pwc-archive/papers-with-abstracts huggingface dataset:
Joint Infrastructure Planning and Order Assignment for On-Demand Food-Delivery Services with Coordinated Drones and Human Couriers
Authors: Yang Liu, Yitong Shang, Sen Li
Date: 2025-01-24 00:00:00

This paper investigates the optimal infrastructure planning and order assignment problem of an on-demand food-delivery platform with a mixed fleet of drones and human couriers. The platform has two delivery modes: (a) ground delivery and (b) drone-assisted delivery (i.e., air delivery). In ground delivery, couriers directly collect and transport orders from restaurants to destinations. For air delivery, the delivery process involves three legs: initially, a human courier picks up the order from the restaurant and transports it to a nearby launchpad, where personnel load the orders onto drones and replace batteries as needed. The loaded drone then transports the order from the launchpad to a kiosk, where another courier retrieves the order from the kiosk for final delivery. The platform must determine the optimal locations for launchpads and kiosks within a transportation network, and devise an order assignment strategy that allocates food-delivery orders between ground and air delivery considering the bundling probabilities of ground deliveries and the waiting times at launchpads and kiosks. We formulate the platform's problem as a mixed-integer nonlinear program and develop a novel neural network-assisted optimization method to obtain high-quality solutions. A case study in Hong Kong validates our model and algorithm, revealing that drone delivery reduces operational costs, minimizes courier fleet size, and increases order bundling opportunities. We also find that the expansion of air delivery services may entail larger delivery times due to the trade-off between the travel time savings induced by the faster air delivery and the associated detours incurred by intermodal transfer and extra waiting times at launchpads and kiosks, which crucially depends on the distance of the orders and the sequence of activating long-distance air delivery routes versus short-distance ones.
Higher-Order Meta Distribution Reliability Analysis of Wireless Networks
Authors: Mehdi Monemi, Mehdi Rasti, S. Ali Mousavi, Matti Latva-aho, Martin Haenggi
Date: 2025-01-24 00:00:00

Communication reliability, as defined by 3GPP, refers to the probability of providing a desired quality of service (QoS). This metric is typically quantified for wireless networks by averaging the QoS success indicator over spatial and temporal random variables. Recently, the meta distribution (MD) has emerged as a two-level performance analysis tool for wireless networks, offering a detailed examination of the outer level (i.e., system-level) reliability versus the inner level (i.e., link-level) reliability thresholds. Most existing studies focus on first-order spatiotemporal MD reliability analyses, and the benefits of leveraging MD reliability for applications beyond this structure remain unexplored, a gap addressed in this paper. We propose a framework for the analysis of higher-order MD reliability of wireless networks considering different levels of temporal dynamicity of random elements in the network where the MD at each layer is leveraged to be used in calculating the MD of the higher layer. We then provide two applications for this framework and provide a detailed analytical and numerical study of the higher-order MD reliability for both examples. The results demonstrate the value of the hierarchical representation of MD reliability across three domains and the impact of the inner-layers target reliabilities on the overall MD reliability measure.
Optimal Investment under Mutual Strategy Influence among Agents
Authors: Huisheng Wang, H. Vicky Zhao
Date: 2025-01-24 00:00:00

In financial markets, agents often mutually influence each other's investment strategies and adjust their strategies to align with others. However, there is limited quantitative study of agents' investment strategies in such scenarios. In this work, we formulate the optimal investment differential game problem to study the mutual influence among agents. We derive the analytical solutions for agents' optimal strategies and propose a fast algorithm to find approximate solutions with low computational complexity. We theoretically analyze the impact of mutual influence on agents' optimal strategies and terminal wealth. When the mutual influence is strong and approaches infinity, we show that agents' optimal strategies converge to the asymptotic strategy. Furthermore, in general cases, we prove that agents' optimal strategies are linear combinations of the asymptotic strategy and their rational strategies without others' influence. We validate the performance of the fast algorithm and verify the correctness of our analysis using numerical experiments. This work is crucial to comprehend mutual influence among agents and design effective mechanisms to guide their strategies in financial markets.
Learning-Augmented Online Control for Decarbonizing Water Infrastructures
Authors: Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, Shaolei Ren
Date: 2025-01-24 00:00:00

