arXiv subjects

Wei Guo

Publications and source records attributed to Wei Guo.

At least 235 records · Page 13Linked to original sources

arXiv2022

Velocity circulation intermittency in finite-temperature turbulent superfluid helium

We study intermittency of circulation moments in turbulent superfluid helium by using experimental grid turbulence and numerical simulations of the Hall-Vinen-Bekarevich-Khalatnikov model. More precisely, we compute the velocity circulation $Γ_r$ in loops of size $r$ laying in the inertial range. For both, experimental and numerical data, the circulation variance shows a clear Kolmogorov scaling $\langle Γ_r^2 \rangle \sim r^{8/3}$ in the inertial range, independently of the temperature. Scaling exponents of high-order moments are comparable, within error bars, to previously reported anomalous circulation exponents in classical turbulence and low-temperature quantum turbulence numerical simulations.

physics.flu-dyn↗

arXiv2022

Cross Pairwise Ranking for Unbiased Item Recommendation

Most recommender systems optimize the model on observed interaction data, which is affected by the previous exposure mechanism and exhibits many biases like popularity bias. The loss functions, such as the mostly used pointwise Binary Cross-Entropy and pairwise Bayesian Personalized Ranking, are not designed to consider the biases in observed data. As a result, the model optimized on the loss would inherit the data biases, or even worse, amplify the biases. For example, a few popular items take up more and more exposure opportunities, severely hurting the recommendation quality on niche items -- known as the notorious Mathew effect. In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism. Distinct from inverse propensity scoring (IPS), we change the loss term of a sample -- we innovatively sample multiple observed interactions once and form the loss as the combination of their predictions. We prove in theory that this way offsets the influence of user/item propensity on the learning, removing the influence of data biases caused by the exposure mechanism. Advantageous to IPS, our proposed CPR ensures unbiased learning for each training instance without the need of setting the propensity scores. Experimental results demonstrate the superiority of CPR over state-of-the-art debiasing solutions in both model generalization and training efficiency. The codes are available at https://github.com/Qcactus/CPR.

cs.IR↗

arXiv2022

Extending the WILDS Benchmark for Unsupervised Adaptation

Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well. However, existing distribution shift benchmarks with unlabeled data do not reflect the breadth of scenarios that arise in real-world applications. In this work, we present the WILDS 2.0 update, which extends 8 of the 10 datasets in the WILDS benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment. These datasets span a wide range of applications (from histology to wildlife conservation), tasks (classification, regression, and detection), and modalities (photos, satellite images, microscope slides, text, molecular graphs). The update maintains consistency with the original WILDS benchmark by using identical labeled training, validation, and test sets, as well as the evaluation metrics. On these datasets, we systematically benchmark state-of-the-art methods that leverage unlabeled data, including domain-invariant, self-training, and self-supervised methods, and show that their success on WILDS is limited. To facilitate method development and evaluation, we provide an open-source package that automates data loading and contains all of the model architectures and methods used in this paper. Code and leaderboards are available at https://wilds.stanford.edu.

cs.LG↗

arXiv2022

6G-enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions

6G-enabled edge intelligence opens up a new era of Internet of Everything and makes it possible to interconnect people-devices-cloud anytime, anywhere. More and more next-generation wireless network smart service applications are changing our way of life and improving our quality of life. As the hottest new form of next-generation Internet applications, Metaverse is striving to connect billions of users and create a shared world where virtual and reality merge. However, limited by resources, computing power, and sensory devices, Metaverse is still far from realizing its full vision of immersion, materialization, and interoperability. To this end, this survey aims to realize this vision through the organic integration of 6G-enabled edge AI and Metaverse. Specifically, we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in Metaverse. Then we summarize technical challenges that these architectures face in Metaverse and the existing solutions. Furthermore, we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital data. Finally, we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

cs.NI↗

arXiv2022

Efficient Community Detection in Large-Scale Dynamic Networks Using Topological Data Analysis

In this paper, we propose a method that extends the persistence-based topological data analysis (TDA) that is typically used for characterizing shapes to general networks. We introduce the concept of the community tree, a tree structure established based on clique communities from the clique percolation method, to summarize the topological structures in a network from a persistence perspective. Furthermore, we develop efficient algorithms to construct and update community trees by maintaining a series of clique graphs in the form of spanning forests, in which each spanning tree is built on an underlying Euler Tour tree. With the information revealed by community trees and the corresponding persistence diagrams, our proposed approach is able to detect clique communities and keep track of the major structural changes during their evolution given a stability threshold. The results demonstrate its effectiveness in extracting useful structural insights for time-varying social networks.

