arXiv subjects

Pengfei Wang

Publications and source records attributed to Pengfei Wang.

At least 181 records · Page 10Linked to original sources

arXiv2022

UltraFuzz: Towards Resource-saving in Distributed Fuzzing

Recent research has sought to improve fuzzing performance via parallel computing. However, researchers focus on improving efficiency while ignoring the increasing cost of testing resources. Parallel fuzzing in the distributed environment amplifies the resource-wasting problem caused by the random nature of fuzzing. In the parallel mode, owing to the lack of an appropriate task dispatching scheme and timely fuzzing status synchronization among different fuzzing instances, task conflicts and workload imbalance occur, making the resource-wasting problem severe. In this paper, we design UltraFuzz, a fuzzer for resource-saving in distributed fuzzing. Based on centralized dynamic scheduling, UltraFuzz can dispatch tasks and schedule power globally and reasonably to avoid resource-wasting. Besides, UltraFuzz can elastically allocate computing power for fuzzing and seed evaluation, thereby avoiding the potential bottleneck of seed evaluation that blocks the fuzzing process. UltraFuzz was evaluated using real-world programs, and the results show that with the same testing resource, UltraFuzz outperforms state-of-the-art tools, such as AFL, AFL-P, PAFL, and EnFuzz. Most importantly, the experiment reveals certain results that seem counter-intuitive, namely that parallel fuzzing can achieve ``super-linear acceleration'' when compared with single-core fuzzing. We conduct additional experiments to reveal the deep reasons behind this phenomenon and dig deep into the inherent advantages of parallel fuzzing over serial fuzzing, including the global optimization of seed energy scheduling and the escape of local optimal seed. Additionally, 24 real-world vulnerabilities were discovered using UltraFuzz.

cs.CR↗

arXiv2022

Observation of uniaxial strain tuned spin cycloid in a freestanding BiFeO3 film

Non-collinear spin order that breaks space inversion symmetry and allows efficient electric-field control of magnetism makes BiFeO$_3$ a promising candidate for applications in low-power spintronic devices. Epitaxial strain effects have been intensively studied and exhibit significant modulation of the magnetic order in BiFeO$_3$, but tuning its spin structure with continuously varied uniaxial strain is still lacking up to date. Here, we apply \emph{in situ} uniaxial strain to a freestanding BiFeO$_3$ film and use scanning NV microscope to image the nanoscale magnetic order in real-space. The strain is continuously increased from 0\% to 1.5\% and four images under different strains are acquired during this period. The images show that the spin cycloid tilts by $\sim 12.6^\circ$ when strain approaches 1.5\%. A first principle calculation has been processed to show that the tilting is energetically favorable under such strain. Our \emph{in situ} strain applying method in combination with scanning NV microscope real-space imaging ability paves a new way in studying the coupling between magnetic order and strain in BiFeO$_3$ films.

cond-mat.mtrl-sci↗

arXiv2022

Tunable quantum criticalities in an isospin extended Hubbard model simulator

Studying strong electron correlations has been an essential driving force for pushing the frontiers of condensed matter physics. In particular, in the vicinity of correlation-driven quantum phase transitions (QPTs), quantum critical fluctuations of multiple degrees of freedom facilitate exotic many-body states and quantum critical behaviors beyond Landau's framework. Recently, moiré heterostructures of van der Waals materials have been demonstrated as a highly tunable quantum platform for exploring fascinating strongly correlated quantum physics. Here, we report the observation of tunable quantum criticalities in an experimental simulator of extended Hubbard model with spin-valley isospins arising in chiral-stacked twisted double bilayer graphene. Scaling analysis shows a quantum two-stage criticality manifesting two distinct quantum critical points as the generalized Wigner crystal transits to a Fermi liquid by varying the displacement field, suggesting the emergence of a critical intermediate phase. The quantum two-stage criticality evolves into a quantum pseudo criticality as a high parallel magnetic field is applied. In such pseudo criticality, we find that the quantum critical scaling is only valid above a critical temperature, indicating a weak first-order QPT therein. Our results demonstrate a highly tunable solid-state simulator with intricate interplay of multiple degrees of freedom for exploring exotic quantum critical states and behaviors.