Water infrastructures are essential for drinking water supply, irrigation, fire protection, and other critical applications. However, water pumping systems, which are key to transporting water to the point of use, consume significant amounts of energy and emit millions of tons of greenhouse gases annually. With the wide deployment of digital water meters and sensors in these infrastructures, Machine Learning (ML) has the potential to optimize water supply control and reduce greenhouse gas emissions. Nevertheless, the inherent vulnerability of ML methods in terms of worst-case performance raises safety concerns when deployed in critical water infrastructures. To address this challenge, we propose a learning-augmented online control algorithm, termed LAOC, designed to dynamically schedule the activation and/or speed of water pumps. To ensure safety, we introduce a novel design of safe action sets for online control problems. By leveraging these safe action sets, LAOC can provably guarantee safety constraints while utilizing ML predictions to reduce energy and environmental costs. Our analysis reveals the tradeoff between safety requirements and average energy/environmental cost performance. Additionally, we conduct an experimental study on a building water supply system to demonstrate the empirical performance of LAOC. The results indicate that LAOC can effectively reduce environmental and energy costs while guaranteeing safety constraints.
Fabrication of Soft and Comfortable Pressure-Sensing Shoe Sole for Intuitive Monitoring of Human Quality Gaits
Authors: Muhammad Adeel, Hasnain Ali, Afaque Manzoor Soomro, Muhammad Waqas
Date: 2025-01-24 00:00:00

The study discusses the design and fabrication of flexible pressure sensors using Ecoflex/Graphene composites. The fabricated sensor is used for the application of intuitive monitoring of human quality gaits and implementation of the soft and comfortable shoe sole for rehabilitation of the patients with foot disorder is also taken into consideration. The sensor is fabricated using molding and casting technique by sandwiching the thin film Ecoflex/Graphene composites between the copper (Cu) electrodes with the dimension of 15 x 15 mm2 with high sensitivity. There are five pressure sensors integrated in the shoe sole, a sensor at the forefoot, three sensors at the midfoot and one sensor at the lower foot (heel). The behavior of the sensor is negative piezoresistive in which the resistance decreases as the pressure increases. The sensors are embedded in a soft and comfortable shoe sole and then integrated with a laptop or mobile application to monitor and analyze human gait in real-time. Furthermore, a dedicated Graphical User Interface (GUI) is designed to read the data. The pressure sensors are integrated with ESP32 microcontroller which wirelessly transmit data to the GUI and smart phones which could be further used in the intuitive monitoring, rehabilitation of the patients with foot disorder or neuromotor diseases.
Passivity-Based Robust Shape Control of a Cable-Driven Solar Sail Boom for the CABLESSail Concept
Authors: Soojeong Lee, Ryan J. Caverly
Date: 2025-01-23 00:00:00

Solar sails provide a means of propulsion using solar radiation pressure, which offers the possibility of exciting new spacecraft capabilities. However, solar sails have attitude control challenges because of the significant disturbance torques that they encounter due to imperfections in the sail and its supporting structure, as well as limited actuation capabilities. The Cable-Actuated Bio-inspired Lightweight Elastic Solar Sail (CABLESSail) concept was previously proposed to overcome these challenges by controlling the shape of the sail through cable actuation. The structural flexibility of CABLESSail introduces control challenges, which necessitate the design of a robust feedback controller for this system. The purpose of the proposed research here is to design a robust controller to ensure precise and reliable control of CABLESSail's boom. Taking into account the system dynamics and the dynamic properties of the CABLESSail concept, a passivity-based proportional-derivative (PD) controller for a single boom on the CABLESSail system is designed. To reach the nonzero desired setpoints, a feedforward input is additionally applied to the control law and a time-varying feedforward input is used instead of the constant one to effectively track a time-varying desired boom tip deflection. This control law is assessed by numerical simulations and by tests using a smaller-scale prototype of Solar Cruiser. Both the simulation and the test results show that this PD control with the time-varying feedforward input robustly controls the flexible cable-actuated solar sail.
Dissertation Machine Learning in Materials Science -- A case study in Carbon Nanotube field effect transistors
Authors: Shulin Tan
Date: 2025-01-19 00:00:00

In this thesis, I explored the use of several machine learning techniques, including neural networks, simulation-based inference, and generative flow networks, on predicting CNTFETs performance, probing the conductivity properties of CNT network, and generating CNTFETs processing information for target performance.
Evaluation of Seismic Artificial Intelligence with Uncertainty
Authors: Samuel Myren, Nidhi Parikh, Rosalyn Rael, Garrison Flynn, Dave Higdon, Emily Casleton
Date: 2025-01-15 00:00:00

Artificial intelligence has transformed the seismic community with deep learning models (DLMs) that are trained to complete specific tasks within workflows. However, there is still lack of robust evaluation frameworks for evaluating and comparing DLMs. We address this gap by designing an evaluation framework that jointly incorporates two crucial aspects: performance uncertainty and learning efficiency. To target these aspects, we meticulously construct the training, validation, and test splits using a clustering method tailored to seismic data and enact an expansive training design to segregate performance uncertainty arising from stochastic training processes and random data sampling. The framework's ability to guard against misleading declarations of model superiority is demonstrated through evaluation of PhaseNet [1], a popular seismic phase picking DLM, under 3 training approaches. Our framework helps practitioners choose the best model for their problem and set performance expectations by explicitly analyzing model performance with uncertainty at varying budgets of training data.
Estimation-theoretic analysis of lensless imaging
Authors: Leyla A. Kabuli, Nalini M. Singh, Laura Waller
Date: 2025-01-24 00:00:00
Tasks: Object