cs.SI↗

arXiv2022

Rotated Object Detection via Scale-invariant Mahalanobis Distance in Aerial Images

Rotated object detection in aerial images is a meaningful yet challenging task as objects are densely arranged and have arbitrary orientations. The eight-parameter (coordinates of box vectors) methods in rotated object detection usually use ln-norm losses (L1 loss, L2 loss, and smooth L1 loss) as loss functions. As ln-norm losses are mainly based on non-scale-invariant Minkowski distance, using ln-norm losses will lead to inconsistency with the detection metric rotational Intersection-over-Union (IoU) and training instability. To address the problems, we use Mahalanobis distance to calculate loss between the predicted and the target box vertices' vectors, proposing a new loss function called Mahalanobis Distance Loss (MDL) for eight-parameter rotated object detection. As Mahalanobis distance is scale-invariant, MDL is more consistent with detection metric and more stable during training than ln-norm losses. To alleviate the problem of boundary discontinuity like all other eight-parameter methods, we further take the minimum loss value to make MDL continuous at boundary cases. We achieve state-of-art performance on DOTA-v1.0 with the proposed method MDL. Furthermore, compared to the experiment that uses smooth L1 loss, we find that MDL performs better in rotated object detection.

cs.CV↗

arXiv2022

Investigation on the self-starting Mamyshev oscillator with low threshold

We have demonstrated a mode-locked Mamyshev oscillator based on a single-mode-fibre configuration with low threshould. The influences of spectral filters and the pump power on the output laser characteristics have been investigated. In the case with spectral-filter central wavelength far away from the peak of gain spectrum, the laser pulses have deviated slightly from similariton evolution with gain-shaping under high pump power. Moreover, self-starting mode-locking operation has been realized when the pump power was only 520 mW, which might benefit from the appropriate polarization conditions and small spectral filter separation. The laser can generate 1.89-nJ ultra-short pulses with 20-dB spectral width of 54.6 nm. The pulse duration can be compressed externally to 64.69 fs with the peak power of 21.3 kW.

physics.optics↗

arXiv2022

Universal anomalous turbulent diffusion in quantum fluids

In classical viscous fluids, turbulent eddies are known to be responsible for the rapid spreading of embedded particles. But in an inviscid quantum fluid where the turbulence is induced by a chaotic tangle of quantized vortices, dispersion of the particles is achieved via a non-classical mechanism, i.e., their binding to the evolving quantized vortices. However, there is limited existing knowledge on how the vortices diffuse and spread in turbulent quantum fluids. Here we report a systematic numerical study of the apparent diffusion of vortices in a random vortex tangle in superfluid helium-4 using full Biot-Savart simulation. We reveal that the vortices in pure superfluid exhibit a universal anomalous diffusion (superdiffusion) at small times, which transits to normal diffusion at large times. This behavior is found to be caused by a generic scaling property of the vortex velocity, which should exist in all quantum fluids where the Biot-Savart law governs the vortex motion. Our simulation at finite temperatures also nicely reproduces recent experimental observations. The knowledge obtained from this study may form the foundation for understanding turbulent transport and universal vortex dynamics in various condensed-matter and cosmic quantum fluids.

physics.flu-dyn↗

arXiv2022

Single electrons on solid neon as a solid-state qubit platform

Progress toward the realization of quantum computers requires persistent advances in their constituent building blocks - qubits. Novel qubit platforms that simultaneously embody long coherence, fast operation, and large scalability offer compelling advantages in the construction of quantum computers and many other quantum information systems. Electrons, ubiquitous elementary particles of nonzero charge, spin, and mass, have commonly been perceived as paradigmatic local quantum information carriers. Despite superior controllability and configurability, their practical performance as qubits via either motional or spin states depends critically on their material environment. Here we report our experimental realization of a new qubit platform based upon isolated single electrons trapped on an ultraclean solid neon surface in vacuum. By integrating an electron trap in a circuit quantum electrodynamics architecture, we achieve strong coupling between the motional states of a single electron and a single microwave photon in an on-chip superconducting resonator. Qubit gate operations and dispersive readout are implemented to measure the energy relaxation time $T_1$ of $15~μ$s and phase coherence time $T_2$ over $200~$ns. These results indicate that the electron-on-solid-neon qubit already performs near the state of the art as a charge qubit.

