arXiv subjects

Peng Yang

Publications and source records attributed to Peng Yang.

At least 127 records · Page 7Linked to original sources

arXiv2024

Sharing Energy in Wide Area: A Two-Layer Energy Sharing Scheme for Massive Prosumers

The popularization of distributed energy resources transforms end-users from consumers into prosumers. Inspired by the sharing economy principle, energy sharing markets for prosumers are proposed to facilitate the utilization of renewable energy. This paper proposes a novel two-layer energy sharing market for massive prosumers, which can promote social efficiency by wider-area sharing. In this market, there is an upper-level wide-area market (WAM) in the distribution system and numerous lower-level local-area markets (LAMs) in communities. Prosumers in the same community share energy with each other in the LAM, which can be uncleared. The energy surplus and shortage of LAMs are cleared in the WAM. Thanks to the wide-area two-layer structure, the market outcome is near-social-optimal in large-scale systems. However, the proposed market forms a complex mathematical program with equilibrium constraints (MPEC). To solve the problem, we propose an efficient and hierarchically distributed bidding algorithm. The proposed two-layer market and bidding algorithm are verified on the IEEE 123-bus system with 11250 prosumers, which demonstrates the practicality and efficiency for large-scale markets.

cs.GT↗

arXiv2023

Holographic dissipative space-time supersolids

Driving a system out of equilibrium enriches the paradigm of spontaneous symmetry breaking, which could then take place not only in space but also in time. The interplay between temporal and spatial symmetries, as well as symmetries from other internal degrees of freedom, can give rise to novel nonequilibrium phases of matter. In this study, we investigate a driven-dissipative superfluid model using holographic methods and reveal the existence of a space-time supersolid (STS) phase which concomitantly breaks the time translation, spatial translation, and the internal U(1) symmetry. The holographic methods naturally include finite temperature effects, which enables us to explore the complex phase diagram of this model and observe a cascade of out-of-equilibrium phase transitions from the STS phase to a synchronized superfluid phase, and finally to a normal fluid phase, by increasing the temperature.

hep-th↗

arXiv2023

Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness is reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.

cs.NE↗

arXiv2023

Heating up quadruply quantized vortices: Splitting patterns and dynamical transitions

Using holographic duality, we investigate the impact of finite temperature on the instability and splitting patterns of quadruply quantized vortices, providing the first-ever analysis in this context. Through linear stability analysis, we reveal the occurrence of two consecutive dynamical transitions. At a specific low temperature, the dominant unstable mode transitions from the $2$-fold rotational symmetry mode to the $3$-fold one, followed by a transition from the $3$-fold one to the $4$-fold one at a higher temperature. As the temperature is increased, we also observe the $5$ and $6$-fold rotational symmetry unstable modes get excited successively. Employing the full non-linear numerical simulations, we further demonstrate that these two novel dynamical transitions, along with the temperature-induced instabilities for the $5$ and $6$-fold rotational symmetry modes, can be identified by examining the resulting distinct splitting patterns, which offers a promising route for the experimental verification in the cold atom gases.

cond-mat.quant-gas↗

arXiv2023

Spatial Perceptual Quality Aware Adaptive Volumetric Video Streaming

Volumetric video offers a highly immersive viewing experience, but poses challenges in ensuring quality of experience (QoE) due to its high bandwidth requirements. In this paper, we explore the effect of viewing distance introduced by six degrees of freedom (6DoF) spatial navigation on user's perceived quality. By considering human visual resolution limitations, we propose a visual acuity model that describes the relationship between the virtual viewing distance and the tolerable boundary point cloud density. The proposed model satisfies spatial visual requirements during 6DoF exploration. Additionally, it dynamically adjusts quality levels to balance perceptual quality and bandwidth consumption. Furthermore, we present a QoE model to represent user's perceived quality at different viewing distances precisely. Extensive experimental results demonstrate that, the proposed scheme can effectively improve the overall average QoE by up to 26% over real networks and user traces, compared to existing baselines.

cs.MM↗

arXiv2023

SAM: Self-adapting Mixture Prior to Dynamically Borrow Information from Historical Data in Clinical Trials

Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a non-informative prior. However, pre-specifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant obstacle to the application and acceptance of mixture priors. To address this challenge, we introduce self-adapting mixture (SAM) priors that determine the mixing weight using likelihood ratio test statistics or Bayes factor. SAM priors are data-driven and self-adapting, favoring the informative (non-informative) prior component when there is little (substantial) evidence of prior-data conflict. Consequently, SAM priors achieve dynamic information borrowing. We demonstrate that SAM priors exhibit desirable properties in both finite and large samples and achieve information-borrowing consistency. Moreover, SAM priors are easy to compute, data-driven, and calibration-free, mitigating the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in adopting prior-data conflicts effectively. We developed an R package and web application that are freely available to facilitate the use of SAM priors.

stat.ME↗

arXiv2023

Edge-Assisted On-Device Model Update for Video Analytics in Adverse Environments

While large deep neural networks excel at general video analytics tasks, the significant demand on computing capacity makes them infeasible for real-time inference on resource-constrained end cam-eras. In this paper, we propose an edge-assisted framework that continuously updates the lightweight model deployed on the end cameras to achieve accurate predictions in adverse environments. This framework consists of three modules, namely, a key frame extractor, a trigger controller, and a retraining manager. The low-cost key frame extractor obtains frames that can best represent the current environment. Those frames are then transmitted and buffered as the retraining data for model update at the edge server. Once the trigger controller detects a significant accuracy drop in the selected frames, the retraining manager outputs the optimal retraining configuration balancing the accuracy and time cost. We prototype our system on two end devices of different computing capacities with one edge server. The results demonstrate that our approach significantly improves accuracy across all tested adverse environment scenarios (up to 24%) and reduces more than 50% of the retraining time compared to existing benchmarks.

cs.MM↗

arXiv2023

Edge-Assisted Lightweight Region-of-Interest Extraction and Transmission for Vehicle Perception

To enhance on-road environmental perception for autonomous driving, accurate and real-time analytics on high-resolution video frames generated from on-board cameras be-comes crucial. In this paper, we design a lightweight object location method based on class activation mapping (CAM) to rapidly capture the region of interest (RoI) boxes that contain driving safety related objects from on-board cameras, which can not only improve the inference accuracy of vision tasks, but also reduce the amount of transmitted data. Considering the limited on-board computation resources, the RoI boxes extracted from the raw image are offloaded to the edge for further processing. Considering both the dynamics of vehicle-to-edge communications and the limited edge resources, we propose an adaptive RoI box offloading algorithm to ensure prompt and accurate inference by adjusting the down-sampling rate of each box. Extensive experimental results on four high-resolution video streams demonstrate that our approach can effectively improve the overall accuracy by up to 16% and reduce the transmission demand by up to 49%, compared with other benchmarks.

cs.MM↗

arXiv2023

End-Edge Coordinated Joint Encoding and Neural Enhancement for Low-Light Video Analytics

In this paper, we investigate video analytics in low-light environments, and propose an end-edge coordinated system with joint video encoding and enhancement. It adaptively transmits low-light videos from cameras and performs enhancement and inference tasks at the edge. Firstly, according to our observations, both encoding and enhancement for low-light videos have a significant impact on inference accuracy, which directly influences bandwidth and computation overhead. Secondly, due to the limitation of built-in computation resources, cameras perform encoding and transmitting frames to the edge. The edge executes neural enhancement to process low contrast, detail loss, and color distortion on low-light videos before inference. Finally, an adaptive controller is designed at the edge to select quantization parameters and scales of neural enhancement networks, aiming to improve the inference accuracy and meet the latency requirements. Extensive real-world experiments demon-strate that, the proposed system can achieve a better trade-off between communication and computation resources and optimize the inference accuracy.

cs.MM↗

arXiv2023

Deadline Aware Two-Timescale Resource Allocation for VR Video Streaming

In this paper, we investigate resource allocation problem in the context of multiple virtual reality (VR) video flows sharing a certain link, considering specific deadline of each video frame and the impact of different frames on video quality. Firstly, we establish a queuing delay bound estimation model, enabling link node to proactively discard frames that will exceed the deadline. Secondly, we model the importance of different frames based on viewport feature of VR video and encoding method. Accordingly, the frames of each flow are sorted. Then we formulate a problem of minimizing long-term quality loss caused by frame dropping subject to per-flow quality guarantee and bandwidth constraints. Since the frequency of frame dropping and network fluctuation are not on the same time scale, we propose a two-timescale resource allocation scheme. On the long timescale, a queuing theory based resource allocation method is proposed to satisfy quality requirement, utilizing frame queuing delay bound to obtain minimum resource demand for each flow. On the short timescale, in order to quickly fine-tune allocation results to cope with the unstable network state, we propose a low-complexity heuristic algorithm, scheduling available resources based on the importance of frames in each flow. Extensive experimental results demonstrate that the proposed scheme can efficiently improve quality and fairness of VR video flows under various network conditions.

