pwc-archive/papers-with-abstracts huggingface dataset:
Advancements in Programmable Lipid Nanoparticles: Exploring the Four-Domain Model for Targeted Drug Delivery
Authors: Zhaoyu Liu, Jingxun Chen, Mingkun Xu, David H. Gracias, Ken-Tye Yong, Yuanyuan Wei, Ho-Pui Ho
Date: 2024-08-11 00:00:00

Programmable lipid nanoparticles, or LNPs, represent a breakthrough in the realm of targeted drug delivery, offering precise spatiotemporal control essential for the treatment of complex diseases such as cancer and genetic disorders. In order to provide a more modular perspective and a more balanced analysis of the mechanism, this review presents a novel Four-Domain Model that consists of Architecture, Interface, Payload, and Dispersal Domain. We explored the dynamical equilibrium between LNPs components and the surroundings throughout their destiny, from formulation to release. On the basis of this, we delve deep into manufacturing challenges, scalability issues, and regulatory hurdles, associated with the clinical translation of LNP technology. Within the framework focusing on the programmability in each domain, we prioritized patient-centric factors like dosing regimens, administration techniques, and potential consequences. Notably, this review expands to innovative anatomical routes, such as intranasal and intraocular administration, offering a thorough examination of the advantages and disadvantages of each route. We also offered a comprehensive comparison between artificial LNPs and natural exosomes in terms of functionality, biocompatibility, and therapeutic potential. Ultimately, this review highlights the potential of programmable LNPs to evolve into more intelligent, naturally integrated systems, achieving optimal biocompatibility and functionality.
Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Authors: Makoto Takamoto, Viktor Zaverkin, Mathias Niepert
Date: 2024-07-23 00:00:00
Tasks: Computational chemistry, Weakly-supervised Learning

Machine learning plays an increasingly important role in computational chemistry and materials science, complementing computationally intensive ab initio and first-principles methods. Despite their utility, machine-learning models often lack generalization capability and robustness during atomistic simulations, yielding unphysical energy and force predictions that hinder their real-world applications. We address this challenge by introducing a physics-informed, weakly supervised approach for training machine-learned interatomic potentials (MLIPs). We introduce two novel loss functions, extrapolating the potential energy via a Taylor expansion and using the concept of conservative forces. Our approach improves the accuracy of MLIPs applied to training tasks with sparse training data sets and reduces the need for pre-training computationally demanding models with large data sets. Particularly, we perform extensive experiments demonstrating reduced energy and force errors -- often lower by a factor of two -- for various baseline models and benchmark data sets. Moreover, we demonstrate improved robustness during MD simulations of the MLIP models trained with the proposed weakly supervised loss. Finally, our approach improves the fine-tuning of foundation models on sparse, highly accurate ab initio data. An implementation of our method and scripts for executing experiments are available at https://github.com/nec-research/PICPS-ML4Sci.
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0
Authors: Nicolas Hayer, Thorsten Wendel, Stephan Mandt, Hans Hasse, Fabian Jirasek
Date: 2024-07-25 00:00:00
Tasks: Matrix Completion

Accurate prediction of thermodynamic properties is pivotal in chemical engineering for optimizing process efficiency and sustainability. Physical group-contribution (GC) methods are widely employed for this purpose but suffer from historically grown, incomplete parameterizations, limiting their applicability and accuracy. In this work, we overcome these limitations by combining GC with matrix completion methods (MCM) from machine learning. We use the novel approach to predict a complete set of pair-interaction parameters for the most successful GC method: UNIFAC, the workhorse for predicting activity coefficients in liquid mixtures. The resulting new method, UNIFAC 2.0, is trained and validated on more than 224,000 experimental data points, showcasing significantly enhanced prediction accuracy (e.g., nearly halving the mean squared error) and increased scope by eliminating gaps in the original model's parameter table. Moreover, the generic nature of the approach facilitates updating the method with new data or tailoring it to specific applications.
Predictive maintenance solution for industrial systems -- an unsupervised approach based on log periodic power law
Authors: Bogdan Łobodziński
Date: 2024-08-01 00:00:00
Tasks: Time Series

