arXiv subjects

Jie Lu

Publications and source records attributed to Jie Lu.

At least 127 records · Page 7Linked to original sources

arXiv2023

Slonczewski-spin-current driven dynamics of 180$^{\circ}$ domain walls in spin valves with interfacial Dzyaloshinskii-Moriya interaction

Steady-flow dynamics of ferromagnetic 180$^{\circ}$ domain walls (180DWs) in long and narrow spin valves (LNSVs) with interfacial Dzyaloshinskii-Moriya interaction (IDMI) under spin currents with Slonczewski $g-$factor are examined. Depending on the magnetization orientation of polarizers (pinned layers of LNSVs), dynamics of 180DWs in free layers of LNSVs are subtly manipulated: (i) For parallel polarizers, stronger spin polarization leads to higher Walker limit thus ensures the longevity of faster steady flows. Meantime, IDMI induces both the stable-region flapping and its width enlargement. (ii) For perpendicular polarizers, a wandering of 180DWs between bi- and tri-stability persists with the criticality adjusted by the IDMI. (iii) For planar-transverse polarizers, IDMI makes the stable region of steady flows completely asymmetric and further imparts a high saturation wall velocity under large current density. Under the last two polarizers, the ultrahigh differential mobility of 180DWs survives. The combination of Slonczewski spin current and IDMI provides rich possibilities of fine controlling on 180DW dynamics, hence opens avenues for magnetic nanodevices with rich functionality and high robustness.

cond-mat.mes-hall↗

arXiv2023

Systematic analysis of doubly charmed baryons $Ξ_{cc}$ and $Ω_{cc}$

In this work, we perform a systematic study of the mass spectra, the root mean square(r.m.s.) radii and the radial density distributions of the doubly charmed baryons $Ξ_{cc}$ and $Ω_{cc}$. The calculations are carried out in the frame work of Godfrey-Isgur (GI) relativized quark model, where the baryon is regarded as a real three-body system of quarks. Our results show that the excited energy of doubly charmed baryon with $ρ$-mode is lower than those of the $λ$-mode and $λ$-$ρ$ mixing mode, which indicates that the lowest state is dominated by the $ρ$-mode. According to this conclusion, we systematically investigate the mass spectra, the r.m.s. radii of the ground and excited states($1S\sim4S$, $1P\sim4P$, $1D\sim4D$, $1F\sim4F$ and $1G\sim4G$) with $ρ$-mode. Using the wave functions obtained from quark model, we also study the radial density distributions. Finally, with the predicated mass spectra, the Regge trajectories of $Ξ_{cc}$ and $Ω_{cc}$ in the ($J$,$M^{2}$) plane are constructed, and the slopes, intercepts are determined by linear fitting. It is found that model predicted masses fit nicely to the constructed Regge trajectories.

hep-ph↗

arXiv2023

Systematic analysis of single heavy baryons $Λ_{Q}$, $Σ_{Q}$ and $Ω_{Q}$

Motivated by great progresses in experiments in searching for the heavy baryons, we systematically analyze the mass spectra and root mean square radius of single heavy baryons $Λ_{Q}$, $Σ_{Q}$ and $Ω_{Q}$. The calculations of the mass spectra are carried out in the frame work of Godfrey-Isgur (GI) relativized quark model, where the baryon is regarded as a three-body system of quarks. Our results show that the mass of single heavy baryon with $λ$-mode is lower than those of the $ρ$-mode and $λ$-$ρ$ mixing mode, which indicates that the lowest state is dominated by the $λ$-mode. Basing on this research, we systematically calculate the mass spectra and the root mean square radius of the baryons with $λ$ excited mode. With these predicated mass spectra, the Regge trajectories in the ($J$,$M^{2}$) plane are constructed, and the slopes, intercepts of the Regge trajectories are obtained by linear fitting. It is found that all available experimental data are well reproduced by model predictions and fit nicely to the constructed Regge trajectories.

hep-ph↗

arXiv2023

The $S$- and $P$-wave fully charmed tetraquark states and their radial excitations

