arXiv subjects

Peng Wang

Publications and source records attributed to Peng Wang.

At least 631 records · Page 35Linked to original sources

arXiv2023

Baryonic Effects on Lagrangian Clustering and Angular Momentum Reconstruction

Recent studies illustrate the correlation between the angular momenta of cosmic structures and their Lagrangian properties. However, only baryons are observable and it is unclear whether they reliably trace the cosmic angular momenta. We study the Lagrangian mass distribution, spin correlation, and predictability of dark matter, gas, and stellar components of galaxy-halo systems using IllustrisTNG, and show that the primordial segregations between components are typically small. Their protoshapes are also similar in terms of the statistics of moment of inertia tensors. Under the common gravitational potential they are expected to exert the same tidal torque and the strong spin correlations are not destroyed by the nonlinear evolution and complicated baryonic effects, as confirmed by the high-resolution hydrodynamic simulations. We further show that their late-time angular momenta traced by total gas, stars, or the central galaxies, can be reliably reconstructed by the initial perturbations. These results suggest that baryonic angular momenta can potentially be used in reconstructing the parameters and models related to the initial perturbations.

astro-ph.CO↗

arXiv2023

Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load

Data-centric prognostics is beneficial to improve the reliability and safety of proton exchange membrane fuel cell (PEMFC). For the prognostics of PEMFC operating under dynamic load, the challenges come from extracting degradation features, improving prediction accuracy, expanding the prognostics horizon, and reducing computational cost. To address these issues, this work proposes a data-driven PEMFC prognostics approach, in which Hilbert-Huang transform is used to extract health indicator in dynamic operating conditions and symbolic-based gated recurrent unit model is used to enhance the accuracy of life prediction. Comparing with other state-of-the-art methods, the proposed data-driven prognostics approach provides a competitive prognostics horizon with lower computational cost. The prognostics performance shows consistency and generalizability under different failure threshold settings.

cs.LG↗

arXiv2023

Data Volume-aware Computation Task Scheduling for Smart Grid Data Analytic Applications

Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.

cs.DC↗

arXiv2023

Superradiance Instabilities of Charged Black Holes in Einstein-Maxwell-scalar Theory

We study time evolutions of charged scalar perturbations on the background of a charged hairy black hole, in which the perturbations can be governed by a double-peak effective potential. By extracting quasinormal modes from the waveform of scalar perturbations, we discover that some quasinormal modes, which are trapped in a potential well between two potential peaks, can be superradiantly amplified. These superradiant modes make the hairy black hole unstable against charged scalar perturbations. Moreover, it is found that the superradiant modes arise from the competition between the superradiant amplification caused by tunneling through the outer potential barrier and the leakage of modes through the inner potential barrier into the black hole.

gr-qc↗

arXiv2023

NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction

We present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with high fidelity from 2D image inputs. Existing neural surface reconstruction approaches, such as DVR and IDR, require foreground mask as supervision, easily get trapped in local minima, and therefore struggle with the reconstruction of objects with severe self-occlusion or thin structures. Meanwhile, recent neural methods for novel view synthesis, such as NeRF and its variants, use volume rendering to produce a neural scene representation with robustness of optimization, even for highly complex objects. However, extracting high-quality surfaces from this learned implicit representation is difficult because there are not sufficient surface constraints in the representation. In NeuS, we propose to represent a surface as the zero-level set of a signed distance function (SDF) and develop a new volume rendering method to train a neural SDF representation. We observe that the conventional volume rendering method causes inherent geometric errors (i.e. bias) for surface reconstruction, and therefore propose a new formulation that is free of bias in the first order of approximation, thus leading to more accurate surface reconstruction even without the mask supervision. Experiments on the DTU dataset and the BlendedMVS dataset show that NeuS outperforms the state-of-the-arts in high-quality surface reconstruction, especially for objects and scenes with complex structures and self-occlusion.

cs.CV↗

arXiv2023

DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings

Large-scale contrastive learning models can learn very informative sentence embeddings, but are hard to serve online due to the huge model size. Therefore, they often play the role of "teacher", transferring abilities to small "student" models through knowledge distillation. However, knowledge distillation inevitably brings some drop in embedding effect. To tackle that, we propose an effective knowledge distillation framework for contrastive sentence embeddings, termed DistilCSE. It first applies knowledge distillation on a large amount of unlabeled data, and then fine-tunes student models through contrastive learning on limited labeled data. To achieve better distillation results, we further propose Contrastive Knowledge Distillation (CKD). CKD uses InfoNCE as the loss function in knowledge distillation, enhancing the objective consistency among teacher model training, knowledge distillation, and student model fine-tuning. Extensive experiments show that student models trained with the proposed DistilCSE and CKD suffer from little or even no performance decrease and consistently outperform the corresponding counterparts of the same parameter size. Impressively, our 110M student model outperforms the latest state-of-the-art model, i.e., Sentence-T5 (11B), with only 1% parameters and 0.25% unlabeled data.

cs.AI↗

arXiv2023

Classification of Minimal Immersions of Conformally Flat $3$-Tori and $4$-Tori in Spheres by The First Eigenfunctions

