pwc-archive/papers-with-abstracts huggingface dataset:
CSA-Trans: Code Structure Aware Transformer for AST
Authors: Saeyoon Oh, Shin Yoo
Date: 2024-04-07 00:00:00
Tasks: Code Summarization, Stochastic Block Model

When applying the Transformer architecture to source code, designing a good self-attention mechanism is critical as it affects how node relationship is extracted from the Abstract Syntax Trees (ASTs) of the source code. We present Code Structure Aware Transformer (CSA-Trans), which uses Code Structure Embedder (CSE) to generate specific PE for each node in AST. CSE generates node Positional Encoding (PE) using disentangled attention. To further extend the self-attention capability, we adopt Stochastic Block Model (SBM) attention. Our evaluation shows that our PE captures the relationships between AST nodes better than other graph-related PE techniques. We also show through quantitative and qualitative analysis that SBM attention is able to generate more node specific attention coefficients. We demonstrate that CSA-Trans outperforms 14 baselines in code summarization tasks for both Python and Java, while being 41.92% faster and 25.31% memory efficient in Java dataset compared to AST-Trans and SG-Trans respectively.
Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists
Authors: Joachim Baumann, Celestine Mendler-Dünner
Date: 2024-03-19 00:00:00
Tasks: Recommendation Systems

We investigate algorithmic collective action in transformer-based recommender systems. Our use case is a music streaming platform where a collective of fans aims to promote the visibility of an underrepresented artist by strategically placing one of their songs in the existing playlists they control. We introduce two easily implementable strategies to select the position at which to insert the song with the goal to boost recommendations at test time. The strategies exploit statistical properties of the learner by targeting discontinuities in the recommendations, and leveraging the long-tail nature of song distributions. We evaluate the efficacy of our strategies using a publicly available recommender system model released by a major music streaming platform. Our findings reveal that through strategic placement even small collectives (controlling less than 0.01\% of the training data) can achieve up to $40\times$ more test time recommendations than an average song with the same number of training set occurrences. Focusing on the externalities of the strategy, we find that the recommendations of other songs are largely preserved, and the newly gained recommendations are distributed across various artists. Together, our findings demonstrate how carefully designed collective action strategies can be effective while not necessarily being adversarial.
The Use of Generative Search Engines for Knowledge Work and Complex Tasks
Authors: Siddharth Suri, Scott Counts, Leijie Wang, Chacha Chen, Mengting Wan, Tara Safavi, Jennifer Neville, Chirag Shah, Ryen W. White, Reid Andersen, Georg Buscher, Sathish Manivannan, Nagu Rangan, Longqi Yang
Date: 2024-03-19 00:00:00

Until recently, search engines were the predominant method for people to access online information. The recent emergence of large language models (LLMs) has given machines new capabilities such as the ability to generate new digital artifacts like text, images, code etc., resulting in a new tool, a generative search engine, which combines the capabilities of LLMs with a traditional search engine. Through the empirical analysis of Bing Copilot (Bing Chat), one of the first publicly available generative search engines, we analyze the types and complexity of tasks that people use Bing Copilot for compared to Bing Search. Findings indicate that people use the generative search engine for more knowledge work tasks that are higher in cognitive complexity than were commonly done with a traditional search engine.
Traffic Signal Control and Speed Offset Coordination Using Q-Learning for Arterial Road Networks
Authors: Tianchen Yuan, Petros A. Ioannou
Date: 2024-04-09 00:00:00
Tasks: Q-Learning, Traffic Signal Control