Inspired by recent progresses in observations of the fully charmed tetraquark states by LHCb, CMS, and ATLAS Collaborations, we perform a systematic study of the ground states and the first radial excitations of the $S$- and $P$-wave $\mathrm{cc}\bar{\mathrm{c}}\bar{\mathrm{c}}$ system. Their mass spectra, root mean square(r.m.s.) radii and radial density distributions are studied with the relativized quark model. The calculations show that there is no stable bound states for the full-charmed tetraquark states, and the r.m.s. radii of these tetraquark states are smaller than 1 fm. Our results support assigning X(6600) structure, $M_{X(6600)}=6552\pm10\pm12$ MeV, as one of the $0^{++}$(1$S$) and $2^{++}$(1$S$) states or their mixtures. Another structure also named as X(6600) by CMS Collaboration, $M_{X(6600)}=6.62\pm0.03^{+0.02}_{-0.01}$ GeV, may arise from the lowest 1$P$ states with $J^{PC}$=$0^{-+}$, $1^{-+}$, and $2^{-+}$. The possible assignments for X(6900) include the $0^{++}$(2$S$), $2^{++}$(2$S$) states, and the highest 1$P$ state with $J^{PC}=0^{-+}$. As for X(7200), it can be interpreted as one of the highest 2$P$ states with $J^{PC}=0^{-+}$, $1^{-+}$, and $2^{-+}$, and the 3$S$ states can not be completely excluded from the candidates.

hep-ph↗

arXiv2023

Systematic analysis of strange single heavy baryons $Ξ_{c}$ and $Ξ_{b}$

Motivated by the experimental progress in the study of heavy baryons, we investigate the mass spectra of strange single heavy baryons in the $λ$-mode, where the relativistic quark model and the infinitesimally shifted Gaussian basis function method are employed. It is shown that the experimental data can be well reproduced by the predicted masses. The root mean square radii and radial probability density distributions of the wave functions are analyzed in detail. Meanwhile, the mass spectra allow us to successfully construct the Regge trajectories in the $(J,M^{2})$ plane. We also preliminarily assign quantum numbers to the recently observed baryons, including $Ξ_{c}(3055)$, $Ξ_{c}(3080)$, $Ξ_{c}(2930)$, $Ξ_{c}(2923)$, $Ξ_{c}(2939)$, $Ξ_{c}(2965)$, $Ξ_{c}(2970)$, $Ξ_{c}(3123)$, $Ξ_{b}(6100)$, $Ξ_{b}(6227)$, $Ξ_{b}(6327)$ and $Ξ_{b}(6333)$. At last, the spectral structure of the strange single heavy baryons is shown. Accordingly, we predict several new baryons that might be observed in forthcoming experiments.

hep-ph↗

arXiv2023

Is Out-of-Distribution Detection Learnable?

Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studied a more realistic setting: out-of-distribution (OOD) detection, where test data may come from classes that are unknown during training (i.e., OOD data). Due to the unavailability and diversity of OOD data, good generalization ability is crucial for effective OOD detection algorithms. To study the generalization of OOD detection, in this paper, we investigate the probably approximately correct (PAC) learning theory of OOD detection, which is proposed by researchers as an open problem. First, we find a necessary condition for the learnability of OOD detection. Then, using this condition, we prove several impossibility theorems for the learnability of OOD detection under some scenarios. Although the impossibility theorems are frustrating, we find that some conditions of these impossibility theorems may not hold in some practical scenarios. Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios. Lastly, we also offer theoretical supports for several representative OOD detection works based on our OOD theory.

cs.LG↗

arXiv2022

Ground states and magnonics in orthogonally-coupled symmetric all-antiferromagnetic junctions

In this work, the rich ground-state structure of orthogonally-coupled symmetric all-antiferromagnetic junctions with easy-plane anisotropy is reported. Spin reorientation process rather than the traditional spin flop (SF) occurs, resulting in a novel phase in which Néel vectors preserve the mirror-reflection symmetry (termed as ``MRS phase"). The phase transitions between SF and MRS phases can be either the first- or second-order. After disturbed by external stimuli, magnons with different parities emerge. For in-plane dc fields, no couplings between magnons occur. When dc fields become oblique, coherent couplings between magnons with opposite parity emerge, leading to anticrossings in resonance frequencies. However, self-hybridization among magnons with the same parity never happens. More interestingly, spin waves based on MRS phase are linearly polarized and their polarization directions can be fine controlled.

cond-mat.mes-hall↗

arXiv2022

A Stochastic Second-Order Proximal Method for Distributed Optimization

In this paper, we propose a distributed stochastic second-order proximal method that enables agents in a network to cooperatively minimize the sum of their local loss functions without any centralized coordination. The proposed algorithm, referred to as St-SoPro, incorporates a decentralized second-order approximation into an augmented Lagrangian function, and then randomly samples the local gradients and Hessian matrices of the agents, so that it is computationally and memory-wise efficient, particularly for large-scale optimization problems. We show that for globally restricted strongly convex problems, the expected optimality error of St-SoPro asymptotically drops below an explicit error bound at a linear rate, and the error bound can be arbitrarily small with proper parameter settings. Simulations over real machine learning datasets demonstrate that St-SoPro outperforms several state-of-the-art distributed stochastic first-order methods in terms of convergence speed as well as computation and communication costs.