cs.NI↗

arXiv2023

Design and Sample Size Determination for Multiple-dose Randomized Phase II Trials for Dose Optimization

The conventional more-is-better dose selection paradigm, which targets the maximum tolerated dose (MTD), is not suitable for the development of targeted therapies and immunotherapies as the efficacy of these novel therapies may not increase with the dose. The U.S. Food and Drug Administration (FDA) has launched Project Optimus "to reform the dose optimization and dose selection paradigm in oncology drug development", and recently published a draft guidance on dose optimization, which outlines various approaches to achieve this goal. One highlighted approach involves conducting a randomized phase II trial following the completion of a phase I trial, where multiple doses (typically including the MTD and one or two doses lower than the MTD) are compared to identify the optimal dose that maximizes the benefit-risk tradeoff. This paper focuses on the design of such a multiple-dose randomized trial, specifically the determination of the sample size. We propose a MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy) design that can be easily implemented with pre-calculated decision boundaries included in the protocol. We generalized the standard definitions of type I error and power to accommodate the unique characteristics of dose optimization and derived a decision rule along with an algorithm to determine the optimal sample size. Simulation studies demonstrate that the resulting MERIT design has desirable operating characteristics. To facilitate the implementation of the MERIT design, we provide software, available at www.trialdesign.org.

stat.ME↗

arXiv2023

Baby Skyrmion in two-component holographic superfluids

In the two-component Ginzburg-Landau theory of superfluidity, a pair of fractional vortices form a composite type of topological defect, usually referred to as a baby skyrmion. In this paper, we initiate the construction of such a baby skyrmion in the holographic model of two-component superfluids. As a result, two types of baby skyrmion configurations are found, where the monopole-type of one is constructed directly by solving the static equations of motion while the dipole-type of one is obtained by resorting to the time evolution method. In addition, we find that the existence of these two types of baby skyrmion depends on the inter-component coupling, reminiscent of the situation in the baby skyrmion model.

hep-th↗

arXiv2023

QoE-Driven Video Transmission: Energy-Efficient Multi-UAV Network Optimization

This paper is concerned with the issue of improving video subscribers' quality of experience (QoE) by deploying a multi-unmanned aerial vehicle (UAV) network. Different from existing works, we characterize subscribers' QoE by video bitrates, latency, and frame freezing and propose to improve their QoE by energy-efficiently and dynamically optimizing the multi-UAV network in terms of serving UAV selection, UAV trajectory, and UAV transmit power. The dynamic multi-UAV network optimization problem is formulated as a challenging sequential-decision problem with the goal of maximizing subscribers' QoE while minimizing the total network power consumption, subject to some physical resource constraints. We propose a novel network optimization algorithm to solve this challenging problem, in which a Lyapunov technique is first explored to decompose the sequential-decision problem into several repeatedly optimized sub-problems to avoid the curse of dimensionality. To solve the sub-problems, iterative and approximate optimization mechanisms with provable performance guarantees are then developed. Finally, we design extensive simulations to verify the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can effectively improve the QoE of subscribers and is 66.75\% more energy-efficient than benchmarks.

cs.NI↗

arXiv2023

Holographic dissipation prefers the Landau over the Keldysh form

Although holographic duality has been regarded as a complementary tool in helping understand the non-equilibrium dynamics of strongly coupled many-body systems, it still remains a remarkable challenge how to confront its predictions quantitatively with the real experimental scenarios. By matching the holographic vortex dynamics with the phenomenological dissipative Gross-Pitaeviskii models, we find that the holographic dissipation mechanism can be well captured by the Landau form rather than the Keldysh one, although the latter is much more widely used in numerical simulations. Our finding is expected to open up novel avenues for facilitating the quantitative test of the holographic predictions against the upcoming experimental data. Our result also provides a prime example how holographic duality can help select proper phenomenological models to describe far-from-equilibrium nonlinear dynamics beyond the hydrodynamic regime.