A new unsupervised predictive maintenance analysis method based on the renormalization group approach used to discover critical behavior in complex systems has been proposed. The algorithm analyzes univariate time series and detects critical points based on a newly proposed theorem that identifies critical points using a Log Periodic Power Law function fits. Application of a new algorithm for predictive maintenance analysis of industrial data collected from reciprocating compressor systems is presented. Based on the knowledge of the dynamics of the analyzed compressor system, the proposed algorithm predicts valve and piston rod seal failures well in advance.
Optical RISs Improve the Secret Key Rate of Free-Space QKD in HAP-to-UAV Scenarios
Authors: Phuc V. Trinh, Shinya Sugiura, Chao Xu, Lajos Hanzo
Date: 2024-08-12 00:00:00

Large optical reconfigurable intelligent surfaces (ORISs) are proposed for employment on building rooftops to facilitate free-space quantum key distribution (QKD) between highaltitude platforms (HAPs) and low-altitude platforms (LAPs). Due to practical constraints, the communication terminals can only be positioned beneath the LAPs, preventing direct upward links to HAPs. By deploying ORISs on rooftops to reflect the beam arriving from HAPs towards LAPs from below, reliable HAP-to-LAP links can be established. To accurately characterize the optical beam propagation, we develop an analytical channel model based on extended Huygens-Fresnel principles for representing both the atmospheric turbulence effects and the hovering fluctuations of LAPs. This model facilitates adaptive ORIS beam-width control through linear, quadratic, and focusing phase shifts, which are capable of effectively mitigating the detrimental effects of beam broadening and pointing errors (PE). Consequently, the information-theoretic bound of the secret key rate and the security performance of a decoy-state QKD protocol are analyzed. Our findings demonstrate that quadratic phase shifts enhance the SKR at high HAP-ORIS zenith angles or mild PE conditions by narrowing the beam to optimal sizes. By contrast, linear phase shifts are advantageous at low HAP-ORIS zenith angles or moderate-to-high PE by diverging the beam to mitigate LAP fluctuations.
From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks
Authors: Andrea Ceschini, Francesco Mauro, Francesca De Falco, Alessandro Sebastianelli, Alessio Verdone, Antonello Rosato, Bertrand Le Saux, Massimo Panella, Paolo Gamba, Silvia L. Ullo
Date: 2024-08-12 00:00:00

Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing data with complex relational structures but suffer from limitations such as high computational complexity and over-smoothing in large-scale applications. Quantum computing, leveraging principles like superposition and entanglement, offers a pathway to enhanced computational capabilities. This paper critically reviews the state-of-the-art in QGNNs, exploring various architectures. We discuss their applications across diverse fields such as high-energy physics, molecular chemistry, finance and earth sciences, highlighting the potential for quantum advantage. Additionally, we address the significant challenges faced by QGNNs, including noise, decoherence, and scalability issues, proposing potential strategies to mitigate these problems. This comprehensive review aims to provide a foundational understanding of QGNNs, fostering further research and development in this promising interdisciplinary field.
Endogeneity Corrections in Binary Outcome Models with Nonlinear Transformations: Identification and Inference
Authors: Alexander Mayer, Dominik Wied
Date: 2024-08-13 00:00:00

For binary outcome models, an endogeneity correction based on nonlinear rank-based transformations is proposed. Identification without external instruments is achieved under one of two assumptions: either the endogenous regressor is a nonlinear function of one component of the error term, conditional on the exogenous regressors, or the dependence between the endogenous and exogenous regressors is nonlinear. Under these conditions, we prove consistency and asymptotic normality. Monte Carlo simulations and an application on German insolvency data illustrate the usefulness of the method.
Estimation and Inference of Average Treatment Effect in Percentage Points under Heterogeneity
Authors: Ying Zeng
Date: 2024-08-13 00:00:00