math.OC↗

arXiv2022

Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making

As a powerful Bayesian non-parameterized algorithm, the Gaussian process (GP) has performed a significant role in Bayesian optimization and signal processing. GPs have also advanced online decision-making systems because their posterior distribution has a closed-form solution. However, its training and inference process requires all historic data to be stored and the GP model to be trained from scratch. For those reasons, several online GP algorithms, such as O-SGPR and O-SVGP, have been specifically designed for streaming settings. In this paper, we present a new theoretical framework for online GPs based on the online probably approximately correct (PAC) Bayes theory. The framework offers both a guarantee of generalized performance and good accuracy. Instead of minimizing the marginal likelihood, our algorithm optimizes both the empirical risk function and a regularization item, which is in proportion to the divergence between the prior distribution and posterior distribution of parameters. In addition to its theoretical appeal, the algorithm performs well empirically on several regression datasets. Compared to other online GP algorithms, ours yields a generalization guarantee and very competitive accuracy.

cs.LG↗

arXiv2022

Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples

Graph Convolutional Neural Networks (GCNs) has been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its neighbours (also known as positive samples). However, beyond these neighbouring nodes, graphs have a large, dark, all-but forgotten world in which we find the non-neighbouring nodes (negative samples). In this paper, we show that this great dark world holds a substantial amount of information that might be useful for representation learning. Most specifically, it can provide negative information about the node representations. Our overall idea is to select appropriate negative samples for each node and incorporate the negative information contained in these samples into the representation updates. Moreover, we show that the process of selecting the negative samples is not trivial. Our theme therefore begins by describing the criteria for a good negative sample, followed by a determinantal point process algorithm for efficiently obtaining such samples. A GCN, boosted by diverse negative samples, then jointly considers the positive and negative information when passing messages. Experimental evaluations show that this idea not only improves the overall performance of standard representation learning but also significantly alleviates over-smoothing problems.

cs.LG↗

arXiv2022

Fast Evolution of Single Qubit Gate in Non-Adiabatic Geometric Quantum Computing

We implemented arbitrary single qubit gates of geometric quantum computing for a three-level system in a single-shot manner. The evolution time of the gate has been minimized by considering the shortest trajectory of the state on the Bloch sphere. The duration of gates grows from zero with the rotation angle $γ$, and the tested T gate time can be reduced to $\sim$40\% of those in the traditional orange-sliced-shaped path non-adiabatic holonomic quantum computing (NHQC) scheme by the parametrization of Rabi frequency. We also demonstrated that certain pulses are robust against static detuning errors and Rabi errors. The time-dependent detuning and Rabi frequency are found to be proportional to each other by a constant which is determined by the geometric phase. In this way, some previous NHQC schemes can be treated as special cases in our generalized model.

quant-ph↗

arXiv2022

Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms

The theoretical analysis of multi-class classification has proved that the existing multi-class classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multi-class classification has not been solved: how to improve the classification accuracy of multi-class classification problems when only imprecise observations are available. Hence, in this paper, we propose a novel framework to address a new realistic problem called multi-class classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations. Firstly, we give the theoretical analysis of the MCIMO problem based on fuzzy Rademacher complexity. Then, two practical algorithms based on support vector machine and neural networks are constructed to solve the proposed new problem. Experiments on both synthetic and real-world datasets verify the rationality of our theoretical analysis and the efficacy of the proposed algorithms.

cs.LG↗

arXiv2022

Stepping beyond your comfort zone: Diffusion-based network analytics for knowledge trajectory recommendation

Interest in tracing the research interests of scientific researchers is rising, and particularly that of predicting a researcher's knowledge trajectories beyond their current foci into potential inter-/cross-/multi-disciplinary interactions. Hence, in this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to reflect real-world academic activity, such as knowledge sharing between co-authors or diffusing between similar research topics. This strategy differentiates the interactions occurring between homogeneous and heterogeneous nodes and weights the strengths of these interactions. Two sets of experiments - one with a local dataset and another with a global dataset - demonstrate that the proposed method is prior to selected baselines. In addition, to further examine the reliability of our method, we conducted a case study on recommending knowledge trajectories of selected information scientists and their research groups. The results demonstrate the empirical insights our method yields for individual researchers, communities, and research institutions in the information science discipline.