We analyze lensless imaging systems with estimation-theoretic techniques based on Fisher information. Our analysis evaluates multiple optical encoder designs on objects with varying sparsity, in the context of both Gaussian and Poisson noise models. Our simulations verify that lensless imaging system performance is object-dependent and highlight tradeoffs between encoder multiplexing and object sparsity, showing quantitatively that sparse objects tolerate higher levels of multiplexing than dense objects. Insights from our analysis promise to inform and improve optical encoder designs for lensless imaging.
Advancing Portfolio Optimization: Adaptive Minimum-Variance Portfolios and Minimum Risk Rate Frameworks
Authors: Ayush Jha, Abootaleb Shirvani, Ali Jaffri, Svetlozar T. Rachev, Frank J. Fabozzi
Date: 2025-01-27 00:00:00
Tasks: Portfolio Optimization

This study presents the Adaptive Minimum-Variance Portfolio (AMVP) framework and the Adaptive Minimum-Risk Rate (AMRR) metric, innovative tools designed to optimize portfolios dynamically in volatile and nonstationary financial markets. Unlike traditional minimum-variance approaches, the AMVP framework incorporates real-time adaptability through advanced econometric models, including ARFIMA-FIGARCH processes and non-Gaussian innovations. Empirical applications on cryptocurrency and equity markets demonstrate the proposed framework's superior performance in risk reduction and portfolio stability, particularly during periods of structural market breaks and heightened volatility. The findings highlight the practical implications of using the AMVP and AMRR methodologies to address modern investment challenges, offering actionable insights for portfolio managers navigating uncertain and rapidly changing market conditions.
Robust Quantile Factor Analysis
Authors: Songnian Chen, Junlong Feng
Date: 2025-01-27 00:00:00

We propose a factor model and an estimator of the factors and loadings that are robust to weak factors. The factors can have an arbitrarily weak influence on the mean or quantile of the outcome variable at most quantile levels; each factor only needs to have a strong impact on the outcome's quantile near one unknown quantile level. The estimator for every factor, loading, and common component is asymptotically normal at the $\sqrt{N}$ or $\sqrt{T}$ rate. It does not require the knowledge of whether the factors are weak and how weak they are. We also develop a weak-factor-robust estimator of the number of factors and a consistent selectors of factors of any desired strength of influence on the quantile or mean of the outcome variable. Monte Carlo simulations demonstrate the effectiveness of our methods.
Simple Inference on a Simplex-Valued Weight
Authors: Nathan Canen, Kyungchul Song
Date: 2025-01-26 00:00:00
Tasks: valid

In many applications, the parameter of interest involves a simplex-valued weight which is identified as a solution to an optimization problem. Examples include synthetic control methods with group-level weights and various methods of model averaging and forecast combination. The simplex constraint on the weight poses a challenge in statistical inference due to the constraint potentially binding. In this paper, we propose a simple method of constructing a confidence set for the weight and prove that the method is asymptotically uniformly valid. The procedure does not require tuning parameters or simulations to compute critical values. The confidence set accommodates both the cases of point-identification or set-identification of the weight. We illustrate the method with an empirical example.
A General Approach to Relaxing Unconfoundedness
Authors: Matthew A. Masten, Alexandre Poirier, Muyang Ren
Date: 2025-01-26 00:00:00
Tasks: Sensitivity

This paper defines a general class of relaxations of the unconfoundedness assumption. This class includes several previous approaches as special cases, including the marginal sensitivity model of Tan (2006). This class therefore allows us to precisely compare and contrast these previously disparate relaxations. We use this class to derive a variety of new identification results which can be used to assess sensitivity to unconfoundedness. In particular, the prior literature focuses on average parameters, like the average treatment effect (ATE). We move beyond averages by providing sharp bounds for a large class of parameters, including both the quantile treatment effect (QTE) and the distribution of treatment effects (DTE), results which were previously unknown even for the marginal sensitivity model.
Influence Function: Local Robustness and Efficiency
Authors: Ruonan Xu, Xiye Yang
Date: 2025-01-25 00:00:00

We propose a direct approach to calculating influence functions based on the concept of functional derivatives. The relative simplicity of our direct method is demonstrated through well-known examples. Using influence functions as a key device, we examine the connection and difference between local robustness and efficiency in both joint and sequential identification/estimation procedures. We show that the joint procedure is associated with efficiency, while the sequential procedure is linked to local robustness. Furthermore, we provide conditions that are theoretically verifiable and empirically testable on when efficient and locally robust estimation for the parameter of interest in a semiparametric model can be achieved simultaneously. In addition, we present straightforward conditions for an adaptive procedure in the presence of nuisance parameters.
Multiscale risk spillovers and external driving factors: Evidence from the global futures and spot markets of staple foods
Authors: Yun-Shi Dai, Peng-Fei Dai, Stéphane Goutte, Duc Khuong Nguyen, Wei-Xing Zhou
Date: 2025-01-25 00:00:00

