arXiv subjects

Rui Yuan

Publications and source records attributed to Rui Yuan.

32 records · Page 2Linked to original sources

arXiv2024

Probing Neutral Triple Gauge Couplings via $\boldsymbol{Zγ\,(\ell^+\ell^-γ)}$ Production at $\boldsymbol{e^+e^-}$ Colliders

Neutral triple gauge couplings (nTGCs) are absent in the Standard Model (SM) and at the dimension-6 level in the Standard Model Effective Field Theory (SMEFT), arising first from dimension-8 operators. As such, they provide a unique window for probing new physics beyond the SM. These dimension-8 operators can be mapped to nTGC form factors whose structure is consistent with the spontaneously-broken electroweak gauge symmetry of the SM. In this work, we study the probes of nTGCs in the reaction $e^+e^-\to Zγ$ with $Z\to\ell^+\ell^-\,(\ell =e,μ)$ at an $e^+e^-$ collider. We perform a detector-level simulation and analysis of this reaction at the Circular Electron Positron Collider (CEPC) with collision energy $\sqrt{s} = 240$ GeV and an integrated luminosity of 20 ab$^{-1}$. We present the sensitivity limits on probing the new physics scales of dimension-8 nTGC operators via measurements of the corresponding nTGC form factors.

hep-ph↗

arXiv2024

Design and test for the CEPC muon subdetector based on extruded scintillator and SiPM

A combination of scintillator, wavelength shifting (WLS) fiber, and silicon photomultiplier (SiPM) shows an excellent performance in the `$K_{L}$ and $μ$ detector (KLM)' of the Belle II experiment. In this study, we present the R&D efforts for a similar detection technology utilizing a new scintillator and SiPM. This technology can be applied to a muon detector for the proposed CEPC experiment. The R&D encompasses the investigation of the performance of a new 150 cm-long scintillator, the NDL SiPM with a sensitive surface of $\times$ 3 mm, or the Hamamatsu MPPC with a sensitive surface of 1.3 mm $\times$ 1.3 mm. Additionally, it includes the construction of a detector strip and the methods employed to achieve excellent light collection. Cosmic ray tests reveal efficient photon collections by NDL SiPM or MPPC, with efficiencies well above 90% using a threshold of 8 p.e.. The time resolutions for hits at the far end of a scintillator strip are better than 1.7 ns. The observed performance lays the foundation for advancing R&D including prototype modules aiming for reference Technical Design Report of CEPC detector recently.

physics.ins-det↗

arXiv2024

Enhancing Policy Gradient with the Polyak Step-Size Adaption

Policy gradient is a widely utilized and foundational algorithm in the field of reinforcement learning (RL). Renowned for its convergence guarantees and stability compared to other RL algorithms, its practical application is often hindered by sensitivity to hyper-parameters, particularly the step-size. In this paper, we introduce the integration of the Polyak step-size in RL, which automatically adjusts the step-size without prior knowledge. To adapt this method to RL settings, we address several issues, including unknown f* in the Polyak step-size. Additionally, we showcase the performance of the Polyak step-size in RL through experiments, demonstrating faster convergence and the attainment of more stable policies.

cs.LG↗

arXiv2024

Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes

Demand response (DR) plays a critical role in ensuring efficient electricity consumption and optimal use of network assets. Yet, existing DR models often overlook a crucial element, the irrational behaviour of electricity end users. In this work, we propose a price-responsive model that incorporates key aspects of end-user irrationality, specifically loss aversion, time inconsistency, and bounded rationality. To this end, we first develop a framework that uses Multiple Seasonal-Trend decomposition using Loess (MSTL) and non-stationary Gaussian processes to model the randomness in the electricity consumption by residential consumers. The impact of this model is then evaluated through a community battery storage (CBS) business model. Additionally, we apply a chance-constrained optimisation model for CBS operation that deals with the unpredictability of the end-user irrationality. Our simulations using real-world data show that the proposed DR model provides a more realistic estimate of end-user price-responsive behaviour when considering irrationality. Compared to a deterministic model that cannot fully take into account the irrational behaviour of end users, the chance-constrained CBS operation model yields an additional 19% revenue. Lastly, the business model reduces the electricity costs of solar end users by 11%.

