pwc-archive/papers-with-abstracts huggingface dataset:
The Efficacy of Utility Functions for Multicriteria Hospital Case-Mix Planning
Authors: Robert L Burdett, Paul corry, Prasad Yarlagadda, David Cook, Sean Birgan
Date: 2023-07-31 00:00:00

A new approach to perform hospital case-mix planning (CMP) is introduced in this article. Our multi-criteria approach utilises utility functions (UF) to articulate the preferences and standpoint of independent decision makers regarding outputs. The primary aim of this article is to test whether a utility functions method (UFM) based upon the scalarization of aforesaid UF is an appropriate quantitative technique to, i) distribute hospital resources to different operating units, and ii) provide a better capacity allocation and case mix. Our approach is motivated by the need to provide a method able to evaluate the trade-off between different stakeholders and objectives of hospitals. To the best of our knowledge, no such approach has been considered before in the literature. As we will later show, this idea addresses various technical limitations, weaknesses, and flaws in current CMP. The efficacy of the aforesaid approach is tested on a case study of a large tertiary hospital. Currently UF are not used by hospital managers, and real functions are unavailable, hence, 14 rational options are tested. Our exploratory analysis has provided important guidelines for the application of these UF. It indicates that these UF provide a valuable starting point for planners, managers, and executives of hospitals to impose their goals and aspirations. In conclusion, our approach may be better at identifying case mix that users want to treat and seems more capable of modelling the varying importance of different levels of output. Apart from finding desirable case mixes to consider, the approach can provide important insights via a sensitivity analysis of the parameters of each UF.
Serendipity in Science
Authors: Pyung Nahm, Raviv Murciano-Goroff, Michael Park, Russell J. Funk
Date: 2023-08-15 00:00:00
Tasks: scientific discovery

Serendipity plays an important role in scientific discovery. Indeed, many of the most important breakthroughs, ranging from penicillin to the electric battery, have been made by scientists who were stimulated by a chance exposure to unsought but useful information. However, not all scientists are equally likely to benefit from such serendipitous exposure. Although scholars generally agree that scientists with a prepared mind are most likely to benefit from serendipitous encounters, there is much less consensus over what precisely constitutes a prepared mind, with some research suggesting the importance of openness and others emphasizing the need for deep prior experience in a particular domain. In this paper, we empirically investigate the role of serendipity in science by leveraging a policy change that exogenously shifted the shelving location of journals in university libraries and subsequently exposed scientists to unsought scientific information. Using large-scale data on 2.4 million papers published in 9,750 journals by 520,000 scientists at 115 North American research universities, we find that scientists with greater openness are more likely to benefit from serendipitous encounters. Following the policy change, these scientists tended to cite less familiar and newer work, and ultimately published papers that were more innovative. By contrast, we find little effect on innovativeness for scientists with greater depth of experience, who, in our sample, tended to cite more familiar and older work following the policy change.
Nested Multilevel Monte Carlo with Biased and Antithetic Sampling
Authors: Abdul-Lateef Haji-Ali, Jonathan Spence
Date: 2023-08-15 00:00:00

We consider the problem of estimating a nested structure of two expectations taking the form $U_0 = E[\max\{U_1(Y), \pi(Y)\}]$, where $U_1(Y) = E[X\ |\ Y]$. Terms of this form arise in financial risk estimation and option pricing. When $U_1(Y)$ requires approximation, but exact samples of $X$ and $Y$ are available, an antithetic multilevel Monte Carlo (MLMC) approach has been well-studied in the literature. Under general conditions, the antithetic MLMC estimator obtains a root mean squared error $\varepsilon$ with order $\varepsilon^{-2}$ cost. If, additionally, $X$ and $Y$ require approximate sampling, careful balancing of the various aspects of approximation is required to avoid a significant computational burden. Under strong convergence criteria on approximations to $X$ and $Y$, randomised multilevel Monte Carlo techniques can be used to construct unbiased Monte Carlo estimates of $U_1$, which can be paired with an antithetic MLMC estimate of $U_0$ to recover order $\varepsilon^{-2}$ computational cost. In this work, we instead consider biased multilevel approximations of $U_1(Y)$, which require less strict assumptions on the approximate samples of $X$. Extensions to the method consider an approximate and antithetic sampling of $Y$. Analysis shows the resulting estimator has order $\varepsilon^{-2}$ asymptotic cost under the conditions required by randomised MLMC and order $\varepsilon^{-2}|\log\varepsilon|^3$ cost under more general assumptions.
Riemannian geometry for efficient analysis of protein dynamics data
Authors: Willem Diepeveen, Carlos Esteve-Yagüe, Jan Lellmann, Ozan Öktem, Carola-Bibiane Schönlieb
Date: 2023-08-15 00:00:00

