real world networks are evolving over time, such as social networks and user-item networks, there are increasing research efforts on dynamic network embedding in recent years. They learn node representations from a sequence of evolving graphs but not only the latest network, for preserving both structural and temporal information from the dynamic networks. Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper. Our survey inspects the data model, representation learning technique, evaluation and application of current related works and derives common patterns from them. Specifically, we present two basic data models, namely, discrete model and continuous model for dynamic networks. Correspondingly, we summarize two major categories of dynamic network embedding techniques, namely, structural-first and temporal-first that are adopted by most related works. Then we build a taxonomy that refines the category hierarchy by typical learning models. The popular experimental data sets and applications are also summarized. Lastly, we have a discussion of several distinct research topics in dynamic network embedding.

cs.SI↗

arXiv2020

Towards Playing Full MOBA Games with Deep Reinforcement Learning

MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc. Developing AI for playing MOBA games has raised much attention accordingly. However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i.e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes. As a result, full MOBA games without restrictions are far from being mastered by any existing AI system. In this paper, we propose a MOBA AI learning paradigm that methodologically enables playing full MOBA games with deep reinforcement learning. Specifically, we develop a combination of novel and existing learning techniques, including curriculum self-play learning, policy distillation, off-policy adaption, multi-head value estimation, and Monte-Carlo tree-search, in training and playing a large pool of heroes, meanwhile addressing the scalability issue skillfully. Tested on Honor of Kings, a popular MOBA game, we show how to build superhuman AI agents that can defeat top esports players. The superiority of our AI is demonstrated by the first large-scale performance test of MOBA AI agent in the literature.

cs.AI↗

arXiv2020

Long-Range Magnetic Exchange Pathways in Complex Clusters from First-Principles

This work builds a bridge between density functional theory (DFT) and model interpretations of Anderson's superexchange theory by constructing a $f$-$d$-$p$ model with DFT Wannier functions to enable a direct quantum many-body solution within an embedding approach. When applied to long-range magnetic interactions in a Mn-Ce magnetic molecule, we are able to obtain numerical insights about double exchange and superexchange interactions. Direct metal-metal charge transfer processes are generally weak in this molecule, which leads to small contributions from double exchange interactions. For long-range interactions, Mn-Ce charge transfer is not significant compared to Ce-O charge transfer. The unusual superexchange between Mn atoms with different valence states is identified as the dominant mechanism. This procedure opens a path for quantitative understanding of different exchange interactions in complex magnetic systems, including molecular magnets, transition metal organic frameworks, and other solid materials.

cond-mat.mtrl-sci↗