cond-mat.mes-hall↗

arXiv2022

PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching

Entity Matching (EM), which aims to identify whether two entity records from two relational tables refer to the same real-world entity, is one of the fundamental problems in data management. Traditional EM assumes that two tables are homogeneous with the aligned schema, while it is common that entity records of different formats (e.g., relational, semi-structured, or textual types) involve in practical scenarios. It is not practical to unify their schemas due to the different formats. To support EM on format-different entity records, Generalized Entity Matching (GEM) has been proposed and gained much attention recently. To do GEM, existing methods typically perform in a supervised learning way, which relies on a large amount of high-quality labeled examples. However, the labeling process is extremely labor-intensive, and frustrates the use of GEM. Low-resource GEM, i.e., GEM that only requires a small number of labeled examples, becomes an urgent need. To this end, this paper, for the first time, focuses on the low-resource GEM and proposes a novel low-resource GEM method, termed as PromptEM. PromptEM has addressed three challenging issues (i.e., designing GEM-specific prompt-tuning, improving pseudo-labels quality, and running efficient self-training) in low-resource GEM. Extensive experimental results on eight real benchmarks demonstrate the superiority of PromptEM in terms of effectiveness and efficiency.

cs.DB↗

arXiv2022

IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning

This paper studies how to improve the generalization performance and learning speed of the navigation agents trained with deep reinforcement learning (DRL). Although DRL exhibits huge potential in robot mapless navigation, DRL agents performing well in training scenarios are often found to perform poorly in unfamiliar scenarios. In this work, we propose that the representation of LiDAR readings is a key factor behind the degradation of agents' performance and present a powerful input pre-processing (IP) approach to address this issue. As this approach uses adaptively parametric reciprocal functions to pre-process LiDAR readings, we refer to this approach as IPAPRec and its normalized version as IPAPRecN. IPAPRec/IPAPRecN can highlight important short-distance values and compress the range of less-important long-distance values in laser scans, which well address the issues induced by conventional representations of laser scans. Their high performance was validated by extensive simulation and real-world experiments. The results show that our methods can substantially improve navigation agents' generalization performance and greatly reduce the training time compared to conventional methods.

cs.RO↗

arXiv2022

Fast multi-qubit global-entangling gates without individual addressing of trapped ions

We propose and study ways speeding up of the entangling operations in the trapped ions system with high fidelity. First, we find a scheme to increase the speed of a two-qubit gate without the limitation of trap frequency, which was considered as the fundamental limit. Second, we study the fast gate scheme for entangling more than two qubits simultaneously. We apply the method of applying multiple frequency components on laser beams for the gate operations. In particular, in order to avoid infinite terms from the coupling to carrier transition, we focus on the phase-insensitive gate scheme here. We carefully study the effect of large excitation of motional mode beyond the limit of Lamb-Dicke approximation by including up to second order terms of the Lamb-Dicke parameter. We study the speed limit of multi-qubit global entangling gates without individual addressing requirements. Furthermore, our gates can be made insensitive to the fluctuation of initial motional phases which are difficult to stabilise in the phase-insensitive gate scheme.

quant-ph↗

arXiv2022

ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities

Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs). Embedding-based approaches have dominated the EA task in recent years. Those methods face problems that come from the geometric properties of embedding vectors, including hubness and isolation. To solve these geometric problems, many normalization approaches have been adopted for EA. However, the increasing scale of KGs renders it hard for EA models to adopt the normalization processes, thus limiting their usage in real-world applications. To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate. ClusterEA contains three components to align entities between large-scale KGs, including stochastic training, ClusterSampler, and SparseFusion. It first trains a large-scale Siamese GNN for EA in a stochastic fashion to produce entity embeddings. Based on the embeddings, a novel ClusterSampler strategy is proposed for sampling highly overlapped mini-batches. Finally, ClusterEA incorporates SparseFusion, which normalizes local and global similarity and then fuses all similarity matrices to obtain the final similarity matrix. Extensive experiments with real-life datasets on EA benchmarks offer insight into the proposed framework, and suggest that it is capable of outperforming the state-of-the-art scalable EA framework by up to 8 times in terms of Hits@1.