eess.SY↗

arXiv2024

A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence

Modern policy optimization methods in reinforcement learning, such as TRPO and PPO, owe their success to the use of parameterized policies. However, while theoretical guarantees have been established for this class of algorithms, especially in the tabular setting, the use of general parameterization schemes remains mostly unjustified. In this work, we introduce a novel framework for policy optimization based on mirror descent that naturally accommodates general parameterizations. The policy class induced by our scheme recovers known classes, e.g., softmax, and generates new ones depending on the choice of mirror map. Using our framework, we obtain the first result that guarantees linear convergence for a policy-gradient-based method involving general parameterization. To demonstrate the ability of our framework to accommodate general parameterization schemes, we provide its sample complexity when using shallow neural networks, show that it represents an improvement upon the previous best results, and empirically validate the effectiveness of our theoretical claims on classic control tasks.

stat.ML↗

arXiv2024

Dynamic and Memory-efficient Shape Based Methodologies for User Type Identification in Smart Grid Applications

Detecting behind-the-meter (BTM) equipment and major appliances at the residential level and tracking their changes in real time is important for aggregators and traditional electricity utilities. In our previous work, we developed a systematic solution called IRMAC to identify residential users' BTM equipment and applications from their imported energy data. As a part of IRMAC, a Similarity Profile (SP) was proposed for dimensionality reduction and extracting self-join similarity from the end users' daily electricity usage data. The proposed SP calculation, however, was computationally expensive and required a significant amount of memory at the user's end. To realise the benefits of edge computing, in this paper, we propose and assess three computationally-efficient updating solutions, namely additive, fixed memory, and codebook-based updating methods. Extensive simulation studies are carried out using real PV users' data to evaluate the performance of the proposed methods in identifying PV users, tracking changes in real time, and examining memory usage. We found that the Codebook-based solution reduces more than 30\% of the required memory without compromising the performance of extracting users' features. When the end users' data storage and computation speed are concerned, the fixed-memory method outperforms the others. In terms of tracking the changes, different variations of the fixed-memory method show various inertia levels, making them suitable for different applications.

eess.SY↗

arXiv2023

Nash Equilibrium Seeking in Networked Games with Intermittent Communication

This paper investigates the Nash equilibrium seeking problems for networked games with intermittent communication, where each player is capable of communicating with other players intermittently over a strongly connected and directed graph. Noticing that the players are not directly and continuously available for the actions of other players, this paper proposed an intermittent communication strategy. Compared with previous literature on intermittent communication, the players considered in this paper communicate with other players without quasi-periodic constraint. Instead, the players are supposed to estimate the actions of the other players with completely aperiodically intermittent communication. The distributions of communication time and silent time are characterized newly according to the concept of average communication ratio. And each player estimates other players' actions only during communication time. Finally, the validity of the theoretical results is demonstrated by two simulation examples.

math.OC↗

arXiv2023

Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies

We consider infinite-horizon discounted Markov decision processes and study the convergence rates of the natural policy gradient (NPG) and the Q-NPG methods with the log-linear policy class. Using the compatible function approximation framework, both methods with log-linear policies can be written as inexact versions of the policy mirror descent (PMD) method. We show that both methods attain linear convergence rates and $\tilde{\mathcal{O}}(1/ε^2)$ sample complexities using a simple, non-adaptive geometrically increasing step size, without resorting to entropy or other strongly convex regularization. Lastly, as a byproduct, we obtain sublinear convergence rates for both methods with arbitrary constant step size.

cs.LG↗

arXiv2022

A general sample complexity analysis of vanilla policy gradient

We adapt recent tools developed for the analysis of Stochastic Gradient Descent (SGD) in non-convex optimization to obtain convergence and sample complexity guarantees for the vanilla policy gradient (PG). Our only assumptions are that the expected return is smooth w.r.t. the policy parameters, that its $H$-step truncated gradient is close to the exact gradient, and a certain ABC assumption. This assumption requires the second moment of the estimated gradient to be bounded by $A\geq 0$ times the suboptimality gap, $B \geq 0$ times the norm of the full batch gradient and an additive constant $C \geq 0$, or any combination of aforementioned. We show that the ABC assumption is more general than the commonly used assumptions on the policy space to prove convergence to a stationary point. We provide a single convergence theorem that recovers the $\widetilde{\mathcal{O}}(ε^{-4})$ sample complexity of PG to a stationary point. Our results also affords greater flexibility in the choice of hyper parameters such as the step size and the batch size $m$, including the single trajectory case (i.e., $m=1$). When an additional relaxed weak gradient domination assumption is available, we establish a novel global optimum convergence theory of PG with $\widetilde{\mathcal{O}}(ε^{-3})$ sample complexity. We then instantiate our theorems in different settings, where we both recover existing results and obtain improved sample complexity, e.g., $\widetilde{\mathcal{O}}(ε^{-3})$ sample complexity for the convergence to the global optimum for Fisher-non-degenerated parametrized policies.

