pwc-archive/papers-with-abstracts huggingface dataset:
A New Definition of Demand Response in the Distributed Energy Resource Era
Authors: Johanna L. Mathieu, Gregor Verbič, Thomas Morstyn, Mads Almassalkhi, Kyri Baker, Julio Braslavsky, Kenneth Bruninx, Yury Dvorkin, Gregory S. Ledva, Nariman Mahdavi, Hrvoje Pandžić, Alessandra Parisio, Vedran Perić
Date: 2024-10-24 00:00:00

Demand response is a concept that has been around since the very first electric power systems. However, we have seen an explosion of research on demand response and demand-side technologies in the past 30 years, coinciding with the shift towards liberalized/deregulated electricity markets and efforts to decarbonize the power sector. Now we are also seeing a shift towards more distributed/decentralized electric systems; we have entered the era of "distributed energy resources," which require new grid management, operational, and control strategies. Given this paradigm shift, we argue that the concept of demand response needs to be revisited, and more carefully/consistently defined to enable us to better utilize this massive resource for economic, technical, environmental, and societal aims. In this paper, we survey existing demand response definitions, highlight their shortcomings, propose a new definition, and describe how this new definition enables us to more effectively harness the value of demand response in modern power systems. We conclude with a demand response research agenda informed by a discussion of demand response barriers and enablers.
Active Target Tracking Using Bearing-only Measurements With Gaussian Process Learning
Authors: Yingbo Fu, Ziwen Yang, Shanying Zhu, Yi Guo, Cailian Chen
Date: 2024-10-24 00:00:00

This paper studies the tracking problem of a target with the partially unknown motion model by an active agent with bearing-only measurements using Gaussian process learning. To address this problem, a learning-planning-control framework is proposed. First, to learn and predict the target motion under mild assumptions, a Gaussian-process-based scheme is proposed, and a probabilistic uniform prediction error bound can be rigorously proved. Second, by analyzing the data dependence of the posterior covariance, we obtain an optimal relative trajectory to achieve efficient sampling. Third, to realize efficient learning, a controller to track the planned path is proposed based on the learned target motion, which can provide guaranteed tracking performance. Theoretical analysis is conducted to prove the the given probabilistic error bounds. Numerical examples and comparison with other typical methods verify the feasibility and superior performance of our proposed framework.
LLM-Aided Efficient Hardware Design Automation
Authors: Kangwei Xu, Ruidi Qiu, Zhuorui Zhao, Grace Li Zhang, Ulf Schlichtmann, Bing Li
Date: 2024-10-24 00:00:00
Tasks: Code Repair, Logical Reasoning, Response Generation

With the rapidly increasing complexity of modern chips, hardware engineers are required to invest more effort in tasks such as circuit design, verification, and physical implementation. These workflows often involve continuous modifications, which are labor-intensive and prone to errors. Therefore, there is an increasing need for more efficient and cost-effective Electronic Design Automation (EDA) solutions to accelerate new hardware development. Recently, large language models (LLMs) have made significant advancements in contextual understanding, logical reasoning, and response generation. Since hardware designs and intermediate scripts can be expressed in text format, it is reasonable to explore whether integrating LLMs into EDA could simplify and fully automate the entire workflow. Accordingly, this paper discusses such possibilities in several aspects, covering hardware description language (HDL) generation, code debugging, design verification, and physical implementation. Two case studies, along with their future outlook, are introduced to highlight the capabilities of LLMs in code repair and testbench generation. Finally, future directions and challenges are highlighted to further explore the potential of LLMs in shaping the next-generation EDA
Constraint-adaptive MPC for large-scale systems: Satisfying state constraints without imposing them
Authors: S. A. N. Nouwens, B. de Jager, M. M. Paulides, W. P. M. H. Heemels
Date: 2024-10-24 00:00:00
Tasks: Model Predictive Control

Model Predictive Control (MPC) is a successful control methodology, which is applied to increasingly complex systems. However, real-time feasibility of MPC can be challenging for complex systems, certainly when an (extremely) large number of constraints have to be adhered to. For such scenarios with a large number of state constraints, this paper proposes two novel MPC schemes for general nonlinear systems, which we call constraint-adaptive MPC. These novel schemes dynamically select at each time step a (varying) set of constraints that are included in the on-line optimization problem. Carefully selecting the included constraints can significantly reduce, as we will demonstrate, the computational complexity with often only a slight impact on the closed-loop performance. Although not all (state) constraints are imposed in the on-line optimization, the schemes still guarantee recursive feasibility and constraint satisfaction. A numerical case study illustrates the proposed MPC schemes and demonstrates the achieved computation time improvements exceeding two orders of magnitude without loss of performance.
Bearing-Only Solution for Fermat-Weber Location Problem: Generalized Algorithms
Authors: Nhat-Minh Le-Phan, Phuoc Doan Nguyen, Hyo-Sung Ahn, Minh Hoang Trinh
Date: 2024-10-24 00:00:00

