pwc-archive/papers-with-abstracts huggingface dataset:
Fast Grid Emissions Sensitivities using Parallel Decentralized Implicit Differentiation
Authors: Anthony Degleris, Lucas Fuentes Valenzuela, Ram Rajagopal, Marco Pavone, Abbas El Gamal
Date: 2024-08-20 00:00:00

Marginal emissions rates -- the sensitivity of carbon emissions to electricity demand -- are important for evaluating the impact of emissions mitigation measures. Like locational marginal prices, locational marginal emissions rates (LMEs) can vary geographically, even between nearby locations, and may be coupled across time periods because of, for example, storage and ramping constraints. This temporal coupling makes computing LMEs computationally expensive for large electricity networks with high storage and renewable penetrations. Recent work demonstrates that decentralized algorithms can mitigate this problem by decoupling timesteps during differentiation. Unfortunately, we show these potential speedups are negated by the sparse structure inherent in power systems problems. We address these limitations by introducing a parallel, reverse-mode decentralized differentiation scheme that never explicitly instantiates the solution map Jacobian. We show both theoretically and empirically that parallelization is necessary to achieve non-trivial speedups when computing grid emissions sensitivities. Numerical results on a 500 node system indicate that our method can achieve greater than 10x speedups over centralized and serial decentralized approaches.
Fault Tolerant Dynamic Task Assignment for UAV-based Search Teams
Authors: Ali Nasir, Mohammad AlDurgam
Date: 2024-08-20 00:00:00
Tasks: Management

This research offers a novel framework for dynamic task assignment for unmanned aerial vehicles (UAVs) in cooperative search settings. Notably, it incorporates post-fault UAV capabilities into job assignment techniques, assuring operational dependability in the event of sensor and actuator failures. A significant innovation is the utilization of UAV battery charge to assess range relative to search objectives, hence improving job distribution while conserving battery life. This model integrates repair, recharge, and stochastic goal recurrence, hence increasing its real-world applicability. Using stochastic dynamic programming, this method makes it simpler to determine optimal assignment policies offline so they may be implemented rapidly online. This paper emphasizes the holistic aspect of the proposed model, which connects high-level task rules to low-level control capabilities. A simulation-based case study proves its usefulness, highlighting its robustness in fault-prone and battery-variable settings. Overall, this paper proposes and demonstrates a comprehensive method for assigning UAV tasks that integrates defect awareness, battery management, and multilayer control through the use of stochastic dynamic programming.
Semi-on-Demand Off-Peak Transit Services with Shared Autonomous Vehicles -- Service Planning, Simulation, and Analysis in Munich, Germany
Authors: Max T. M. Ng, Roman Engelhardt, Florian Dandl, Vasileios Volakakis, Hani S. Mahmassani, Klaus Bogenberger
Date: 2024-08-20 00:00:00
Tasks: Autonomous Vehicles

This study investigates the implementation of semi-on-demand (SoD) hybrid-route services using Shared Autonomous Vehicles (SAVs) on existing transit lines. SoD services combine the cost efficiency of fixed-route buses with the flexibility of on-demand services. SAVs first serve all scheduled fixed-route stops, then drop off and pick up passengers in the pre-determined flexible-route portion, and return to the fixed route. This study addresses four key questions: optimal fleet and vehicle sizes for peak-hour fixed-route services with SAVs and during transition (from drivers to autonomous vehicles), optimal off-peak SoD service planning, and suitable use cases. The methodology combines analytical modeling for service planning with agent-based simulation for operational analysis. We examine ten bus routes in Munich, Germany, considering full SAV and transition scenarios with varying proportions of drivers. Our findings demonstrate that the lower operating costs of SAVs improve service quality through increased frequency and smaller vehicles, even in transition scenarios. The reduced headway lowers waiting time and also favors more flexible-route operation in SoD services. The optimal SoD settings range from fully flexible to hybrid routes, where higher occupancy from the terminus favors shorter flexible routes. During the transition phase, limited fleet size and higher headways constrain the benefits of flexible-route operations. The simulation results corroborate the SoD benefits of door-to-door convenience, attracting more passengers without excessive detours and operator costs at moderate flexible-route lengths, and validate the analytical model.
High-Sensitivity and Compact Time-domain Soil Moisture Sensor Using Dispersive Phase Shifter for Complex Permittivity Measurement
Authors: Rasool Keshavarz, Negin Shariati
Date: 2024-08-20 00:00:00
Tasks: Sensitivity