cs.LG↗

arXiv2022

IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications

Modern power systems are experiencing the challenge of high uncertainty with the increasing penetration of renewable energy resources and the electrification of heating systems. In this paradigm shift, understanding electricity users' demand is of utmost value to retailers, aggregators, and policymakers. However, behind-the-meter (BTM) equipment and appliances at the household level are unknown to the other stakeholders mainly due to privacy concerns and tight regulations. In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters. To solve this problem with an interpretable, fast, secure, and maintainable solution, we propose an integrated method called Interpretable Refined Motifs And binary Classification (IRMAC). The proposed method comprises a novel shape-based pattern extraction technique, called Refined Motif (RM) discovery, and a single-neuron classifier. The first part extracts a sub-pattern from the long time series considering the frequency of occurrences, average dissimilarity, and time dynamics while emphasising specific times with annotated distances. The second part identifies users' types with linear complexity while preserving the transparency of the algorithms. With the real data from Australia and Denmark, the proposed method is tested and verified in identifying PV owners and electrical heating system users.

cs.LG↗

arXiv2022

Optimal activity and battery scheduling algorithm using load and solar generation forecasts

Energy usage optimal scheduling has attracted great attention in the power system community, where various methodologies have been proposed. However, in real-world applications, the optimal scheduling problems require reliable energy forecasting, which is scarcely discussed as a joint solution to the scheduling problem. The 5\textsuperscript{th} IEEE Computational Intelligence Society (IEEE-CIS) competition raised a practical problem of decreasing the electricity bill by scheduling building activities, where forecasting the solar energy generation and building consumption is a necessity. To solve this problem, we propose a technical sequence for tackling the solar PV and demand forecast and optimal scheduling problems, where solar generation prediction methods and an optimal university lectures scheduling algorithm are proposed.

cs.LG↗

arXiv2022

Sketched Newton-Raphson

We propose a new globally convergent stochastic second order method. Our starting point is the development of a new Sketched Newton-Raphson (SNR) method for solving large scale nonlinear equations of the form $F(x)=0$ with $F:\mathbb{R}^p \rightarrow \mathbb{R}^m$. We then show how to design several stochastic second order optimization methods by re-writing the optimization problem of interest as a system of nonlinear equations and applying SNR. For instance, by applying SNR to find a stationary point of a generalized linear model (GLM), we derive completely new and scalable stochastic second order methods. We show that the resulting method is very competitive as compared to state-of-the-art variance reduced methods. Furthermore, using a variable splitting trick, we also show that the Stochastic Newton method (SNM) is a special case of SNR, and use this connection to establish the first global convergence theory of SNM. We establish the global convergence of SNR by showing that it is a variant of the stochastic gradient descent (SGD) method, and then leveraging proof techniques of SGD. As a special case, our theory also provides a new global convergence theory for the original Newton-Raphson method under strictly weaker assumptions as compared to the classic monotone convergence theory.

math.NA↗

arXiv2022

SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums

We present a principled approach for designing stochastic Newton methods for solving finite sum optimization problems. Our approach has two steps. First, we re-write the stationarity conditions as a system of nonlinear equations that associates each data point to a new row. Second, we apply a Subsampled Newton Raphson method to solve this system of nonlinear equations. Using our approach, we develop a new Stochastic Average Newton (SAN) method, which is incremental by design, in that it requires only a single data point per iteration. It is also cheap to implement when solving regularized generalized linear models, with a cost per iteration of the order of the number of the parameters. We show through extensive numerical experiments that SAN requires no knowledge about the problem, neither parameter tuning, while remaining competitive as compared to classical variance reduced gradient methods (e.g. SAG and SVRG), incremental Newton and quasi-Newton methods (e.g. SNM, IQN).

math.OC↗

arXiv2021

Optimal activity and battery scheduling algorithm using load and solar generation forecast

In this report, we provide a technical sequence on tackling the solar PV and demand forecast as well as optimal scheduling problem proposed by the IEEE-CIS 3rd technical challenge on predict + optimize for activity and battery scheduling. Using the historical data provided by the organizers, a simple pre-processing approach with a rolling window was used to detect and replace invalid data points. Upon filling the missing values, advanced time-series forecasting techniques, namely tree-based methods and refined motif discovery, were employed to predict the baseload consumption on six different buildings together with the power production on their associated solar PV panels. An optimization problem is then formulated to use the predicted values and the wholesale electricity prices to create a timetable for a set of activities, including the scheduling of lecture theatres and battery charging and discharging operation, for one month ahead. The valley-filling optimization was done across all the buildings with the objective of minimizing the total energy cost and achieving net-zero imported power from the grid.

eess.SY↗