Arterial traffic interacts with freeway traffic, yet the two are controlled independently. Arterial traffic signals do not take into account freeway traffic and how ramps control ingress traffic and have no control over egress traffic from the freeway. This often results in long queues in either direction that block ramps and spill over to arterial streets or freeway lanes. In this paper, we propose an adaptive arterial traffic control strategy that combines traffic signal control (TSC) and dynamic speed offset (DSO) coordination using a Q-learning algorithm for a traffic network that involves a freeway segment and adjacent arterial streets. The TSC agent computes the signal cycle length and split based on observed intersection demands and adjacent freeway off-ramp queues. The DSO agent computes the relative offset and the recommended speeds of both ways between consecutive intersections based on their physical distance, intersection queues, and signal cycles. We evaluate the performance of the proposed arterial traffic control strategy using microscopic traffic simulations of an arterial corridor with seven intersections near the I-710 freeway. The proposed QL-based control significantly outperforms a fixed-time control and MAXBAND in terms of the travel time and the number of stops under low or moderate demands. In high-demand scenarios, the travel-time benefit provided by the QL-based control is reduced as it mitigates off-ramp and intersection queues, which is a necessary trade-off in our perspective. In addition, mutual benefit is obtained by implementing freeway and arterial traffic control simultaneously.
Lunar Power Grid: Network Structure and Spontaneous Synchronization
Authors: Mark Walth, Amir Sajadi, Marc Carbone, Bri-Mathias Hodge
Date: 2024-04-09 00:00:00

Achieving stable synchronized operation in an alternating current power network is critical to the continuity and reliability of energy delivery. In this paper, we study a dynamic model for synchronization in the proposed power network which the National Aeronautic and Space Agency plans to build on the lunar surface to support continuous human presence on the Moon and a lunar economy. This network is quite remarkable in the sense that it is expected to be the first power network to operate at the unprecedented operating frequency in the $\text{kHz}$ range. The particular structure of this network allows us to derive the necessary and sufficient conditions guaranteeing the existence of a unique locally stable synchronized mode, which will provide a passive control mechanism for the system. Furthermore, we study the bifurcation process leading to the loss of synchronization when the system parameters fall outside of the stable regime. Our results have broader implications for many complex networks with parametric heterogeneity to enhance stability and resilience.
Inertia emulation contribution of Frades 2 variable speed pump-turbine to power network stability
Authors: Christophe Nicolet, Antoine Béguin, Matthieu Dreyer, Sébastien Alligné, Alexander Jung, Diogo Cordeiro, Carlos Moreira
Date: 2024-04-09 00:00:00

This paper is addressing the quantification and the comparison of pumped storage power plants, PSPP, contribution to synchronous inertia and synthetic inertia when fixed speed and variable speed motor-generators technologies are considered, respectively. Therefore, a grid stability study was conducted by means of 1D SIMSEN simulation for the 2 x 395 MW PSPP Frades 2 in Portugal with both fixed speed and variable speed technologies in case of operation connected to an infinite power network or to an islanded 4.4 GW synchronous power network.
XFLEX HYDRO demonstrators grid services assessment and Ancillary Services Matrix elaboration
Authors: Christophe Nicolet, Matthieu Dreyer, Christian Landry, Sébastien Alligné, Antoine Béguin, Yves Vaillant, Stefan Tobler, Goekhan Sari, Grégory Païs, Matteo Bianciotto, Steve Sawyer, Richard Taylor, Manuel Vaz Castro, Maria Helena Vasconcelos, Carlos Moreira
Date: 2024-04-09 00:00:00

This paper presents the methodology and key results which enabled to establish the so-called Ancillary Service Matrix (ASM) presenting the ability to deliver the different ancillary services of each of the 6 demonstrators of the XFLEX HYDRO research project combined with the applicable technologies studied in this analysis. These technologies include i) the variable speed technology with Doubly Fed Induction Machine (DFIM) or Full Size Frequency Converters (FSFC), ii) the Smart Power Plant Supervisor (SPPS) enabling to extend the operating range of the hydraulic units in turbine mode based on a better knowledge of the hydro unit wear and tear and associated costs over the full unit operating range, iii) the hydraulic short circuit (HSC) operation leading to simultaneous operation of pump and turbines of Pumped Storage Power Plants (PSPP) and iv) the Hydro-Battery-Hybrid (HBH) applied at Run-of-River demonstrator. The demonstrators considered for this study includes 4 pumped storage power plants, 1 conventional hydro storage plant and 1 run-of-the-river plant. For each demonstrator a 1D simulation model was developed and validated and was further enhanced to include the model of control system enabling to address the various ancillary services. The systematic 1D numerical simulation of ancillary service contribution of each demonstrator and related technologies enabled to quantify the magnitude of active power response to contribute to the different grid services. The results have been scored between 0 and 5 for each ancillary service, allowing to populate the Ancillary Service Matrix which is summarizing the results in a graphical and synthetic way. The analysis of the score of the Ancillary Services Matrix enables the reader to draw several key conclusions about the benefits unlocked by the implementation of these technologies which are summarized in the paper.
An Operator-Theoretic Framework to Simulate Neuromorphic Circuits
Authors: Amir Shahhosseini, Thomas Chaffey, Rodolphe Sepulchre
Date: 2024-04-09 00:00:00