hep-th↗

arXiv2023

Splitting of doubly quantized vortices in holographic superfluid of finite temperature

The temperature effect on the linear instability and the splitting process of a doubly quantized vortex is studied. Using the linear perturbation theory to calculate out the quasi-normal modes of the doubly quantized vortex, we find that the imaginary part of the unstable mode increases with the temperature till some turning temperature, after which the imaginary part of the unstable mode decreases with the temperature. On the other hand, by the fully non-linear numerical simulations, we also examine the real time splitting process of the doubly quantized vortex, where not only do the split singly quantized vortex pair depart from each other, but also revolve around each other. In particular, the characteristic time scale for the splitting process is identified and its temperature dependence is found to be in good agreement with the linear instability analysis in the sense that the larger the imaginary part of the unstable mode is, the longer the splitting time is. Such a temperature effect is expected to be verified in the cold atom experiments in the near future.

hep-th↗

arXiv2023

Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions

In this paper, our goal is to design a simple learning paradigm for long-tail visual recognition, which not only improves the robustness of the feature extractor but also alleviates the bias of the classifier towards head classes while reducing the training skills and overhead. We propose an efficient one-stage training strategy for long-tailed visual recognition called Global and Local Mixture Consistency cumulative learning (GLMC). Our core ideas are twofold: (1) a global and local mixture consistency loss improves the robustness of the feature extractor. Specifically, we generate two augmented batches by the global MixUp and local CutMix from the same batch data, respectively, and then use cosine similarity to minimize the difference. (2) A cumulative head tail soft label reweighted loss mitigates the head class bias problem. We use empirical class frequencies to reweight the mixed label of the head-tail class for long-tailed data and then balance the conventional loss and the rebalanced loss with a coefficient accumulated by epochs. Our approach achieves state-of-the-art accuracy on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT datasets. Additional experiments on balanced ImageNet and CIFAR demonstrate that GLMC can significantly improve the generalization of backbones. Code is made publicly available at https://github.com/ynu-yangpeng/GLMC.

cs.CV↗

arXiv2023

Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence

Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner. Recently, FL has been interpreted within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous datasets. However, existing research simply combines MAML and FL without explicitly addressing how much benefit MAML brings to FL and how to maximize such benefit over mobile edge networks. In this paper, we quantify the benefit from two aspects: optimizing FL hyperparameters (i.e., sampled data size and the number of communication rounds) and resource allocation (i.e., transmit power) in mobile edge networks. Specifically, we formulate the MAML-based FL design as an overall learning time minimization problem, under the constraints of model accuracy and energy consumption. Facilitated by the convergence analysis of MAML-based FL, we decompose the formulated problem and then solve it using analytical solutions and the coordinate descent method. With the obtained FL hyperparameters and resource allocation, we design a MAML-based FL algorithm, called Automated Federated Learning (AutoFL), that is able to conduct fast adaptation and convergence. Extensive experimental results verify that AutoFL outperforms other benchmark algorithms regarding the learning time and convergence performance.

cs.LG↗

arXiv2023

Improving Prosody for Cross-Speaker Style Transfer by Semi-Supervised Style Extractor and Hierarchical Modeling in Speech Synthesis

Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesized speech of a target speaker's timbre. In most previous methods, the synthesized fine-grained prosody features often represent the source speaker's average style, similar to the one-to-many problem(i.e., multiple prosody variations correspond to the same text). In response to this problem, a strength-controlled semi-supervised style extractor is proposed to disentangle the style from content and timbre, improving the representation and interpretability of the global style embedding, which can alleviate the one-to-many mapping and data imbalance problems in prosody prediction. A hierarchical prosody predictor is proposed to improve prosody modeling. We find that better style transfer can be achieved by using the source speaker's prosody features that are easily predicted. Additionally, a speaker-transfer-wise cycle consistency loss is proposed to assist the model in learning unseen style-timbre combinations during the training phase. Experimental results show that the method outperforms the baseline. We provide a website with audio samples.

cs.SD↗