This paper presents a Time-Domain Transmissometry Soil Moisture Sensor (TDT-SMS) using a Dispersive Phase Shifter (DPS), consisting of an interdigital capacitor that is loaded with a stacked 4-turn Complementary Spiral Resonator (S4-CSR). Soil moisture measurement technique of the proposed sensor is based on the complex permittivity sensing property of a DPS in time domain. Soil relative permittivity which varies with its moisture content is measured by burying the DPS under a soil mass and changing its phase difference while excited with a 114 MHz sine wave (single tone). DPS output phase and magnitude are compared with the reference signal and measured with a phase/loss detector. The proposed sensor exhibits accuracy better than +-1.2 percent at the highest Volumetric Water Content (VWC=30 percent) for sandy-type soil. Precise design guide is developed and simulations are performed to achieve a highly sensitive sensor. The measurement results validate the accuracy of theoretical analysis and design procedure. Owning the advantages of low profile, low power consumption, and high sensitivity makes the proposed TDT-SMS a good candidate for precision farming and IoT systems.
Low Profile Metamaterial Band-Pass Filter Loaded with 4-Turn Complementary Spiral Resonator for WPT Applications
Authors: Rasool Keshavarz, Negin Shariati
Date: 2024-08-20 00:00:00

In this paper, a very compact and low insertion loss metamaterial band-pass filter (MBPF) at the center frequency of f0=730 MHz is proposed, based on the rectangular-shape 4 turn complementary spiral resonators (4 CSR). The proposed MBPF consists of an interdigital capacitor as a series capacitance in the top layer, leading to improve the stopband performance in the pass band range of 700 to 760 MHz, which makes it suitable for wireless power transfer (WPT) systems by rejecting unwanted signals. In order to validate the performance of the proposed technique, the MBPF is fabricated on the RO-4003 substrate and great agreement is achieved between simulated and measured results. The stop band attenuations of greater than 52 dB and 20 dB are obtained around the 0.8xfcl (lower cutoff frequency) and 1.2xfcu (upper cutoff frequency), respectively.
Self-Refined Generative Foundation Models for Wireless Traffic Prediction
Authors: Chengming Hu, Hao Zhou, Di wu, Xi Chen, Jun Yan, Xue Liu
Date: 2024-08-19 00:00:00
Tasks: Few-Shot Learning, In-Context Learning, Language Modelling, Large Language Model, Prediction, Traffic Prediction

With a broad range of emerging applications in 6G networks, wireless traffic prediction has become a critical component of network management. However, the dynamically shifting distribution of wireless traffic in non-stationary 6G networks presents significant challenges to achieving accurate and stable predictions. Motivated by recent advancements in Generative AI (GAI)-enabled 6G networks, this paper proposes a novel self-refined Large Language Model (LLM) for wireless traffic prediction, namely TrafficLLM, through in-context learning without parameter fine-tuning or model training. The proposed TrafficLLM harnesses the powerful few-shot learning abilities of LLMs to enhance the scalability of traffic prediction in dynamically changing wireless environments. Specifically, our proposed TrafficLLM embraces an LLM to iteratively refine its predictions through a three-step process: traffic prediction, feedback generation, and prediction refinement. Initially, the proposed TrafficLLM conducts traffic predictions using task-specific demonstration prompts. Recognizing that LLMs may generate incorrect predictions on the first attempt, we subsequently incorporate feedback demonstration prompts designed to provide multifaceted and valuable feedback related to these initial predictions. Following this comprehensive feedback, our proposed TrafficLLM introduces refinement demonstration prompts, enabling the same LLM to further refine its predictions and thereby enhance prediction performance. The evaluations on two realistic datasets demonstrate that the proposed TrafficLLM outperforms state-of-the-art methods with performance improvements of 23.17% and 17.09%, respectively.
An LMI-based Robust Fuzzy Controller for Blood Glucose Regulation in Type 1 Diabetes
Authors: Mohammadreza Ganji, Mahdi Pourgholi
Date: 2024-08-19 00:00:00