This paper presents novel algorithms for the Fermat-Weber Location Problem, guiding an autonomous agent to the point that minimizes the weighted sum of Euclidean distances to some beacons using only bearing measurements. The existing results address only the simple scenario where the beacons are stationary and the agent is modeled by a single integrator. In this paper, we propose a number of bearing-only algorithms that let the agent, which can be modeled as either a single-integrator or a double-integrator, follow the Fermat-Weber point of a group of stationary or moving beacons. The theoretical results are rigorously proven using Lyapunov theory and supported with simulation examples.
Sensing-Communication-Computing-Control Closed-Loop Optimization for 6G Unmanned Robotic Systems
Authors: Xinran Fang, Chengleyang Lei, Wei Feng, Yunfei Chen, Ming Xiao, Ning Ge, Chengxiang Wang
Date: 2024-10-24 00:00:00
Tasks: ARC, CPU

Rapid advancements in field robots have brought a new kind of cyber physical system (CPS)--unmanned robotic system--under the spotlight. In the upcoming sixth-generation (6G) era, these systems hold great potential to replace humans in hazardous tasks. This paper investigates an unmanned robotic system comprising a multi-functional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that transfers and processes information. During the task execution, the EIH gathers sensing data, calculates control commands, and transmits commands to actuators--leading to reflex-arc-like sensing-communication-computing-control ($\mathbf{SC}^3$) loops. Unlike existing studies that design $\mathbf{SC}^3$ loop components separately, we take each $\mathbf{SC}^3$ loop as an integrated structure and propose a goal-oriented closed-loop optimization scheme. This scheme jointly optimizes uplink and downlink (UL&DL) communication and computing within and across the $\mathbf{SC}^3$ loops to minimize the total linear quadratic regulator (LQR) cost. We derive optimal closed-form solutions for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency availability, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate that the proposed scheme achieves a two-tier task-level balance within and across $\mathbf{SC}^3$ loops.
Structured Connectivity for 6G Reflex Arc: Task-Oriented Virtual User and New Uplink-Downlink Tradeoff
Authors: Xinran Fang, Chengleyang Lei, Wei Feng, Yunfei Chen, Ning Ge, Shi Jin
Date: 2024-10-24 00:00:00
Tasks: ARC, CPU

To accommodate the evolving demands of unmanned operations, the future sixth-generation (6G) network will support not only communication links but also sensing-communication-computing-control ($\mathbf{SC}^3$) loops. In each $\mathbf{SC}^3$ cycle, the sensor uploads sensing data to the computing center, and the computing center calculates the control command and sends it to the actuator to take action. To maintain the task-level connections between the sensor-computing center link and the computing center-actuator link, we propose to treat the sensor and actuator as a virtual user. In this way, the two communication links of the $\mathbf{SC}^3$ loop become the uplink and downlink (UL&DL) of the virtual user. Based on the virtual user, we propose a task-oriented UL&DL optimization scheme. This scheme jointly optimizes UL&DL transmit power, time, bandwidth, and CPU frequency to minimize the control linear quadratic regulator (LQR) cost. We decouple the complex problem into a convex UL&DL bandwidth allocation problem with the closed-form solution for the optimal time allocation. Simulation results demonstrate that the proposed scheme achieves a task-level balance between the UL&DL, surpassing conventional communication schemes that optimize each link separately.
Experimental Validation of a 3GPP Compliant 5G-Based Positioning System
Authors: Sarik Dhungel, Gaurav Duggal, Dara Ron, Nishith Tripathi, R. Michael Buehrer, Jeffrey H. Reed, Vijay K Shah
Date: 2024-10-23 00:00:00

The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results.
A Bayesian Approach to Low-Thrust Maneuvering Spacecraft Tracking
Authors: Enrico M. Zucchelli, Brandon A. Jones
Date: 2024-10-23 00:00:00