This paper is devoted to the study of minimal immersions of flat $n$-tori into spheres, especially those immersed by the first eigenfunctions (such immersion is called $λ_1$-minimal immersion), which also play important roles in spectral geometry. It is known that there are only two non-congruent $λ_1$-minimal $2$-tori in spheres, which are both flat. For higher dimensional case, the Clifford $n$-torus in $\mathbb{S}^{2n-1}$ might be the only known example in the literature. In this paper, by discussing the general construction of homogeneous minimal flat $n$-tori in spheres, we construct many new examples of $λ_1$-minimal flat $3$-tori and $4$-tori. In contrast to the rigidity in the case of $2$-tori, we show that there exists a $2$-parameter family of non-congruent $λ_1$-minimal flat $4$-tori. It turns out that the examples we constructed exhaust all $λ_1$-minimal immersions of conformally flat $3$-tori and $4$-tori in spheres. The classification involves some detailed investigations of shortest vectors in lattices, which can also be used to solve the Berger's problem on flat $3$-tori and $4$-tori. The dilation-invariant functional $λ_1(g)V(g)^{\frac{2}{n}}$ about the first eignvalue is proved to have maximal value among all flat $3$-tori and $4$-tori.

math.DG↗

arXiv2023

Mass testing of the JUNO experiment 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%.

physics.ins-det↗

arXiv2023

Black Holes with Multiple Photon Spheres

Recently, asymptotically-flat black holes with multiple photon spheres have been discovered and found to produce distinctive observational signatures. In this paper, we focus on whether these black hole solutions are physically viable, e.g., satisfying energy conditions of interest. Intriguingly, black hole and naked singularity solutions with two photon spheres and one anti-photon sphere are shown to exist in physically reasonable models, which satisfy the null, weak, dominant and strong energy conditions. Our findings reveal that black holes with multiple photon spheres may not be frequent, but they are not exotic.

gr-qc↗

arXiv2022

Optimizing Replacement Policies for Content Delivery Network Caching: Beyond Belady to Attain A Seemingly Unattainable Byte Miss Ratio

When facing objects/files of differing sizes in content delivery networks (CDNs) caches, pursuing an optimal object miss ratio (OMR) by approximating Belady no longer ensures an optimal byte miss ratio (BMR), creating confusion about how to achieve a superior BMR in CDNs. To address this issue, we experimentally observe that there exists a time window to delay the eviction of the object with the longest reuse distance to improve BMR without increasing OMR. As a result, we introduce a deep reinforcement learning (RL) model to capture this time window by dynamically monitoring the changes in OMR and BMR, and implementing a BMR-friendly policy in the time window. Based on this policy, we propose a Belady and Size Eviction (LRU-BaSE) algorithm, reducing BMR while maintaining OMR. To make LRU-BaSE efficient and practical, we address the feedback delay problem of RL with a two-pronged approach. On the one hand, our observation of a rear section of the LRU cache queue containing most of the eviction candidates allows LRU-BaSE to shorten the decision region. On the other hand, the request distribution on CDNs makes it feasible to divide the learning region into multiple sub-regions that are each learned with reduced time and increased accuracy. In real CDN systems, compared to LRU, LRU-BaSE can reduce "backing to OS" traffic and access latency by 30.05\% and 17.07\%, respectively, on average. The results on the simulator confirm that LRU-BaSE outperforms the state-of-the-art cache replacement policies, where LRU-BaSE's BMR is 0.63\% and 0.33\% less than that of Belady and Practical Flow-based Offline Optimal (PFOO), respectively, on average. In addition, compared to Learning Relaxed Belady (LRB), LRU-BaSE can yield relatively stable performance when facing workload drift.

cs.NI↗

arXiv2022

Measuring cosmic filament spin with the kinetic Sunyaev-Zel'dovich effect

The spin of intergalactic filaments has been predicted from simulations, and supported by tentative evidence from redshift-space filament shapes in a galaxy redshift survey: generally, a filament is redshifted on one side of its axis, and blueshifted on the other. Here, we investigate whether filament spins could have a measurable kinetic Sunyaev-Zel'dovich (kSZ) signal, from CMB photons being scattered by moving ionised gas; this pure velocity information is complementary to filament redshift-space shapes. We propose to measure the kSZ dipole by combining galaxy redshift surveys with CMB experiments. We base our S/N analyses first on an existing filament catalogue, and its combination with Planck data. We then investigate the detectability of the kSZ dipole using the combination of DESI or SKA-2 with next-stage CMB experiments. We find that the gas halos of filament galaxies co-rotating with filaments induce a stronger kSZ dipole signal than that from the diffuse filamentary gas, but both signals seem too small to be detected in near-term surveys such as DESI+future CMB experiments. But the combination of SKA-2 with future CMB experiments could give a more than 10$σ$ detection. The gain comes mainly from an increased area overlap and an increased number of filaments, but also the low noise and high resolution in future CMB experiments are important to capture signals from filaments small on the sky. Successful detection of the signals may help to find the gravitomagnetic effect in large-scale structure and advance our understanding of baryons in the cosmic web.