quant-ph↗

arXiv2022

Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance

The separation assurance task will be extremely challenging for air traffic controllers in a complex and high density airspace environment. Deep reinforcement learning (DRL) was used to develop an autonomous separation assurance framework in our previous work where the learned model advised speed maneuvers. In order to improve the safety of this model in unseen environments with uncertainties, in this work we propose a safety module for DRL in autonomous separation assurance applications. The proposed module directly addresses both model uncertainty and state uncertainty to improve safety. Our safety module consists of two sub-modules: (1) the state safety sub-module is based on the execution-time data augmentation method to introduce state disturbances in the model input state; (2) the model safety sub-module is a Monte-Carlo dropout extension that learns the posterior distribution of the DRL model policy. We demonstrate the effectiveness of the two sub-modules in an open-source air traffic simulator with challenging environment settings. Through extensive numerical experiments, our results show that the proposed sub-safety modules help the DRL agent significantly improve its safety performance in an autonomous separation assurance task.

cs.AI↗

arXiv2022

High order chiral Lagrangians with vector mesons in different approaches

The chiral Lagrangians with vector mesons are constructed in different approaches, including the next-to-leading order Lagrangian in the vector-field approach, the next-to-next-to-leading order Lagrangians in the tensor-field and the hidden local symmetry approaches. Some redundant terms are found at the next-to-leading order in the tensor-field and the hidden local symmetry approaches. The corresponding relations between the next-to-next-to-leading order pseudoscalar mesonic low-energy constants and the ones in the hidden local symmetry approach are obtained at tree level. The equivalence between the tensor-field approach and the hidden local symmetry approach is also discussed.

hep-ph↗

arXiv2022

MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction

CTR prediction is essential for modern recommender systems. Ranging from early factorization machines to deep learning based models in recent years, existing CTR methods focus on capturing useful feature interactions or mining important behavior patterns. Despite the effectiveness, we argue that these methods suffer from the risk of label sparsity (i.e., the user-item interactions are highly sparse with respect to the feature space), label noise (i.e., the collected user-item interactions are usually noisy), and the underuse of domain knowledge (i.e., the pairwise correlations between samples). To address these challenging problems, we propose a novel Multi-Interest Self-Supervised learning (MISS) framework which enhances the feature embeddings with interest-level self-supervision signals. With the help of two novel CNN-based multi-interest extractors,self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item). Based on that, contrastive learning losses are further applied to the augmented views of interest representations, which effectively improves the feature representation learning. Furthermore, our proposed MISS framework can be used as an plug-in component with existing CTR prediction models and further boost their performances. Extensive experiments on three large-scale datasets show that MISS significantly outperforms the state-of-the-art models, by up to 13.55% in AUC, and also enjoys good compatibility with representative deep CTR models.

cs.IR↗

arXiv2022

A conservative low rank tensor method for the Vlasov dynamics

In this paper, we propose a conservative low rank tensor method to approximate nonlinear Vlasov solutions. The low rank approach is based on our earlier work (arxiv: 2106.08834). It takes advantage of the fact that the differential operators in the Vlasov equation are tensor friendly, based on which we propose to dynamically and adaptively build up low rank solution basis by adding new basis functions from discretization of the differential equation, and removing basis from a singular value decomposition (SVD)-type truncation procedure. For the discretization, we adopt a high order finite difference spatial discretization together with a second order strong stability preserving multi-step time discretization. While the SVD truncation will remove the redundancy in representing the high dimensional Vlasov solution, it will destroy the conservation properties of the associated full conservative scheme. In this paper, we develop a conservative truncation procedure with conservation of mass, momentum and kinetic energy densities. The conservative truncation is achieved by an orthogonal projection onto a subspace spanned by $1$, $v$ and $v^2$ in the velocity space associated with a weighted inner product. Then the algorithm performs a weighted SVD truncation of the remainder, which involves a scaling, followed by the standard SVD truncation and rescaling back. The algorithm is further developed in high dimensions with hierarchical Tucker tensor decomposition of high dimensional Vlasov solutions, overcoming the curse of dimensionality. An extensive set of nonlinear Vlasov examples are performed to show the effectiveness and conservation property of proposed conservative low rank approach. Comparison is performed against the non-conservative low rank tensor approach on conservation history of mass, momentum and energy.

math.NA↗

arXiv2021

Commensurate stacking phase transitions in an intercalated transition metal dichalcogenide