In semi-log regression models with heterogeneous treatment effects, the average treatment effect (ATE) in log points and its exponential transformation minus one underestimate the ATE in percentage points. I propose new estimation and inference methods for the ATE in percentage points, with inference utilizing the Fenton-Wilkinson approximation. These methods are particularly relevant for staggered difference-in-differences designs, where treatment effects often vary across groups and periods. I prove the methods' large-sample properties and demonstrate their finite-sample performance through simulations, revealing substantial discrepancies between conventional and proposed measures. Two empirical applications further underscore the practical importance of these methods.
An unbounded intensity model for point processes
Authors: Kim Christensen, Alexei Kolokolov
Date: 2024-08-12 00:00:00
Tasks: model, Point Processes

We develop a model for point processes on the real line, where the intensity can be locally unbounded without inducing an explosion. In contrast to an orderly point process, for which the probability of observing more than one event over a short time interval is negligible, the bursting intensity causes an extreme clustering of events around the singularity. We propose a nonparametric approach to detect such bursts in the intensity. It relies on a heavy traffic condition, which admits inference for point processes over a finite time interval. With Monte Carlo evidence, we show that our testing procedure exhibits size control under the null, whereas it has high rejection rates under the alternative. We implement our approach on high-frequency data for the EUR/USD spot exchange rate, where the test statistic captures abnormal surges in trading activity. We detect a nontrivial amount of intensity bursts in these data and describe their basic properties. Trading activity during an intensity burst is positively related to volatility, illiquidity, and the probability of observing a drift burst. The latter effect is reinforced if the order flow is imbalanced or the price elasticity of the limit order book is large.
Correcting invalid regression discontinuity designs with multiple time period data
Authors: Dor Leventer, Daniel Nevo
Date: 2024-08-11 00:00:00
Tasks: Causal Inference, regression, valid

Regression Discontinuity (RD) designs rely on the continuity of potential outcome means at the cutoff, but this assumption often fails when other treatments or policies are implemented at this cutoff. We characterize the bias in sharp and fuzzy RD designs due to violations of continuity, and develop a general identification framework that leverages multiple time periods to estimate local effects on the (un)treated. We extend the framework to settings with carry-over effects and time-varying running variables, highlighting additional assumptions needed for valid causal inference. We propose an estimation framework that extends the conventional and bias-corrected single-period local linear regression framework to multiple periods and different sampling schemes, and study its finite-sample performance in simulations. Finally, we revisit a prior study on fiscal rules in Italy to illustrate the practical utility of our approach.
Bank Cost Efficiency and Credit Market Structure Under a Volatile Exchange Rate
Authors: Mikhail Mamonov, Christopher Parmeter, Artem Prokhorov
Date: 2024-08-11 00:00:00

We study the impact of exchange rate volatility on cost efficiency and market structure in a cross-section of banks that have non-trivial exposures to foreign currency (FX) operations. We use unique data on quarterly revaluations of FX assets and liabilities (Revals) that Russian banks were reporting between 2004 Q1 and 2020 Q2. {\it First}, we document that Revals constitute the largest part of the banks' total costs, 26.5\% on average, with considerable variation across banks. {\it Second}, we find that stochastic estimates of cost efficiency are both severely downward biased -- by 30\% on average -- and generally not rank preserving when Revals are ignored, except for the tails, as our nonparametric copulas reveal. To ensure generalizability to other emerging market economies, we suggest a two-stage approach that does not rely on Revals but is able to shrink the downward bias in cost efficiency estimates by two-thirds. {\it Third}, we show that Revals are triggered by the mismatch in the banks' FX operations, which, in turn, is driven by household FX deposits and the instability of Ruble's exchange rate. {\it Fourth}, we find that the failure to account for Revals leads to the erroneous conclusion that the credit market is inefficient, which is driven by the upper quartile of the banks' distribution by total assets. Revals have considerable negative implications for financial stability which can be attenuated by the cross-border diversification of bank assets.
Change-Point Detection in Time Series Using Mixed Integer Programming
Authors: Artem Prokhorov, Peter Radchenko, Alexander Semenov, Anton Skrobotov
Date: 2024-08-11 00:00:00
Tasks: Change Point Detection, regression, Time Series, Time Series Regression