cs.DB↗

arXiv2022

Observing frustrated quantum magnetism in two-dimensional ion crystals

Two-dimensional (2D) quantum magnetism is a paradigm in strongly correlated many-body physics. The understanding of 2D quantum magnetism can be expedited by employing a controllable quantum simulator that faithfully maps 2D-spin Hamiltonians. The 2D quantum simulators can exhibit exotic phenomena such as frustrated quantum magnetism and topological order and can be used to show quantum computational advantages. Many experimental platforms are being developed, including Rydberg atoms and superconducting annealers. However, with trapped-ion systems, which showed the most advanced controllability and quantum coherence, quantum magnetism was explored in one-dimensional chains. Here, we report simulations of frustrated quantum magnetism with 2D ion crystals. We create a variety of spin-spin interactions for quantum magnets, including those that exhibit frustration by driving different vibrational modes and adiabatically prepare the corresponding ground states. The experimentally measured ground states are consistent with the theoretical predictions and are highly degenerate for geometrically frustrated spin models in two dimensions. Quantum coherence of the ground states is probed by reversing the time evolution of the B-field to the initial value and then measuring the extent to which the remaining state coincides with the initial state. Our results open the door for quantum simulations with 2D ion crystals.

quant-ph↗

arXiv2022

Self-aligned patterning technique for fabricating high-performance diamond sensor arrays with nanoscale precision

To efficiently align the creation of defect center with photonics structure in nanoscale precision is one of the outstanding challenges for realizing high-performance photonic devices and the application in quantum technology such as quantum sensing, scalable quantum systems, and quantum computing network. Here, we propose a facile self-aligned patterning technique wholly based on conventional engineering technology, with the doping precision can reach ~15nm. Specifically, we demonstrate this technique by fabricating diamond nanopillar sensor arrays, which show high consistency and near-optimal photon counts, high yield approaching the theoretical limit, and high filtering efficiency for different NV centers. Combined with appropriate crystal orientation, a saturated fluorescence rate of 4.65 Mcps and the best reported fluorescence-dependent detection sensitivity of 1900 cps^(-1/2) are achieved. This technique applicable to all similar solid-state systems should facilitate the development of parallel quantum sensing and scalable information processing.

physics.app-ph↗

arXiv2022

Blue-band frequency comb and photodarkening in silica whispering gallery microresonators

To date there are extensive studies of optical nonlinearities in whispering gallery resonators (WGRs) in the near and mid-infrared wavelengths. Pushing this research into the visible region is equally valuable. Here, we demonstrate a Kerr frequency comb and Raman lasing at 462 nm in an SiO2 WGR. Notably, due to the high optical intensities achieved, photodarkening is unavoidable and can quickly degrade the optical quality of both the coupling optical nanofiber and the microcavity even at very low pump powers. Nonetheless, stable stimulated Raman scattering (SRS) and hyper-parametric oscillation in normally dispersed WGRs is demonstrated in the presence of photodarkening by taking advantage of in-situ thermal bleaching. These observations highlight the challenges of silica-based, short wavelength nonlinear optics in high quality, small mode volume devices. We propose a method to overcome this apparent limitation and demonstrate blue-band nonlinear optical processes in silica WGRs, thus providing a baseline for optics research in the blue region for any optical devices fabricated from SiO2.

physics.optics↗

arXiv2022

Three-dimensional structure and formation mechanism of biskyrmions in uniaxial ferromagnets

Magnetic biskyrmions are observed in experiments but their existences are still under debate. In this work, we present the existence of biskyrmions in a magnetic film with tilted uniaxial anisotropy via micromagnetic simulations. We find biskyrmions and bubbles share a unified three-dimensional structure, in which the relative position of two intrinsic Bloch points dominates the two-dimensional topological property in the film middle. The film edge can drive Bloch points and transform bubbles into biskyrmions via the demagnetizing field. This mechanism is found in the formation process of biskyrmions in confined geometry under zero field. Our work clarifies the structure and formation mechanism of biskyrmions, emphasizing the three-dimensional aspect of skyrmion-related nanostructures.