Splitting algorithms are well-established in convex optimization and are designed to solve large-scale problems. Using such algorithms to simulate the behavior of nonlinear circuit networks provides scalable methods for the simulation and design of neuromorphic systems. For circuits made of linear capacitors and inductors with nonlinear resistive elements, we propose a splitting that breaks the network into its LTI lossless component and its static resistive component. This splitting has both physical and algorithmic advantages and allows for separate calculations in the time domain and in the frequency domain. To demonstrate the scalability of this approach, a network made from one hundred neurons modeled by the well-known FitzHugh-Nagumo circuit with all-to-all diffusive coupling is simulated.
A data-driven approach to UIO-based fault diagnosis
Authors: Giulio Fattore, Maria Elena Valcher
Date: 2024-04-09 00:00:00
Tasks: Fault Diagnosis, State Space Models

In this paper we propose a data-driven approach to the design of a residual generator, based on a dead-beat unknown-input observer, for linear time-invariant discrete-time state-space models, whose state equation is affected both by disturbances and by actuator faults. We first review the modelbased conditions for the existence of such a residual generator, and then prove that under suitable assumptions on the collected historical data, we are both able to determine if the problem is solvable and to identify the matrices of a possible residual generator. We propose an algorithm that, based only on the collected data (and not on the system description), is able to perform both tasks. An illustrating example and some remarks on limitations and possible extensions of the current results conclude the paper.
Model Predictive Control based Energy Management System for Home Energy Resiliency
Authors: Ninad Gaikwad, Shishir Lamichhane, Anamika Dubey
Date: 2024-04-08 00:00:00
Tasks: energy management, Management, Model Predictive Control

As the occurrence of extreme weather events is increasing so are the outages caused by them. During such unplanned outages, a house needs to be provided with an energy supply to maintain habitable conditions by maintaining thermal comfort and servicing at least critical loads. An energy system consisting of rooftop photovoltaic (PV) panels along with battery storage is an excellent carbon-free choice to provide energy resiliency to houses against extreme weather-related outages. However, to provide habitable conditions this energy system has to provide not only for the non-air-conditioning (non-AC) load demand but also for the turning on of the AC system which has a considerably higher startup power requirement as compared to its rated power. Hence, an intelligent automated decision-making controller is needed which can manage the trade-off between competing requirements. In this paper, we propose such an intelligent controller based on Model Predictive Control (MPC). We compare its performance with a Baseline controller which is unintelligent, and a Rule-Based controller which has some intelligence, based on three resiliency metrics that we have developed. We perform extensive simulations for numerous scenarios involving different energy system sizes and AC startup power requirements. Every simulation is one week long and is carried out for a single-family detached house located in Florida in the aftermath of Hurricane Irma in 2017. The simulation results show that the MPC controller performs better than the other controllers in the more energy-constrained scenarios (smaller PV-battery size, larger AC startup power requirement) in providing both thermal comfort and servicing non-AC loads in a balanced manner.
Optimal robust exact first-order differentiators with Lipschitz continuous output
Authors: Rodrigo Aldana-Lopez, Richard Seeber, Hernan Haimovich, David Gomez-Gutierrez
Date: 2024-04-08 00:00:00