An increasingly common viewpoint is that protein dynamics data sets reside in a non-linear subspace of low conformational energy. Ideal data analysis tools for such data sets should therefore account for such non-linear geometry. The Riemannian geometry setting can be suitable for a variety of reasons. First, it comes with a rich structure to account for a wide range of geometries that can be modelled after an energy landscape. Second, many standard data analysis tools initially developed for data in Euclidean space can also be generalised to data on a Riemannian manifold. In the context of protein dynamics, a conceptual challenge comes from the lack of a suitable smooth manifold and the lack of guidelines for constructing a smooth Riemannian structure based on an energy landscape. In addition, computational feasibility in computing geodesics and related mappings poses a major challenge. This work considers these challenges. The first part of the paper develops a novel local approximation technique for computing geodesics and related mappings on Riemannian manifolds in a computationally feasible manner. The second part constructs a smooth manifold of point clouds modulo rigid body group actions and a Riemannian structure that is based on an energy landscape for protein conformations. The resulting Riemannian geometry is tested on several data analysis tasks relevant for protein dynamics data. It performs exceptionally well on coarse-grained molecular dynamics simulated data. In particular, the geodesics with given start- and end-points approximately recover corresponding molecular dynamics trajectories for proteins that undergo relatively ordered transitions with medium sized deformations. The Riemannian protein geometry also gives physically realistic summary statistics and retrieves the underlying dimension even for large-sized deformations within seconds on a laptop.
Interest Rate Dynamics and Commodity Prices
Authors: Christophe Gouel, Qingyin Ma, John Stachurski
Date: 2023-08-15 00:00:00

In economic studies and popular media, interest rates are routinely cited as a major factor behind commodity price fluctuations. At the same time, the transmission channels are far from transparent, leading to long-running debates on the sign and magnitude of interest rate effects. Purely empirical studies struggle to address these issues because of the complex interactions between interest rates, prices, supply changes, and aggregate demand. To move this debate to a solid footing, we extend the competitive storage model to include stochastically evolving interest rates. We establish general conditions for existence and uniqueness of solutions and provide a systematic theoretical and quantitative analysis of the interactions between interest rates and prices.
Preliminary investigation of the short-term in situ performance of an automatic masker selection system
Authors: Bhan Lam, Zhen-Ting Ong, Kenneth Ooi, Wen-Hui Ong, Trevor Wong, Karn N. Watcharasupat, Woon-Seng Gan
Date: 2023-08-15 00:00:00

Soundscape augmentation or "masking" introduces wanted sounds into the acoustic environment to improve acoustic comfort. Usually, the masker selection and playback strategies are either arbitrary or based on simple rules (e.g. -3 dBA), which may lead to sub-optimal increment or even reduction in acoustic comfort for dynamic acoustic environments. To reduce ambiguity in the selection of maskers, an automatic masker selection system (AMSS) was recently developed. The AMSS uses a deep-learning model trained on a large-scale dataset of subjective responses to maximize the derived ISO pleasantness (ISO 12913-2). Hence, this study investigates the short-term in situ performance of the AMSS implemented in a gazebo in an urban park. Firstly, the predicted ISO pleasantness from the AMSS is evaluated in comparison to the in situ subjective evaluation scores. Secondly, the effect of various masker selection schemes on the perceived affective quality and appropriateness would be evaluated. In total, each participant evaluated 6 conditions: (1) ambient environment with no maskers; (2) AMSS; (3) bird and (4) water masker from prior art; (5) random selection from same pool of maskers used to train the AMSS; and (6) selection of best-performing maskers based on the analysis of the dataset used to train the AMSS.
Model predictive control with dynamic move blocking
Authors: Valentina Breschi, Simone Formentin, Alberto Leva
Date: 2023-08-15 00:00:00
Tasks: Blocking, model, Model Predictive Control