astro-ph.CO↗

arXiv2022

Transferring General Multimodal Pretrained Models to Text Recognition

This paper proposes a new method, OFA-OCR, to transfer multimodal pretrained models to text recognition. Specifically, we recast text recognition as image captioning and directly transfer a unified vision-language pretrained model to the end task. Without pretraining on large-scale annotated or synthetic text recognition data, OFA-OCR outperforms the baselines and achieves state-of-the-art performance in the Chinese text recognition benchmark. Additionally, we construct an OCR pipeline with OFA-OCR, and we demonstrate that it can achieve competitive performance with the product-level API. The code (https://github.com/OFA-Sys/OFA) and demo (https://modelscope.cn/studios/damo/ofa_ocr_pipeline/summary) are publicly available.

cs.CV↗

arXiv2022

Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance

Weakly supervised video anomaly detection (WSVAD) is a challenging task since only video-level labels are available for training. In previous studies, the discriminative power of the learned features is not strong enough, and the data imbalance resulting from the mini-batch training strategy is ignored. To address these two issues, we propose a novel WSVAD method based on cross-batch clustering guidance. To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data. Meanwhile, we design a cross-batch learning strategy by introducing clustering results from previous mini-batches to reduce the impact of data imbalance. In addition, we propose to generate more accurate segment-level anomaly scores based on batch clustering guidance further improving the performance of WSVAD. Extensive experiments on two public datasets demonstrate the effectiveness of our approach.

cs.CV↗

arXiv2022

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.

physics.ins-det↗

arXiv2022

OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models

Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alternative to approaching general-purpose AI, existing generalist models are still at an early stage, where modality and task coverage is limited. To empower multi-modal task-scaling and speed up this line of research, we release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction. At the core of OFASys is the idea of decoupling multi-modal task representations from the underlying model implementations. In OFASys, a task involving multiple modalities can be defined declaratively even with just a single line of code. The system automatically generates task plans from such instructions for training and inference. It also facilitates multi-task training for diverse multi-modal workloads. As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data. The single OFA+ model achieves 95% performance in average with only 16% parameters of 15 task-finetuned models, showcasing the performance reliability of multi-modal task-scaling provided by OFASys. Available at https://github.com/OFA-Sys/OFASys

cs.CV↗

arXiv2022

Generalizable Person Re-Identification via Viewpoint Alignment and Fusion

In the current person Re-identification (ReID) methods, most domain generalization works focus on dealing with style differences between domains while largely ignoring unpredictable camera view change, which we identify as another major factor leading to a poor generalization of ReID methods. To tackle the viewpoint change, this work proposes to use a 3D dense pose estimation model and a texture mapping module to map the pedestrian images to canonical view images. Due to the imperfection of the texture mapping module, the canonical view images may lose the discriminative detail clues from the original images, and thus directly using them for ReID will inevitably result in poor performance. To handle this issue, we propose to fuse the original image and canonical view image via a transformer-based module. The key insight of this design is that the cross-attention mechanism in the transformer could be an ideal solution to align the discriminative texture clues from the original image with the canonical view image, which could compensate for the low-quality texture information of the canonical view image. Through extensive experiments, we show that our method can lead to superior performance over the existing approaches in various evaluation settings.

cs.CV↗

arXiv2022

NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies

We present a novel method, called NeuralUDF, for reconstructing surfaces with arbitrary topologies from 2D images via volume rendering. Recent advances in neural rendering based reconstruction have achieved compelling results. However, these methods are limited to objects with closed surfaces since they adopt Signed Distance Function (SDF) as surface representation which requires the target shape to be divided into inside and outside. In this paper, we propose to represent surfaces as the Unsigned Distance Function (UDF) and develop a new volume rendering scheme to learn the neural UDF representation. Specifically, a new density function that correlates the property of UDF with the volume rendering scheme is introduced for robust optimization of the UDF fields. Experiments on the DTU and DeepFashion3D datasets show that our method not only enables high-quality reconstruction of non-closed shapes with complex typologies, but also achieves comparable performance to the SDF based methods on the reconstruction of closed surfaces.

cs.CV↗

arXiv2022

DVGG: Deep Variational Grasp Generation for Dextrous Manipulation

Grasping with anthropomorphic robotic hands involves much more hand-object interactions compared to parallel-jaw grippers. Modeling hand-object interactions is essential to the study of multi-finger hand dextrous manipulation. This work presents DVGG, an efficient grasp generation network that takes single-view observation as input and predicts high-quality grasp configurations for unknown objects. In general, our generative model consists of three components: 1) Point cloud completion for the target object based on the partial observation; 2) Diverse sets of grasps generation given the complete point cloud; 3) Iterative grasp pose refinement for physically plausible grasp optimization. To train our model, we build a large-scale grasping dataset that contains about 300 common object models with 1.5M annotated grasps in simulation. Experiments in simulation show that our model can predict robust grasp poses with a wide variety and high success rate. Real robot platform experiments demonstrate that the model trained on our dataset performs well in the real world. Remarkably, our method achieves a grasp success rate of 70.7\% for novel objects in the real robot platform, which is a significant improvement over the baseline methods.

cs.RO↗