Bayesian estimation with an explicit transitional prior is required for a tracking algorithm to be embedded in most multi-target tracking frameworks. This paper describes a novel approach capable of tracking maneuvering spacecraft with an explicit transitional prior and in a Bayesian framework, with fewer than two observations passes per day. The algorithm samples thrust profiles according to a multivariate Laplace distribution. It is shown that multivariate Laplace distributions are particularly suited to track maneuvering spacecraft, leading to a log probability function that is almost linear with the thrust. Principles from rare event simulation theory are used to propagate the tails of the distribution. Fast propagation is enabled by multi-fidelity methods. Because of the diffuse transitional prior, a novel k-nearest neighbor-based ensemble Gaussian mixture filter is developed and used.The method allows Bayesian tracking of maneuvering spacecraft for several scenarios with fewer than two measurement passes per day, and with a mismatch between the true and expected thrust magnitude of up to a factor of 200. The validity domain and statistical significance of the method are shown by simulation through several Monte Carlo trials in different scenarios and with different filter settings.
Trajectory Optimization for Spatial Microstructure Control in Electron Beam Metal Additive Manufacturing
Authors: Mikhail Khrenov, Moon Tan, Lauren Fitzwater, Michelle Hobdari, Sneha Prabha Narra
Date: 2024-10-23 00:00:00
Tasks: GPU

Metal additive manufacturing (AM) opens the possibility for spatial control of as-fabricated microstructure and properties. However, since the solid state diffusional transformations that drive microstructure outcomes are governed by nonlinear ODEs in terms of temperature, which is itself governed by PDEs over the entire part domain, solving for the system inputs needed to achieve desired microstructure distributions has proven difficult. In this work, we present a trajectory optimization approach for spatial control of microstructure in metal AM, which we demonstrate by controlling the hardness of a low-alloy steel in electron beam powder bed fusion (EB-PBF). To this end, we present models for thermal and microstructural dynamics. Next, we use experimental data to identify the parameters of the microstructure transformation dynamics. We then pose spatial microstructure control as a finite-horizon optimal control problem. The optimal power field trajectory is computed using an augmented Lagrangian differential dynamic programming (AL-DDP) method with GPU acceleration. The resulting time-varying power fields are then realized on an EB-PBF machine through an approximation scheme. Measurements of the resultant hardness shows that the optimized power field trajectory is able to closely produce the desired hardness distribution.
KMS states of Information Flow in Directed Brain Synaptic Networks
Authors: El-kaïoum M. Moutuou, Habib Benali
Date: 2024-10-23 00:00:00
Tasks: Diversity

The brain's synaptic network, characterized by parallel connections and feedback loops, drives information flow between neurons through a large system with infinitely many degrees of freedom. This system is best modeled by the graph $C^*$-algebra of the underlying directed graph, the Toeplitz-Cuntz-Krieger algebra, which captures the diversity of potential information pathways. Coupled with the gauge action, this graph algebra defines an {\em algebraic quantum system}, and here we demonstrate that its thermodynamic properties provide a natural framework for describing the dynamic mappings of information flow within the network. Specifically, we show that the KMS states of this system yield global statistical measures of neuronal interactions, with computational illustrations based on the {\em C. elegans} synaptic network.
Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective
Authors: Yuzhi Xu, Haowei Ni, Qinhui Gao, Chia-Hua Chang, Yanran Huo, Fanyu Zhao, Shiyu Hu, Wei Xia, Yike Zhang, Radu Grovu, Min He, John. Z. H. Zhang, Yuanqing Wang
Date: 2024-10-08 00:00:00

Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to create valuable new molecular entities, from small molecule therapeutics to protein biologics. In the small data regime, physics-based approaches model the interaction between the molecule being designed and proteins of key physiological functions, providing structural insights into the mechanism. When abundant data has been collected, a quantitative structure-activity relationship (QSAR) can be more directly constructed from experimental data, from which machine learning can distill key insights to guide the design of the next round of experiment design. Machine learning methodologies can also facilitate physical modeling, from improving the accuracy of force fields and extending them to unseen chemical spaces, to more directly enhancing the sampling on the conformational spaces. We argue that these techniques are mature enough to be applied to not just extend the longevity of life, but the beauty it manifests. In this perspective, we review the current frontiers in the research \& development of skin care products, as well as the statistical and physical toolbox applicable to addressing the challenges in this industry. Feasible interdisciplinary research projects are proposed to harness the power of machine learning tools to design innovative, effective, and inexpensive skin care products.
Inference on High Dimensional Selective Labeling Models
Authors: Shakeeb Khan, Elie Tamer, Qingsong Yao
Date: 2024-10-24 00:00:00
Tasks: Selection bias