Model Predictive Control (MPC) has proven to be a powerful tool for the control of systems with constraints. Nonetheless, in many applications, a major challenge arises, that is finding the optimal solution within a single sampling instant to apply a receding-horizon policy. In such cases, many suboptimal solutions have been proposed, among which the possibility of "blocking" some moves a-priori. In this paper, we propose a dynamic approach to move blocking, to exploit the solution already available at the previous iteration, and we show not only that such an approach preserves asymptotic stability, but also that the decrease of performance with respect to the ideal solution can be theoretically bounded.
A Data-Driven Policy for Addressing Deployability Issue of FMM FRPs: Resources Qualification and Deliverability
Authors: Mohammad Ghaljehei, Mojdeh Khorsand
Date: 2023-08-15 00:00:00

Intensified netload uncertainty and variability led to the concept of a new market product, flexible ramping product (FRP). The main goal of FRP is to enhance the generation dispatch flexibility inside real-time (RT) markets to mitigate energy imbalances due to ramp capability shortage. Generally, the FRP requirements are based on system-wide or proxy requirements, so the effect of FRP awards on the transmission line constraints is not considered. This can lead to FRP deployability issues in RT operation. This paper proposes a new FRP design based on a datadriven policy incorporating ramping response factor sets to address FRP deployability issue. First, a data-mining algorithm is performed to predict the ramp-qualified generators to create the data-driven policy. Then, the FRP awards are assigned to these units while considering effects of post-deployment of FRPs on the transmission line limits. Finally, the proposed data-driven policy is tested against proxy policy through an out-of-sample validation phase that (i) mimics the RT operation of the CAISO, and (ii) represents the expensive ad-hoc actions needed for procuring additional ramping capability to follow realized netload changes. The results show the effectiveness of the proposed data-driven policy from reliability and economic points of view
Network-Independent and User-Controlled RIS: An Experimental Perspective
Authors: Emre Arslan, Fatih Kilinc, Ertugrul Basar, Huseyin Arslan
Date: 2023-08-15 00:00:00

The march towards 6G is accelerating and future wireless network architectures require enhanced performance along with significant coverage especially, to combat impairments on account of the wireless channel. Reconfigurable intelligent surface (RIS) technology is a promising solution, that has recently been considered as a research topic in standards, to help manipulate the channel in favor of users needs. Generally, in experimental RIS systems, the RIS is either connected to the transmitter (Tx) or receiver (Rx) through a physical backhaul link and it is controlled by the network and requires significant computation at the RIS for codebook (CB) designs. In this paper, we propose a practical user-controlled RIS system that is isolated from the network to enhance communication performance and provide coverage to the user based on its location and preference. Furthermore, a low-complexity algorithm is proposed to aid in CB selection for the user, which is performed through the wireless cloud to enable a passive and energy efficient RIS. Extensive experimental test-bed measurements demonstrate the enhanced performance of the proposed system while both results match and validate each other.
Resilience assessment and planning in power distribution systems:Past and future considerations
Authors: Shuva Paul, Abodh Poudyal, Shiva Poudel, Anamika Dubey, Zhaoyu Wang
Date: 2023-08-15 00:00:00