This paper presents a control algorithm for creating an artificial pancreas for type 1 diabetes, factoring in input saturation for a practical application. By utilizing the parallel distributed compensation and Takagi-Sugeno Fuzzy model, we design an optimal robust fuzzy controller. Stability conditions derived from the Lyapunov method are expressed as linear matrix inequalities, allowing for optimal controller gain selection that minimizes disturbance effects. We employ the minimal Bergman and Tolic models to represent type 1 diabetes glucose-insulin dynamics, converting them into corresponding Takagi-Sugeno fuzzy models using the sector nonlinearity approach. Simulation results demonstrate the proposed controller's effectiveness.
A reassessment of the "hard-steps" model for the evolution of intelligent life
Authors: Daniel B. Mills, Jennifer L. Macalady, Adam Frank, Jason T. Wright
Date: 2024-08-19 00:00:00

According to the "hard-steps" model, the origin of humanity required "successful passage through a number of intermediate steps" (so-called "hard" or "critical" steps) that were intrinsically improbable with respect to the total time available for biological evolution on Earth. This model similarly predicts that technological life analogous to human life on Earth is "exceedingly rare" in the universe. Here, we critically reevaluate the core assumptions of the hard-steps model in light of recent advances in the Earth and life sciences. Specifically, we advance a potential alternative model where there are no hard steps, and evolutionary novelties (or singularities) required for human origins can be explained via mechanisms outside of intrinsic improbability. Furthermore, if Earth's surface environment was initially inhospitable not only to human life, but also to certain key intermediate steps in human evolution (e.g., the origin of eukaryotic cells, multicellular animals), then the "delay" in the appearance of humans can be best explained through the sequential opening of new global environmental windows of habitability over Earth history, with humanity arising relatively quickly once the right conditions were established. In this co-evolutionary (or geobiological) scenario, humans did not evolve "early" or "late" with respect to the total lifespan of the biosphere, but "on time."
Mechanics promotes coherence in heterogeneous active media
Authors: Soling Zimik, Sitabhra Sinha
Date: 2024-08-20 00:00:00
Tasks: Diversity

Synchronization of activity among myocytes constituting vital organs, e.g., the heart, is crucial for physiological functions. Self-organized coordination in such heterogeneous ensemble of excitable and oscillatory cells is therefore of clinical importance. We show by varying the strength of intercellular coupling and the electrophysiological diversity, a wide range of collective behavior emerges including clusters of synchronized activity. Strikingly, stretch-activated currents allow waves of mechanical deformation to alter the activity of neighboring cells, promoting robust global coherence.
Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature
Authors: Andrei Constantin, Deaglan Bartlett, Harry Desmond, Pedro G. Ferreira
Date: 2024-08-12 00:00:00
Tasks: Symbolic Regression

Physics, as a fundamental science, aims to understand the laws of Nature and describe them in mathematical equations. While the physical reality manifests itself in a wide range of phenomena with varying levels of complexity, the equations that describe them display certain statistical regularities and patterns, which we begin to explore here. By drawing inspiration from linguistics, where Zipf's law states that the frequency of any word in a large corpus of text is roughly inversely proportional to its rank in the frequency table, we investigate whether similar patterns for the distribution of operators emerge in the equations of physics. We analyse three corpora of formulae and find, using sophisticated implicit-likelihood methods, that the frequency of operators as a function of their rank in the frequency table is best described by an exponential law with a stable exponent, in contrast with Zipf's inverse power-law. Understanding the underlying reasons behind this statistical pattern may shed light on Nature's modus operandi or reveal recurrent patterns in physicists' attempts to formalise the laws of Nature. It may also provide crucial input for symbolic regression, potentially augmenting language models to generate symbolic models for physical phenomena. By pioneering the study of statistical regularities in the equations of physics, our results open the door for a meta-law of Nature, a (probabilistic) law that all physical laws obey.
Enhancing Causal Discovery in Financial Networks with Piecewise Quantile Regression
Authors: Cameron Cornell, Lewis Mitchell, Matthew Roughan
Date: 2024-08-22 00:00:00
Tasks: Causal Discovery, quantile regression, regression