We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framework is constructed based on the least squares problem subject to a penalty on the number of breakpoints. We restate the $l_0$-penalized regression problem as a quadratic programming problem with integer- and real-valued arguments and show that MIO is capable of finding provably optimal solutions using a well-known optimization solver. Compared to the popular $l_1$-penalized regression (LASSO) and other classical methods, the MIO framework permits simultaneous estimation of the number and location of structural breaks as well as regression coefficients, while accommodating the option of specifying a given or minimal number of breaks. We derive the asymptotic properties of the estimator and demonstrate its effectiveness through extensive numerical experiments, confirming a more accurate estimation of multiple breaks as compared to popular non-MIO alternatives. Two empirical examples demonstrate usefulness of the framework in applications from business and economic statistics.
ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments
Authors: Ke Sun, Linglong Kong, Hongtu Zhu, Chengchun Shi
Date: 2024-08-09 00:00:00
Tasks: Time Series

Online experiments %in which experimental units receive a sequence of treatments over time are frequently employed in many technological companies to evaluate the performance of a newly developed policy, product, or treatment relative to a baseline control. In many applications, the experimental units receive a sequence of treatments over time. To handle these time-dependent settings, existing A/B testing solutions typically assume a fully observable experimental environment that satisfies the Markov condition. However, this assumption often does not hold in practice. This paper studies the optimal design for A/B testing in partially observable online experiments. We introduce a controlled (vector) autoregressive moving average model to capture partial observability. We introduce a small signal asymptotic framework to simplify the calculation of asymptotic mean squared errors of average treatment effect estimators under various designs. We develop two algorithms to estimate the optimal design: one utilizing constrained optimization and the other employing reinforcement learning. We demonstrate the superior performance of our designs using two dispatch simulators that realistically mimic the behaviors of drivers and passengers to create virtual environments, along with two real datasets from a ride-sharing company. A Python implementation of our proposal is available at https://github.com/datake/ARMADesign.
What are the real implications for $CO_2$ as generation from renewables increases?
Authors: Dhruv Suri, Jacques de Chalendar, Ines Azevedo
Date: 2024-08-09 00:00:00

Wind and solar electricity generation account for 14% of total electricity generation in the United States and are expected to continue to grow in the next decades. In low carbon systems, generation from renewable energy sources displaces conventional fossil fuel power plants resulting in lower system-level emissions and emissions intensity. However, we find that intermittent generation from renewables changes the way conventional thermal power plants operate, and that the displacement of generation is not 1 to 1 as expected. Our work provides a method that allows policy and decision makers to continue to track the effect of additional renewable capacity and the resulting thermal power plant operational responses.
Vela: A Data-Driven Proposal for Joint Collaboration in Space Exploration
Authors: Holly M. Dinkel, Jason K. Cornelius
Date: 2024-08-08 00:00:00

The UN Office of Outer Space Affairs identifies synergy of space development activities and international cooperation through data and infrastructure sharing in their Sustainable Development Goal 17 (SDG17). Current multilateral space exploration paradigms, however, are divided between the Artemis and the Roscosmos-CNSA programs to return to the moon and establish permanent human settlements. As space agencies work to expand human presence in space, economic resource consolidation in pursuit of technologically ambitious space expeditions is the most sensible path to accomplish SDG17. This paper compiles a budget dataset for the top five federally-funded space agencies: CNSA, ESA, JAXA, NASA, and Roscosmos. Using time-series econometric anslysis methods in STATA, this work analyzes each agency's economic contributions toward space exploration. The dataset results are used to propose a multinational space mission, Vela, for the development of an orbiting space station around Mars in the late 2030s. Distribution of economic resources and technological capabilities by the respective space programs are proposed to ensure programmatic redundancy and increase the odds of success on the given timeline.
Semiparametric Estimation of Individual Coefficients in a Dyadic Link Formation Model Lacking Observable Characteristics
Authors: L. Sanna Stephan
Date: 2024-08-08 00:00:00