Over the past decade, extreme weather events have significantly increased worldwide, leading to widespread power outages and blackouts. As these threats continue to challenge power distribution systems, the importance of mitigating the impacts of extreme weather events has become paramount. Consequently, resilience has become crucial for designing and operating power distribution systems. This work comprehensively explores the current landscape of resilience evaluation and metrics within the power distribution system domain, reviewing existing methods and identifying key attributes that define effective resilience metrics. The challenges encountered during the formulation, development, and calculation of these metrics are also addressed. Additionally, this review acknowledges the intricate interdependencies between power distribution systems and critical infrastructures, including information and communication technology, transportation, water distribution, and natural gas networks. It is important to understand these interdependencies and their impact on power distribution system resilience. Moreover, this work provides an in-depth analysis of existing research on planning solutions to enhance distribution system resilience and support power distribution system operators and planners in developing effective mitigation strategies. These strategies are crucial for minimizing the adverse impacts of extreme weather events and fostering overall resilience within power distribution systems.
Sign Gradient Descent Algorithms for Kinetostatic Protein Folding
Authors: Alireza Mohammadi, Mohammad Al Janaideh
Date: 2023-08-14 00:00:00
Tasks: Protein Folding

This paper proposes a sign gradient descent (SGD) algorithm for predicting the three-dimensional folded protein molecule structures under the kinetostatic compliance method (KCM). In the KCM framework, which can be used to simulate the range of motion of peptide-based nanorobots/nanomachines, protein molecules are modeled as a large number of rigid nano-linkages that form a kinematic mechanism under motion constraints imposed by chemical bonds while folding under the kinetostatic effect of nonlinear interatomic force fields. In a departure from the conventional successive kinetostatic fold compliance framework, the proposed SGD-based iterative algorithm in this paper results in convergence to the local minima of the free energy of protein molecules corresponding to their final folded conformations in a faster and more robust manner. KCMbased folding dynamics simulations of the backbone chains of protein molecules demonstrate the effectiveness of the proposed algorithm.
Using High-fidelity Time-Domain Simulation Data to Construct Multi-fidelity State Derivative Function Surrogate Models for use in Control and Optimization
Authors: Athul Krishna Sundarrajan, Daniel R. Herber
Date: 2023-08-14 00:00:00

Models that balance accuracy against computational costs are advantageous when designing dynamic systems with optimization studies, as several hundred predictive function evaluations might be necessary to identify the optimal solution. The efficacy and use of derivative function surrogate models (DFSMs), or approximate models of the state derivative function, have been well-established in the literature. However, previous studies have assumed an a priori state dynamic model is available that can be directly evaluated to construct the DFSM. In this article, we propose an approach to extract the state derivative information from system simulations using piecewise polynomial approximations. Once the required information is available, we propose a multi-fidelity DFSM approach as a predictive model for the system's dynamic response. This multi-fidelity model consists of summation between a linear-fit lower-fidelity model and an additional nonlinear error corrective function that compensates for the error between the high-fidelity simulations and low-fidelity models. We validate the model by comparing the simulation results from the DFSM to the high-fidelity tools. The DFSM model is, on average, five times faster than the high-fidelity tools while capturing the key time domain and power spectral density~(PSD) trends. Then, an optimal control study using the DFSM is conducted with outcomes showing that the DFSM approach can be used for complex systems like floating offshore wind turbines~(FOWTs) and help identify control trends and trade-offs.
N-Body Oscillator Interactions of Higher-Order Coupling Functions
Authors: Youngmin Park, Dan Wilson
Date: 2023-08-14 00:00:00
Tasks: valid