The signal differentiation problem involves the development of algorithms that allow to recover a signal's derivatives from noisy measurements. This paper develops a first-order differentiator with the following combination of properties: robustness to measurement noise, exactness in the absence of noise, optimal worst-case differentiation error, and Lipschitz continuous output where the output's Lipschitz constant is a tunable parameter. This combination of advantageous properties is not shared by any existing differentiator. Both continuous-time and sample-based versions of the differentiator are developed and theoretical guarantees are established for both. The continuous-time version of the differentiator consists in a regularized and sliding-mode-filtered linear adaptive differentiator. The sample-based, implementable version is then obtained through appropriate discretization. An illustrative example is provided to highlight the features of the developed differentiator.
Enhance Low-Carbon Power System Operation via Carbon-Aware Demand Response
Authors: Xin Chen
Date: 2024-04-08 00:00:00
Tasks: Scheduling

As the electrification process advances, enormous power flexibility is becoming available on the demand side, which can be harnessed to facilitate power system decarbonization. Hence, this paper studies the carbon-aware demand response (C-DR) paradigm, where individual users aim to minimize their carbon footprints through the optimal scheduling of flexible load devices. The specific operational dynamics and constraints of deferrable loads and thermostatically controlled loads are considered, and the carbon emission flow method is employed to determine users' carbon footprints using nodal carbon intensities. Then, an optimal power dispatch model that integrates the C-DR mechanism is proposed for low-carbon power system operation, based on the carbon-aware optimal power flow (C-OPF) method. Two solution algorithms, including a centralized Karush-Kuhn-Tucker (KKT) reformulation algorithm and an iterative solution algorithm, are developed to solve the bi-level power dispatch optimization model. Numerical simulations on the IEEE New England 39-bus system demonstrate the effectiveness of the proposed methods.
Stability Enhancement of LCL-Type Grid-Following Inverters Using Capacitor Voltage Active Damping
Authors: Naser Souri, Ali Mehrizi-Sani, Kambiz Tehrani
Date: 2024-04-08 00:00:00

An LCL filter offers superior attenuation for high-frequency harmonics for three-phase grid-following inverters compared to LC and L filters. However, it also introduces an inherent resonance peak, which can lead to power quality issues or even instability of the inverter control system. Active damping (AD) is widely employed to effectively mitigate this resonance. Capacitor voltage feedback (CVF) and capacitor current feedback (CCF) are effective AD methods for LCL resonance damping. CVF is preferred due to its lower sensor requirement compared to CCF. However, a derivative term appears in the active damping loop, which introduces high-frequency noise into the system. This paper proposes a noise-immune approach by replacing the derivative term with a discrete function suitable for digital implementation. The LCL resonance can be damped effectively, resulting in enhanced stability of the inverter control system. Simulation results verify the proposed effectiveness of the method with grid inductance variation and weak grid conditions
Variable-Pitch-Propeller Mechanism Design, and Development of Heliquad for Mid-flight Flipping and Fault-Tolerant-Control
Authors: Eeshan Kulkarni, Suresh Sundaram
Date: 2024-04-08 00:00:00

This paper presents the design of Variable-Pitch-Propeller mechanism and its application on a quadcopter called Heliquad to demonstrate its unique capabilities. The input-output relationship is estimated for a generic mechanism. Various singularities and actuator sizing requirements are also analyzed. The mechanism is manufactured, and the validated input-output relationship is implemented in the controller of Heliquad. Heliquad is controlled by a unified non-switching cascaded attitude-rate controller, followed by a unique Neural-Network-based reconfigurable control allocation to approximate nonlinear relationship between the control input and actuator command. The Heliquad prototype's mid-flight flip experiment validates the controller's tracking performance in upright as well as inverted conditions. The prototype is then flown in upright condition with only three of its working actuators. To the best of the authors' knowledge, the cambered airfoil propeller-equipped Heliquad prototype demonstrates full-attitude control, including yaw-rate, on three working actuators for the first time in the literature. Finally, the utility of this novel capability is demonstrated by safe recovery and precise landing post-mid-flight actuator failure crisis. Overall, the controller tracks the references well for all the experiments, and the output of the NN-based control allocation remains bounded throughout.
Stability Mechanisms for Predictive Safety Filters
Authors: Elias Milios, Kim Peter Wabersich, Felix Berkel, Lukas Schwenkel
Date: 2024-04-08 00:00:00
Tasks: Model Predictive Control