cond-mat.str-el↗

arXiv2022

Quality-aware Part Models for Occluded Person Re-identification

Occlusion poses a major challenge for person re-identification (ReID). Existing approaches typically rely on outside tools to infer visible body parts, which may be suboptimal in terms of both computational efficiency and ReID accuracy. In particular, they may fail when facing complex occlusions, such as those between pedestrians. Accordingly, in this paper, we propose a novel method named Quality-aware Part Models (QPM) for occlusion-robust ReID. First, we propose to jointly learn part features and predict part quality scores. As no quality annotation is available, we introduce a strategy that automatically assigns low scores to occluded body parts, thereby weakening the impact of occluded body parts on ReID results. Second, based on the predicted part quality scores, we propose a novel identity-aware spatial attention (ISA) module. In this module, a coarse identity-aware feature is utilized to highlight pixels of the target pedestrian, so as to handle the occlusion between pedestrians. Third, we design an adaptive and efficient approach for generating global features from common non-occluded regions with respect to each image pair. This design is crucial, but is often ignored by existing methods. QPM has three key advantages: 1) it does not rely on any outside tools in either the training or inference stages; 2) it handles occlusions caused by both objects and other pedestrians;3) it is highly computationally efficient. Experimental results on four popular databases for occluded ReID demonstrate that QPM consistently outperforms state-of-the-art methods by significant margins. The code of QPM will be released.

cs.CV↗

arXiv2021

Significant-loophole-free test of Kochen-Specker contextuality using two species of atomic-ions

Quantum measurements cannot be thought of as revealing preexisting results, even when they do not disturb any other measurement in the same trial. This feature is called contextuality and is crucial for the quantum advantage in computing. Here, we report the first observation of quantum contextuality simultaneously free of the detection, sharpness and compatibility loopholes. The detection and sharpness loopholes are closed by adopting a hybrid two-ion system and highly efficient fluorescence measurements offering a detection efficiency of $100\%$ and a measurement repeatability $>98\%$. The compatibility loophole is closed by targeting correlations between observables for two different ions in a Paul trap, a $^{171}\mathrm{Yb}^{+}$ ion and a $^{138}\mathrm{Ba}^{+}$ ion, chosen so measurements on each ion use different operation laser wavelengths, fluorescence wavelengths, and detectors. The experimental results show a violation of the bound for the most adversarial noncontextual models and open a new way to certify quantum systems.

quant-ph↗

arXiv2021

Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy

In this paper, we propose a single-agent Monte Carlo based reinforced feature selection (MCRFS) method, as well as two efficiency improvement strategies, i.e., early stopping (ES) strategy and reward-level interactive (RI) strategy. Feature selection is one of the most important technologies in data prepossessing, aiming to find the optimal feature subset for a given downstream machine learning task. Enormous research has been done to improve its effectiveness and efficiency. Recently, the multi-agent reinforced feature selection (MARFS) has achieved great success in improving the performance of feature selection. However, MARFS suffers from the heavy burden of computational cost, which greatly limits its application in real-world scenarios. In this paper, we propose an efficient reinforcement feature selection method, which uses one agent to traverse the whole feature set, and decides to select or not select each feature one by one. Specifically, we first develop one behavior policy and use it to traverse the feature set and generate training data. And then, we evaluate the target policy based on the training data and improve the target policy by Bellman equation. Besides, we conduct the importance sampling in an incremental way, and propose an early stopping strategy to improve the training efficiency by the removal of skew data. In the early stopping strategy, the behavior policy stops traversing with a probability inversely proportional to the importance sampling weight. In addition, we propose a reward-level interactive strategy to improve the training efficiency via reward-level external advice. Finally, we design extensive experiments on real-world data to demonstrate the superiority of the proposed method.

cs.LG↗

arXiv2021

CollaborER: A Self-supervised Entity Resolution Framework Using Multi-features Collaboration