We introduce a method to identify phase equations that include $N$-body interactions for general coupled oscillators valid far beyond the weak coupling approximation. This strategy is an extension of the theory from [Park and Wilson, SIADS 20.3 (2021)] and yields coupling functions for $N\geq2$ oscillators for arbitrary types of coupling (e.g., diffusive, gap-junction, chemical synaptic). These coupling functions enable the study of oscillator networks in terms of phase-locked states, whose stability can be determined using straightforward linear stability arguments. We demonstrate the utility of our approach with two examples. First, we use $N=3$ diffusively coupled complex Ginzburg-Landau (CGL) model and show that the loss of stability in its splay state occurs through a Hopf bifurcation \yp{as a function of non-weak diffusive coupling. Our reduction also captures asymptotic limit-cycle dynamics in the phase differences}. Second, we use $N=3$ realistic conductance-based thalamic neuron models and show that our method correctly predicts a loss in stability of a splay state for non-weak synaptic coupling. In both examples, our theory accurately captures model behaviors that weak and recent non-weak coupling theories can not.
Optimizing B2B Product Offers with Machine Learning, Mixed Logit, and Nonlinear Programming
Authors: John V. Colias, Stella Park, Elizabeth Horn
Date: 2023-08-15 00:00:00

In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential customer, features, discounts, and customer purchase decisions) to estimate a mixed logit choice model. The model is estimated via hierarchical Bayes and machine learning, delivering customer-level parameter estimates. Customer-level estimates are input into a nonlinear programming next-offer maximization problem to select optimal features and discount level for customer segments, where segments are based on loyalty and discount elasticity. The mixed logit model is integrated with economic theory (the random utility model), and it predicts both customer perceived value for and response to alternative future sales offers. The methodology can be implemented to support value-based pricing and selling efforts. Contributions to the literature include: (a) the use of customer-level parameter estimates from a mixed logit model, delivered via a hierarchical Bayes estimation procedure, to support value-based pricing decisions; (b) validation that mixed logit customer-level modeling can deliver strong predictive accuracy, not as high as random forest but comparing favorably; and (c) a nonlinear programming problem that uses customer-level mixed logit estimates to select optimal features and discounts.
Impact of COVID-19 Lockdown Measures on Chinese Startups and Local Government Public Finance: Challenges and Policy Implications
Authors: Xin Sun
Date: 2023-08-14 00:00:00

This paper aims to assess the impact of COVID-19 on the public finance of Chinese local governments, with a particular focus on the effect of lockdown measures on startups during the pandemic. The outbreak has placed significant fiscal pressure on local governments, as containment measures have led to declines in revenue and increased expenses related to public health and social welfare. In tandem, startups have faced substantial challenges, including reduced funding and profitability, due to the negative impact of lockdown measures on entrepreneurship. Moreover, the pandemic has generated short- and long-term economic shocks, affecting both employment and economic recovery. To address these challenges, policymakers must balance health concerns with economic development. In this regard, the government should consider implementing more preferential policies that focus on startups to ensure their survival and growth. Such policies may include financial assistance, tax incentives, and regulatory flexibility to foster innovation and entrepreneurship. By and large, the COVID-19 pandemic has had a profound impact on both the public finance of Chinese local governments and the startup ecosystem. Addressing the challenges faced by local governments and startups will require a comprehensive approach that balances health and economic considerations and includes targeted policies to support entrepreneurship and innovation.
Stochastic Optimal Investment Strategy for Net-Zero Energy Houses
Authors: Mengmou Li, Taichi Tanaka, A. Daniel Carnerero, Yasuaki Wasa, Kenji Hirata, Yasumasa Fujisaki, Yoshiaki Ushifusa, Takeshi Hatanaka
Date: 2023-08-11 00:00:00

In this research, we investigate Net-Zero Energy Houses (ZEH), which harness regionally produced electricity from photovoltaic(PV) panels and fuel cells, integrating them into a local power system in pursuit of achieving carbon neutrality. This paper examines the impact of electricity sharing among users who are working towards attaining ZEH status through the integration of PV panels and battery storage devices. We propose two potential scenarios: the first assumes that all users individually invest in storage devices, hence minimizing their costs on a local level without energy sharing; the second envisions cost minimization through the collective use of a shared storage device, managed by a central manager. These two scenarios are formulated as a stochastic convex optimization and a cooperative game, respectively. To tackle the stochastic challenges posed by multiple random variables, we apply the Monte Carlo sample average approximation (SAA) to the problems. To demonstrate the practical applicability of these models, we implement the proposed scenarios in the Jono neighborhood in Kitakyushu, Japan.