A class of simultaneous equation models arise in the many domains where observed binary outcomes are themselves a consequence of the existing choices of of one of the agents in the model. These models are gaining increasing interest in the computer science and machine learning literatures where they refer the potentially endogenous sample selection as the {\em selective labels} problem. Empirical settings for such models arise in fields as diverse as criminal justice, health care, and insurance. For important recent work in this area, see for example Lakkaruju et al. (2017), Kleinberg et al. (2018), and Coston et al.(2021) where the authors focus on judicial bail decisions, and where one observes the outcome of whether a defendant filed to return for their court appearance only if the judge in the case decides to release the defendant on bail. Identifying and estimating such models can be computationally challenging for two reasons. One is the nonconcavity of the bivariate likelihood function, and the other is the large number of covariates in each equation. Despite these challenges, in this paper we propose a novel distribution free estimation procedure that is computationally friendly in many covariates settings. The new method combines the semiparametric batched gradient descent algorithm introduced in Khan et al.(2023) with a novel sorting algorithms incorporated to control for selection bias. Asymptotic properties of the new procedure are established under increasing dimension conditions in both equations, and its finite sample properties are explored through a simulation study and an application using judicial bail data.
Partially Identified Rankings from Pairwise Interactions
Authors: Federico Crippa, Danil Fedchenko
Date: 2024-10-23 00:00:00
Tasks: valid

This paper considers the problem of ranking objects based on their latent merits using data from pairwise interactions. Existing approaches rely on the restrictive assumption that all the interactions are either observed or missed randomly. We investigate what can be inferred about rankings when this assumption is relaxed. First, we demonstrate that in parametric models, such as the popular Bradley-Terry-Luce model, rankings are point-identified if and only if the tournament graph is connected. Second, we show that in nonparametric models based on strong stochastic transitivity, rankings in a connected tournament are only partially identified. Finally, we propose two statistical tests to determine whether a ranking belongs to the identified set. One test is valid in finite samples but computationally intensive, while the other is easy to implement and valid asymptotically. We illustrate our procedure using Brazilian employer-employee data to test whether male and female workers rank firms differently when making job transitions.
On the Existence of One-Sided Representations for the Generalised Dynamic Factor Model
Authors: Philipp Gersing
Date: 2024-10-23 00:00:00

We consider the Generalised Dynamic Factor Model (GDFM) under the assumption that the dynamic common component is purely non-deterministic. We show that, under thisassumption, the common shocks - and consequently, the dynamic common component - can be represented in terms of current and past observed variables. This generalises existing results on the so-called "One-Sidedness Problem" of the GDFM.
Periodic portfolio selection with quasi-hyperbolic discounting
Authors: Yushi Hamaguchi, Alex S. L. Tse
Date: 2024-10-23 00:00:00

We introduce an infinite-horizon, continuous-time portfolio selection problem faced by an agent with periodic S-shaped preference and present bias. The inclusion of a quasi-hyperbolic discount function leads to time-inconsistency and we characterize the optimal portfolio for a pre-committing, naive and sophisticated agent respectively. In the more theoretically challenging problem with a sophisticated agent, the time-consistent planning strategy can be formulated as an equilibrium to a static mean field game. Interestingly, present bias and naivety do not necessarily result in less desirable risk taking behaviors, while agent's sophistication may lead to excessive leverage (underinvestement) in the bad (good) states of the world.
A minimal model of money creation under regulatory constraints
Authors: Victor Le Coz, Michael Benzaquen, Damien Challet
Date: 2024-10-22 00:00:00

We propose a minimal model of the secured interbank network able to shed light on recent money markets puzzles. We find that excess liquidity emerges due to the interactions between the reserves and liquidity ratio constraints; the appearance of evergreen repurchase agreements and collateral re-use emerges as a simple answer to banks' counterparty risk and liquidity ratio regulation. In line with prevailing theories, re-use increases with collateral scarcity. In our agent-based model, banks create money endogenously to meet the funding requests of economic agents. The latter generate payment shocks to the banking system by reallocating their deposits. Banks absorbs these shocks thanks to repurchase agreements, while respecting reserves, liquidity, and leverage constraints. The resulting network is denser and more robust to stress scenarios than an unsecured one; in addition, the stable bank trading relationships network exhibits a core-periphery structure. Finally, we show how this model can be used as a tool for stress testing and monetary policy design.
Dynamic Investment-Driven Insurance Pricing and Optimal Regulation
Authors: Bingzheng Chen, Zongxia Liang, Shunzhi Pang
Date: 2024-10-24 00:00:00

This paper analyzes the equilibrium of insurance market in a dynamic setting, focusing on the interaction between insurers' underwriting and investment strategies. Three possible equilibrium outcomes are identified: a positive insurance market, a zero insurance market, and market failure. Our findings reveal why insurers may rationally accept underwriting losses by setting a negative safety loading while relying on investment profits, particularly when there is a negative correlation between insurance gains and financial returns. Additionally, we explore the impact of regulatory frictions, showing that while imposing a cost on investment can enhance social welfare under certain conditions, it may not always be necessary.