cs.SI↗

arXiv2022

Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data

Modern kernel-based two-sample tests have shown great success in distinguishing complex, high-dimensional distributions with appropriate learned kernels. Previous work has demonstrated that this kernel learning procedure succeeds, assuming a considerable number of observed samples from each distribution. In realistic scenarios with very limited numbers of data samples, however, it can be challenging to identify a kernel powerful enough to distinguish complex distributions. We address this issue by introducing the problem of meta two-sample testing (M2ST), which aims to exploit (abundant) auxiliary data on related tasks to find an algorithm that can quickly identify a powerful test on new target tasks. We propose two specific algorithms for this task: a generic scheme which improves over baselines and a more tailored approach which performs even better. We provide both theoretical justification and empirical evidence that our proposed meta-testing schemes out-perform learning kernel-based tests directly from scarce observations, and identify when such schemes will be successful.

stat.ML↗

arXiv2021

Engineering of the qubit initialization in an imperfect physical system

We proposed a method to engineer the light matter interaction while initializing a qubit in presence of physical constraints utilizing the inverse engineering. Combining the multiple degrees of freedom in the pulse parameters with the perturbation theory, we developed pulses to initialize the qubit within a tightly packed frequency interval to an arbitrary superposition state with high fidelity. Importantly, the initialization induces low off-resonant excitations to the neighboring qubits, and it is robust against the spatial inhomogeneity in the laser intensity. We applied the method to the ensemble rare-earth ions system, and simulations show that the initialization is more robust against the variations in laser intensity than the previous pulses, and reduces the time that ions spend in the intermediate excited state by a factor of 17. The method is applicable to any systems addressed in frequency such as NV centers, superconducting qubits, quantum dots, and molecular qubit systems.

quant-ph↗

arXiv2021

Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning

Transfer learning where the behavior of extracting transferable knowledge from the source domain(s) and reusing this knowledge to target domain has become a research area of great interest in the field of artificial intelligence. Probabilistic graphical models (PGMs) have been recognized as a powerful tool for modeling complex systems with many advantages, e.g., the ability to handle uncertainty and possessing good interpretability. Considering the success of these two aforementioned research areas, it seems natural to apply PGMs to transfer learning. However, although there are already some excellent PGMs specific to transfer learning in the literature, the potential of PGMs for this problem is still grossly underestimated. This paper aims to boost the development of PGMs for transfer learning by 1) examining the pilot studies on PGMs specific to transfer learning, i.e., analyzing and summarizing the existing mechanisms particularly designed for knowledge transfer; 2) discussing examples of real-world transfer problems where existing PGMs have been successfully applied; and 3) exploring several potential research directions on transfer learning using PGM.

cs.LG↗

arXiv2021

Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Time

Causal effect estimation for dynamic treatment regimes (DTRs) contributes to sequential decision making. However, censoring and time-dependent confounding under DTRs are challenging as the amount of observational data declines over time due to a reducing sample size but the feature dimension increases over time. Long-term follow-up compounds these challenges. Another challenge is the highly complex relationships between confounders, treatments, and outcomes, which causes the traditional and commonly used linear methods to fail. We combine outcome regression models with treatment models for high dimensional features using uncensored subjects that are small in sample size and we fit deep Bayesian models for outcome regression models to reveal the complex relationships between confounders, treatments, and outcomes. Also, the developed deep Bayesian models can model uncertainty and output the prediction variance which is essential for the safety-aware applications, such as self-driving cars and medical treatment design. The experimental results on medical simulations of HIV treatment show the ability of the proposed method to obtain stable and accurate dynamic causal effect estimation from observational data, especially with long-term follow-up. Our technique provides practical guidance for sequential decision making, and policy-making.

stat.ML↗

arXiv2021

A Unifying Approximate Method of Multipliers for Distributed Composite Optimization

This paper investigates solving convex composite optimization on an undirected network, where each node, privately endowed with a smooth component function and a nonsmooth one, is required to minimize the sum of all the component functions throughout the network. To address such a problem, a general Approximate Method of Multipliers (AMM) is developed, which attempts to approximate the Method of Multipliers by virtue of a surrogate function with numerous options. We then design the possibly nonseparable, time-varying surrogate function in various ways, leading to different distributed realizations of AMM. We demonstrate that AMM generalizes more than ten state-of-the-art distributed optimization algorithms, and certain specific designs of its surrogate function result in a variety of new algorithms to the literature. Furthermore, we show that AMM is able to achieve an $O(1/k)$ rate of convergence to optimality, and the convergence rate becomes linear when the problem is locally restricted strongly convex and smooth. Such convergence rates provide new or stronger convergence results to many prior methods that can be viewed as specializations of AMM.

math.OC↗