Stable and efficient food markets are crucial for global food security, yet international staple food markets are increasingly exposed to complex risks, including intensified risk contagion and escalating external uncertainties. This paper systematically investigates risk spillovers in global staple food markets and explores the key determinants of these spillover effects, combining innovative decomposition-reconstruction techniques, risk connectedness analysis, and random forest models. The findings reveal that short-term components exhibit the highest volatility, with futures components generally more volatile than spot components. Further analysis identifies two main risk transmission patterns, namely cross-grain and cross-timescale transmission, and clarifies the distinct roles of each component in various net risk spillover networks. Additionally, price drivers, external uncertainties, and core supply-demand indicators significantly influence these spillover effects, with heterogeneous importance of varying factors in explaining different risk spillovers. This study provides valuable insights into the risk dynamics of staple food markets, offers evidence-based guidance for policymakers and market participants to enhance risk warning and mitigation efforts, and supports the stabilization of international food markets and the safeguarding of global food security.
Quantitative Theory of Money or Prices? A Historical, Theoretical, and Econometric Analysis
Authors: Jose Mauricio Gomez Julian
Date: 2025-01-24 00:00:00
Tasks: Ensemble Learning

This research studies the relation between money and prices and its practical implications analyzing quarterly data from United States (1959-2022), Canada (1961-2022), United Kingdom (1986-2022), and Brazil (1996-2022). The historical, logical, and econometric consistency of the logical core of the two main theories of money is analyzed using objective bayesian and frequentist machine learning models, bayesian regularized artificial neural networks, and ensemble learning. It is concluded that money is not neutral at any time horizon and that, despite money is ultimately subordinated to prices, there is a reciprocal influence over time between money and prices which constitute a complex system. Non-neutrality is transmitted through aggregate demand and is based on the exchange value of money as a monetary unit.
Triple Instrumented Difference-in-Differences
Authors: Sho Miyaji
Date: 2025-01-24 00:00:00

In this paper, we formalize a triple instrumented difference-in-differences (DID-IV). In this design, a triple Wald-DID estimand, which divides the difference-in-difference-in-differences (DDD) estimand of the outcome by the DDD estimand of the treatment, captures the local average treatment effect on the treated. The identifying assumptions mainly comprise a monotonicity assumption, and the common acceleration assumptions in the treatment and the outcome. We extend the canonical triple DID-IV design to staggered instrument cases. We also describe the estimation and inference in this design in practice.
Forecasting the Volatility of Energy Transition Metals
Authors: Andrea Bastianin, Xiao Li, Luqman Shamsudin
Date: 2025-01-27 00:00:00

The transition to a cleaner energy mix, essential for achieving net-zero greenhouse gas emissions by 2050, will significantly increase demand for metals critical to renewable energy technologies. Energy Transition Metals (ETMs), including copper, lithium, nickel, cobalt, and rare earth elements, are indispensable for renewable energy generation and the electrification of global economies. However, their markets are characterized by high price volatility due to supply concentration, low substitutability, and limited price elasticity. This paper provides a comprehensive analysis of the price volatility of ETMs, a subset of Critical Raw Materials (CRMs). Using a combination of exploratory data analysis, data reduction, and visualization methods, we identify key features for accurate point and density forecasts. We evaluate various volatility models, including Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Stochastic Volatility (SV) models, to determine their forecasting performance. Our findings reveal significant heterogeneity in ETM volatility patterns, which challenge standard groupings by data providers and geological classifications. The results contribute to the literature on CRM economics and commodity volatility, offering novel insights into the complex dynamics of ETM markets and the modeling of their returns and volatilities.
Microfoundations of IPR and standardization strategies of companies: Evidence from the evolving European Single Market
Authors: Jussi Heikkilä, Satu Rinkinen, Tero Rantala
Date: 2025-01-27 00:00:00

Intellectual property rights (IPR) and standards are important institutions that by shaping appropriability conditions of companies impact international trade flows and the rate and direction of technological progress and innovation activity. We shed light on microfoundations of IPR and standardization capabilities and explore how companies have developed their IPR and standardization strategies and adapted to related institutional changes in the European Single Market. The analysis of the IPR and standardization strategies of companies active in P\"aij\"at-H\"ame region of Finland, a northern part of the European Union, reveals that only a few companies have explicit IPR and standardization strategies, but several have systematic approaches to following the development of standards and IPR environments in their industries. Companies build dynamic IPR and standardization capabilities and adapt their IPR and standardization strategies to the changing institutional environment via experiential learning.