Financial networks can be constructed using statistical dependencies found within the price series of speculative assets. Across the various methods used to infer these networks, there is a general reliance on predictive modelling to capture cross-correlation effects. These methods usually model the flow of mean-response information, or the propagation of volatility and risk within the market. Such techniques, though insightful, don't fully capture the broader distribution-level causality that is possible within speculative markets. This paper introduces a novel approach, combining quantile regression with a piecewise linear embedding scheme - allowing us to construct causality networks that identify the complex tail interactions inherent to financial markets. Applying this method to 260 cryptocurrency return series, we uncover significant tail-tail causal effects and substantial causal asymmetry. We identify a propensity for coins to be self-influencing, with comparatively sparse cross variable effects. Assessing all link types in conjunction, Bitcoin stands out as the primary influencer - a nuance that is missed in conventional linear mean-response analyses. Our findings introduce a comprehensive framework for modelling distributional causality, paving the way towards more holistic representations of causality in financial markets.
Dynamical analysis of financial stocks network: improving forecasting using network properties
Authors: Ixandra Achitouv
Date: 2024-08-21 00:00:00

Applying a network analysis to stock return correlations, we study the dynamical properties of the network and how they correlate with the market return, finding meaningful variables that partially capture the complex dynamical processes of stock interactions and the market structure. We then use the individual properties of stocks within the network along with the global ones, to find correlations with the future returns of individual S&P 500 stocks. Applying these properties as input variables for forecasting, we find a 50% improvement on the R2score in the prediction of stock returns on long time scales (per year), and 3% on short time scales (2 days), relative to baseline models without network variables.
Distributed and Secure Kernel-Based Quantum Machine Learning
Authors: Arjhun Swaminathan, Mete Akgün
Date: 2024-08-16 00:00:00
Tasks: Quantum Machine Learning

Quantum computing promises to revolutionize machine learning, offering significant efficiency gains in tasks such as clustering and distance estimation. Additionally, it provides enhanced security through fundamental principles like the measurement postulate and the no-cloning theorem, enabling secure protocols such as quantum teleportation and quantum key distribution. While advancements in secure quantum machine learning are notable, the development of secure and distributed quantum analogues of kernel-based machine learning techniques remains underexplored. In this work, we present a novel approach for securely computing common kernels, including polynomial, radial basis function (RBF), and Laplacian kernels, when data is distributed, using quantum feature maps. Our methodology introduces a robust framework that leverages quantum teleportation to ensure secure and distributed kernel learning. The proposed architecture is validated using IBM's Qiskit Aer Simulator on various public datasets.
A nested nonparametric logit model for microtransit revenue management supplemented with citywide synthetic data
Authors: Xiyuan Ren, Joseph Y. J. Chow, Venktesh Pandey, Linfei Yuan
Date: 2024-08-22 00:00:00
Tasks: Management

As an IT-enabled multi-passenger mobility service, microtransit can improve accessibility, reduce congestion, and enhance flexibility. However, its heterogeneous impacts across travelers necessitate better tools for microtransit forecasting and revenue management, especially when actual usage data are limited. We propose a nested nonparametric model for joint travel mode and ride pass subscription choice, estimated using marginal subscription data and synthetic populations. The model improves microtransit choice modeling by (1) leveraging citywide synthetic data for greater spatiotemporal granularity, (2) employing an agent-based estimation approach to capture heterogeneous user preferences, and (3) integrating mode choice parameters into subscription choice modeling. We apply our methodology to a case study in Arlington, TX, using synthetic data from Replica Inc. and microtransit data from Via. Our model accurately predicts the number of subscribers in the upper branch and achieves a high McFadden R2 in the lower branch (0.603 for weekday trips and 0.576 for weekend trips), while also retrieving interpretable elasticities and consumer surplus. We further integrate the model into a simulation-based framework for microtransit revenue management. For the ride pass pricing policy, our simulation results show that reducing the price of the weekly pass ($25 -> $18.9) and monthly pass ($80 -> $71.5) would surprisingly increase total revenue by $127 per day. For the subsidy policy, our simulation results show that a 100% fare discount would reduce 61 car trips to AT&T Stadium for a game event, and increase 82 microtransit trips to Medical City Arlington, but require subsidies of $533 per event and $483 per day, respectively.
Momentum Informed Inflation-at-Risk
Authors: Tibor Szendrei, Arnab Bhattacharjee
Date: 2024-08-22 00:00:00