Predictive safety filters enable the integration of potentially unsafe learning-based control approaches and humans into safety-critical systems. In addition to simple constraint satisfaction, many control problems involve additional stability requirements that may vary depending on the specific use case or environmental context. In this work, we address this problem by augmenting predictive safety filters with stability guarantees, ranging from bounded convergence to uniform asymptotic stability. The proposed framework extends well-known stability results from model predictive control (MPC) theory while supporting commonly used design techniques. As a result, straightforward extensions to dynamic trajectory tracking problems can be easily adapted, as outlined in this article. The practicality of the framework is demonstrated using an automotive advanced driver assistance scenario, involving a reference trajectory stabilization problem.
Joint Active and Passive Beamforming for IRS-Aided Wireless Energy Transfer Network Exploiting One-Bit Feedback
Authors: Taotao Ji, Meng Hua, ChunGuo Li, Yongming Huang, Luxi Yang
Date: 2024-04-08 00:00:00

To reap the active and passive beamforming gain in an intelligent reflecting surface (IRS)-aided wireless network, a typical way is to first acquire the channel state information (CSI) relying on the pilot signal, and then perform the joint beamforming design. However, it is a great challenge when the receiver can neither send pilot signals nor have complex signal processing capabilities due to its hardware limitation. To tackle this problem, we study in this paper an IRS-aided wireless energy transfer (WET) network and propose two joint beamforming design methods, namely, the channel-estimationbased method and the distributed-beamforming-based method, that require only one-bit feedback from the energy receiver (ER) to the energy transmitter (ET). Specifically, for the channelestimation-based method, according to the feedback information, the ET is able to infer the cascaded ET-IRS-ER channel by continually adjusting its transmit beamformer while applying the analytic center cutting plane method (ACCPM). Then, based on the estimated cascaded CSI, the joint beamforming design can be performed by using the existing optimization techniques. While for the distributed-beamforming-based method, we first apply the distributed beamforming algorithm to optimize the IRS reflection coefficients, which is theoretically proven to converge to a local optimum almost surely. Then, the optimal ET's transmit covariance matrix is obtained based on the effective ET-ER channel learned by applying the ACCPM only once. Numerical results demonstrate the effectiveness of our proposed one-bitfeedback-based joint beamforming design schemes while greatly reducing the requirement on the hardware complexity of the ER. In particular, the high accuracy of our IRS-involved cascaded channel estimation method exploiting one-bit feedback is also validated.
Contouring Error Bounded Control for Biaxial Switched Linear Systems
Authors: Meng Yuan, Ye Wang, Chris Manzie, Zhezhuang Xu, Tianyou Chai
Date: 2024-04-08 00:00:00
Tasks: Model Predictive Control, Position

Biaxial motion control systems are used extensively in manufacturing and printing industries. To improve throughput and reduce machine cost, lightweight materials are being proposed in structural components but may result in higher flexibility in the machine links. This flexibility is often position dependent and compromises precision of the end effector of the machine. To address the need for improved contouring accuracy in industrial machines with position-dependent structural flexibility, this paper introduces a novel contouring error-bounded control algorithm for biaxial switched linear systems. The proposed algorithm utilizes model predictive control to guarantee the satisfaction of state, input, and contouring error constraints for any admissible mode switching. In this paper, the switching signal remains unknown to the controller, although information about the minimum time the system is expected to stay in a specific mode is considered to be available. The proposed algorithm has the property of recursive feasibility and ensures the stability of the closed-loop system. The effectiveness of the proposed method is demonstrated by applying it to a high-fidelity simulation of a dual-drive industrial laser machine. The results show that the contouring error is successfully bounded within the given tolerance.