Intercalation and stacking order modulation are two active ways in manipulating the interlayer interaction of transition metal dichalcogenides (TMDCs), which lead to a variety of emergent phases and allow for engineering material properties. Herein, the growth of Pb intercalated TMDCs--Pb(Ta$_{1+x}$Se$_2$)$_2$, the first 124-phase, is reported. Pb(Ta$_{1+x}$Se$_2$)$_2$ exhibits a unique two-step first-order structural phase transition at around 230 K. The transitions are solely associated with the stacking degree of freedom, evolving from a high temperature phase with ABC stacking and symmetry $R3m$ to an intermediate phase with AB stacking and $P3m1$, and finally to a low temperature phase with again symmetry $R3m$, but with ACB stacking. Each step involves a rigid slide of building blocks by a vector [1/3, 2/3, 0]. Intriguingly, gigantic lattice contractions occur at the transitions on warming. At low temperature, bulk superconductivity with $T_\textrm{c}\approx$ 1.8 K is observed. The underlying physics of the structural phase transitions are discussed from first-principle calculations. The symmetry analysis reveals topological nodal lines in the band structure. Our results demonstrate the possibility to realize higher order metal intercalated phases of TMDCs, advance our knowledge of polymorphic transitions and may inspire stacking order engineering in TMDCs and beyond.

cond-mat.mtrl-sci↗

arXiv2021

Light propagation and observed light propagation

Light propagation is viewed as a process involving mutual creation of electric and magnetic fields. This viewpoint is used to argue that the conventional retarded solutions to electromagnetic wave equations (whose source is a current density in this work) are wrong, and to solve the wave equations in a different way by tracking every step in the said process. It turns out that the solutions to the wave equations, or the emitted fields from the current density, have equally weighted advanced and retarded components. After these components are explained for their mathematical and physical origins, it is then pointed out that the emitted fields are related but not identical to those fields observed in light observation. The latter fields are calculated and found, as expected, to be retarded.

physics.class-ph↗

arXiv2021

An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences

Together with impressive advances touching every aspect of our society, AI technology based on Deep Neural Networks (DNN) is bringing increasing security concerns. While attacks operating at test time have monopolised the initial attention of researchers, backdoor attacks, exploiting the possibility of corrupting DNN models by interfering with the training process, represents a further serious threat undermining the dependability of AI techniques. In a backdoor attack, the attacker corrupts the training data so to induce an erroneous behaviour at test time. Test time errors, however, are activated only in the presence of a triggering event corresponding to a properly crafted input sample. In this way, the corrupted network continues to work as expected for regular inputs, and the malicious behaviour occurs only when the attacker decides to activate the backdoor hidden within the network. In the last few years, backdoor attacks have been the subject of an intense research activity focusing on both the development of new classes of attacks, and the proposal of possible countermeasures. The goal of this overview paper is to review the works published until now, classifying the different types of attacks and defences proposed so far. The classification guiding the analysis is based on the amount of control that the attacker has on the training process, and the capability of the defender to verify the integrity of the data used for training, and to monitor the operations of the DNN at training and test time. As such, the proposed analysis is particularly suited to highlight the strengths and weaknesses of both attacks and defences with reference to the application scenarios they are operating in.

cs.CR↗

arXiv2021

Temporal analysis of radiating current densities

From electromagnetic wave equations, it is first found that, mathematically, any current density that emits an electromagnetic wave into the far-field region has to be differentiable in time infinitely, and that while the odd-order time derivatives of the current density are built in the emitted electric field, the even-order derivatives are built in the emitted magnetic field. With the help of Faraday's law and Ampere's law, light propagation is then explained as a process involving alternate creation of electric and magnetic fields. From this explanation, the preceding mathematical result is demonstrated to be physically sound. It is also explained why the conventional retarded solutions to the wave equations fail to describe the emitted fields.

physics.class-ph↗

arXiv2021

a dynamo-based prediction of solar cycle 25

Solar activity cycle varies in amplitude. The last Cycle 24 is the weakest in the past century. Sun's activity dominates Earth's space environment. The frequency and intensity of the Sun's activity are accordant with the solar cycle. Hence there are practical needs to know the amplitude of the upcoming Cycle 25. The dynamo-based solar cycle predictions not only provide predictions, but also offer an effective way to evaluate our understanding of the solar cycle. In this article we apply the method of the first successful dynamo-based prediction developed for Cycle 24 to the prediction of Cycle 25, so that we can verify whether the previous success is repeatable. The prediction shows that Cycle 25 would be about 10% stronger than Cycle 24 with an amplitude of 126 (international sunspot number version 2.0). The result suggests that Cycle 25 will not enter the Maunder-like grand solar minimum as suggested by some publications. Solar behavior in about four to five years will give a verdict whether the prediction method captures the key mechanism for solar cycle variability, which is assumed as the polar field around the cycle minimum in the model.

astro-ph.SR↗