Dyadic network formation models have wide applicability in economic research, yet are difficult to estimate in the presence of individual specific effects and in the absence of distributional assumptions regarding the model noise component. The availability of (continuously distributed) individual or link characteristics generally facilitates estimation. Yet, while data on social networks has recently become more abundant, the characteristics of the entities involved in the link may not be measured. Adapting the procedure of \citet{KS}, I propose to use network data alone in a semiparametric estimation of the individual fixed effect coefficients, which carry the interpretation of the individual relative popularity. This entails the possibility to anticipate how a new-coming individual will connect in a pre-existing group. The estimator, needed for its fast convergence, fails to implement the monotonicity assumption regarding the model noise component, thereby potentially reversing the order if the fixed effect coefficients. This and other numerical issues can be conveniently tackled by my novel, data-driven way of normalising the fixed effects, which proves to outperform a conventional standardisation in many cases. I demonstrate that the normalised coefficients converge both at the same rate and to the same limiting distribution as if the true error distribution was known. The cost of semiparametric estimation is thus purely computational, while the potential benefits are large whenever the errors have a strongly convex or strongly concave distribution.
Robust and Efficient Estimation of Potential Outcome Means under Random Assignment
Authors: Akanksha Negi, Jeffrey M. Wooldridge
Date: 2020-10-05 00:00:00
Tasks: regression

We study efficiency improvements in randomized experiments for estimating a vector of potential outcome means using regression adjustment (RA) when there are more than two treatment levels. We show that linear RA which estimates separate slopes for each assignment level is never worse, asymptotically, than using the subsample averages. We also show that separate RA improves over pooled RA except in the obvious case where slope parameters in the linear projections are identical across the different assignment levels. We further characterize the class of nonlinear RA methods that preserve consistency of the potential outcome means despite arbitrary misspecification of the conditional mean functions. Finally, we apply these regression adjustment techniques to efficiently estimate the lower bound mean willingness to pay for an oil spill prevention program in California.
Hungry Professors? Decision Biases Are Less Widespread than Previously Thought
Authors: Katja Bergonzoli, Laurent Bieri, Dominic Rohner, Christian Zehnder
Date: 2024-08-12 00:00:00
Tasks: Scheduling

In many situations people make sequences of similar, but unrelated decisions. Such decision sequences are prevalent in many important contexts including judicial judgments, loan approvals, college admissions, and athletic competitions. A growing literature claims that decisions in such sequences may be severely biased because decision outcomes seem to be systematically affected by the scheduling. In particular, it has been argued that mental depletion leads to harsher decisions before food breaks and that the ``law of small numbers'' induces decisions to be negatively auto-correlated (i.e. favorable decisions are followed by unfavorable ones and vice versa). These findings have attracted much academic and media attention and it has been suspected that they may only represent the ``tip of the iceberg''. However, voices of caution point out that existing studies may suffer from serious limitations, because the decision order is not randomly determined, other influencing factors are hard to exclude, or direct evidence for the underlying mechanisms is not available. We exploit a large-scale natural experiment in a context in which the previous literature would predict the presence of scheduling biases. Specifically, we investigate whether the grades of randomly scheduled oral exams in Law School depend on the position of the exam in the sequence. Our rich data enables us to filter-out student, professor, day, and course-specific features. Our results contradict the previous findings and suggest that caution is advised when generalizing from previous studies for policy advice.