Growth-at-Risk has recently become a key measure of macroeconomic tail-risk, which has seen it be researched extensively. Surprisingly, the same cannot be said for Inflation-at-Risk where both tails, deflation and high inflation, are of key concern to policymakers, which has seen comparatively much less research. This paper will tackle this gap and provide estimates for Inflation-at-Risk. The key insight of the paper is that inflation is best characterised by a combination of two types of nonlinearities: quantile variation, and conditioning on the momentum of inflation.
SPORTSCausal: Spill-Over Time Series Causal Inference
Authors: Carol Liu
Date: 2024-08-21 00:00:00
Tasks: Causal Inference, Sociology, Time Series

Randomized controlled trials (RCTs) have long been the gold standard for causal inference across various fields, including business analysis, economic studies, sociology, clinical research, and network learning. The primary advantage of RCTs over observational studies lies in their ability to significantly reduce noise from individual variance. However, RCTs depend on strong assumptions, such as group independence, time independence, and group randomness, which are not always feasible in real-world applications. Traditional inferential methods, including analysis of covariance (ANCOVA), often fail when these assumptions do not hold. In this paper, we propose a novel approach named \textbf{Sp}ill\textbf{o}ve\textbf{r} \textbf{T}ime \textbf{S}eries \textbf{Causal} (\verb+SPORTSCausal+), which enables the estimation of treatment effects without relying on these stringent assumptions. We demonstrate the practical applicability of \verb+SPORTSCausal+ through a real-world budget-control experiment. In this experiment, data was collected from both a 5\% live experiment and a 50\% live experiment using the same treatment. Due to the spillover effect, the vanilla estimation of the treatment effect was not robust across different treatment sizes, whereas \verb+SPORTSCausal+ provided a robust estimation.
Actually, There is No Rotational Indeterminacy in the Approximate Factor Model
Authors: Philipp Gersing
Date: 2024-08-21 00:00:00

We show that in the approximate factor model the population normalised principal components converge in mean square (up to sign) under the standard assumptions for $n\to \infty$. Consequently, we have a generic interpretation of what the principal components estimator is actually identifying and existing results on factor identification are reinforced and refined. Based on this result, we provide a new asymptotic theory for the approximate factor model entirely without rotation matrices. We show that the factors space is consistently estimated with finite $T$ for $n\to \infty$ while consistency of the factors a.k.a the $L^2$ limit of the normalised principal components requires that both $(n, T)\to \infty$.
Robust Bayes Treatment Choice with Partial Identification
Authors: Andrés Aradillas Fernández, José Luis Montiel Olea, Chen Qiu, Jörg Stoye, Serdil Tinda
Date: 2024-08-21 00:00:00

We study a class of binary treatment choice problems with partial identification, through the lens of robust (multiple prior) Bayesian analysis. We use a convenient set of prior distributions to derive ex-ante and ex-post robust Bayes decision rules, both for decision makers who can randomize and for decision makers who cannot. Our main messages are as follows: First, ex-ante and ex-post robust Bayes decision rules do not tend to agree in general, whether or not randomized rules are allowed. Second, randomized treatment assignment for some data realizations can be optimal in both ex-ante and, perhaps more surprisingly, ex-post problems. Therefore, it is usually with loss of generality to exclude randomized rules from consideration, even when regret is evaluated ex-post. We apply our results to a stylized problem where a policy maker uses experimental data to choose whether to implement a new policy in a population of interest, but is concerned about the external validity of the experiment at hand (Stoye, 2012); and to the aggregation of data generated by multiple randomized control trials in different sites to make a policy choice in a population for which no experimental data are available (Manski, 2020; Ishihara and Kitagawa, 2021).