Entity Resolution (ER) aims to identify whether two tuples refer to the same real-world entity and is well-known to be labor-intensive. It is a prerequisite to anomaly detection, as comparing the attribute values of two matched tuples from two different datasets provides one effective way to detect anomalies. Existing ER approaches, due to insufficient feature discovery or error-prone inherent characteristics, are not able to achieve stable performance. In this paper, we present CollaborER, a self-supervised entity resolution framework via multi-features collaboration. It is capable of (i) obtaining reliable ER results with zero human annotations and (ii) discovering adequate tuples' features in a fault-tolerant manner. CollaborER consists of two phases, i.e., automatic label generation (ALG) and collaborative ER training (CERT). In the first phase, ALG is proposed to generate a set of positive tuple pairs and a set of negative tuple pairs. ALG guarantees the high quality of the generated tuples and hence ensures the training quality of the subsequent CERT. In the second phase, CERT is introduced to learn the matching signals by discovering graph features and sentence features of tuples collaboratively. Extensive experimental results over eight real-world ER benchmarks show that CollaborER outperforms all the existing unsupervised ER approaches and is comparable or even superior to the state-of-the-art supervised ER methods.

cs.DB↗

arXiv2021

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole, which provides a unified framework to develop and reproduce recommendation algorithms for research purpose. In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We implement the RecBole library based on PyTorch, which is one of the most popular deep learning frameworks. Our library is featured in many aspects, including general and extensible data structures, comprehensive benchmark models and datasets, efficient GPU-accelerated execution, and extensive and standard evaluation protocols. We provide a series of auxiliary functions, tools, and scripts to facilitate the use of this library, such as automatic parameter tuning and break-point resume. Such a framework is useful to standardize the implementation and evaluation of recommender systems. The project and documents are released at https://recbole.io/.

cs.IR↗

arXiv2021

Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification

Unsupervised Domain Adaptive (UDA) object re-identification (Re-ID) aims at adapting a model trained on a labeled source domain to an unlabeled target domain. State-of-the-art object Re-ID approaches adopt clustering algorithms to generate pseudo-labels for the unlabeled target domain. However, the inevitable label noise caused by the clustering procedure significantly degrades the discriminative power of Re-ID model. To address this problem, we propose an uncertainty-aware clustering framework (UCF) for UDA tasks. First, a novel hierarchical clustering scheme is proposed to promote clustering quality. Second, an uncertainty-aware collaborative instance selection method is introduced to select images with reliable labels for model training. Combining both techniques effectively reduces the impact of noisy labels. In addition, we introduce a strong baseline that features a compact contrastive loss. Our UCF method consistently achieves state-of-the-art performance in multiple UDA tasks for object Re-ID, and significantly reduces the gap between unsupervised and supervised Re-ID performance. In particular, the performance of our unsupervised UCF method in the MSMT17$\to$Market1501 task is better than that of the fully supervised setting on Market1501. The code of UCF is available at https://github.com/Wang-pengfei/UCF.

cs.CV↗

arXiv2021

Double-EIT Ground-State Cooling of Stationary Two-Dimensional Ion Lattices

We theoretically and experimentally investigate double electromagnetically induced transparency (double-EIT) cooling of two-dimensional ion crystals confined in a Paul trap. The double-EIT ground-state cooling is observed for \Yb ions with clock state, for which EIT cooling has not been realized like many other ions with a simple $Λ$-scheme. A cooling rate of $\dot{\bar n}=34~(\pm1.8)~\rm{ms}^{-1}$ and a cooling limit of $\bar n=0.06~(\pm 0.059)$ are observed for a single ion. The measured cooling rate and limit are consistent with theoretical predictions. We apply double-EIT cooling to the transverse modes of two-dimensional (2D) crystals with up to 12 ions. In our 2D crystals, the micromotion and the transverse mode directions are perpendicular, which makes them decoupled. Therefore, the cooling on transverse modes is not disturbed by micromotion, which is confirmed in our experiment. For the center of mass mode of a 12 ions crystal, we observe a cooling rate and a cooling limit that are consistent with those of a single ion, including heating rates proportional to the number of ions. This method can be extended to other hyperfine qubits, and near ground-state cooling of stationary 2D crystals with large numbers of ions may advance the field of quantum information sciences.

quant-ph↗