pwc-archive/papers-with-abstracts huggingface dataset:
Active Front-End Rectifier Modeling and Analysis
Authors: Aidar Zhetessov, Nathan Petersen
Date: 2023-11-23 00:00:00
Tasks: Scheduling

Modeling and comprehensive analysis of the Active Front-End (AFE) rectifier is addressed in this work. The particular emphasis is on the comparison of two state feedback control frameworks: state-space and frequency domain. The equivalence of both frameworks for the AFE case was shown. Moreover, analytical relations between control gains and system parameters was derived, allowing for explicit gain scheduling as a function of operating point over the mission profile. The state observation of grid currents has also been addressed. Finally, the work shows the example of robustness evaluation in presence of system variation using the Lyapunov functions approach. The system in nominal conditions was shown to be robust with respect to $50\%$ AC hardware parameter variations.
Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
Authors: Namkyeong Lee, Heewoong Noh, Sungwon Kim, Dongmin Hyun, Gyoung S. Na, Chanyoung Park
Date: 2023-10-24 00:00:00
Proceeding: NeurIPS 2023 11

The density of states (DOS) is a spectral property of crystalline materials, which provides fundamental insights into various characteristics of the materials. While previous works mainly focus on obtaining high-quality representations of crystalline materials for DOS prediction, we focus on predicting the DOS from the obtained representations by reflecting the nature of DOS: DOS determines the general distribution of states as a function of energy. That is, DOS is not solely determined by the crystalline material but also by the energy levels, which has been neglected in previous works. In this paper, we propose to integrate heterogeneous information obtained from the crystalline materials and the energies via a multi-modal transformer, thereby modeling the complex relationships between the atoms in the crystalline materials and various energy levels for DOS prediction. Moreover, we propose to utilize prompts to guide the model to learn the crystal structural system-specific interactions between crystalline materials and energies. Extensive experiments on two types of DOS, i.e., Phonon DOS and Electron DOS, with various real-world scenarios demonstrate the superiority of DOSTransformer. The source code for DOSTransformer is available at https://github.com/HeewoongNoh/DOSTransformer.
Identifying percolation phase transitions with unsupervised learning based on largest clusters
Authors: Dian Xu, Shanshan Wang, Weibing Deng, Feng Gao, Wei Li, Jianmin Shen
Date: 2023-11-20 00:00:00

The application of machine learning in the study of phase transitions has achieved remarkable success in both equilibrium and non-equilibrium systems. It is widely recognized that unsupervised learning can retrieve phase transition information through hidden variables. However, using unsupervised methods to identify the critical point of percolation models has remained an intriguing challenge. This paper suggests that, by inputting the largest cluster rather than the original configuration into the learning model, unsupervised learning can indeed predict the critical point of the percolation model. Furthermore, we observe that when the largest cluster configuration is randomly shuffled-altering the positions of occupied sites or bonds-there is no significant difference in the output compared to learning the largest cluster configuration directly. This finding suggests a more general principle: unsupervised learning primarily captures particle density, or more specifically, occupied site density. However, shuffling does impact the formation of the largest cluster, which is directly related to phase transitions. As randomness increases, we observe that the correlation length tends to decrease, providing direct evidence of this relationship. We also propose a method called Fake Finite Size Scaling (FFSS) to calculate the critical value, which improves the accuracy of fitting to a great extent.
Delayed loss of stability of periodic travelling waves: insights from the analysis of essential spectra
Authors: Lukas Eigentler, Mattia Sensi
Date: 2023-11-17 00:00:00

Periodic travelling waves (PTW) are a common solution type of partial differential equations. Such models exhibit multistability of PTWs, typically visualised through the Busse balloon, and parameter changes typically lead to a cascade of wavelength changes through the Busse balloon. In the past, the stability boundaries of the Busse balloon have been used to predict such wavelength changes. Here, motivated by anecdotal evidence from previous work, we provide compelling evidence that the Busse balloon provides insufficient information to predict wavelength changes due to a delayed loss of stability phenomenon. Using two different reaction-advection-diffusion systems, we relate the delay that occurs between the crossing of a stability boundary in the Busse balloon and the occurrence of a wavelength change to features of the essential spectrum of the destabilised PTW. This leads to a predictive framework that can estimate the order of magnitude of such a time delay, which provides a novel ``early warning sign'' for pattern destabilization. We illustrate the implementation of the predictive framework to predict under what conditions a wavelength change of a PTW occurs.
Coarse-Grained Configurational Polymer Fingerprints for Property Prediction using Machine Learning
Authors: Ishan Kumar, Prateek K Jha
Date: 2023-11-20 00:00:00
Tasks: Property Prediction

In this work, we present a method to generate a configurational level fingerprint for polymers using the Bead-Spring-Model. Unlike some of the previous fingerprinting approaches that employ monomer-level information where atomistic descriptors are computed using quantum chemistry calculations, this approach incorporates configurational information from a coarse-grained model of a long polymer chain. The proposed approach may be advantageous for the study of behavior resulting from large molecular weights. To create this fingerprint, we make use of two kinds of descriptors. First, we calculate certain geometric descriptors like Re2, Rg2 etc. and label them as Calculated Descriptors. Second, we generate a set of data-driven descriptors using an unsupervised autoencoder model and call them Learnt Descriptors. Using a combination of both of them, we are able to learn mappings from the structure to various properties of the polymer chain by training ML models. We test our fingerprint to predict the probability of occurrence of a configuration at equilibrium, which is approximated by a simple linear relationship between the instantaneous internal energy and equilibrium average internal energy.
Wavelength-multiplexed Multi-mode EUV Reflection Ptychography based on Automatic-Differentiation
Authors: Yifeng Shao, Sven Weerdenburg, Jacob Seifert, H. Paul Urbach, Allard P. Mosk, Wim Coene
Date: 2023-11-24 00:00:00
Tasks: GPU

Ptychographic extreme ultraviolet (EUV) diffractive imaging has emerged as a promising candidate for the next-generation metrology solutions in the semiconductor industry, as it can image wafer samples in reflection geometry at the nanoscale. This technique has surged attention recently, owing to the significant progress in high-harmonic generation (HHG) EUV sources and advancements in both hardware and software for computation. In this study, a novel algorithm is introduced and tested, which enables wavelength-multiplexed reconstruction that enhances the measurement throughput and introduces data diversity, allowing the accurate characterisation of sample structures. To tackle the inherent instabilities of the HHG source, a modal approach was adopted, which represents the cross-density function of the illumination by a series of mutually incoherent and independent spatial modes. The proposed algorithm was implemented on a mainstream machine learning platform, which leverages automatic differentiation to manage the drastic growth in model complexity and expedites the computation using GPU acceleration. By optimising over 200 million parameters, we demonstrate the algorithm's capacity to accommodate experimental uncertainties and achieve a resolution approaching the diffraction limit in reflection geometry. The reconstruction of wafer samples with 20-nm heigh patterned gold structures on a silicon substrate highlights our ability to handle complex physical interrelations involving a multitude of parameters. These results establish ptychography as an efficient and accurate metrology tool.
Beyond the aggregated paradigm: phenology and structure in mutualistic networks
Authors: Clàudia Payrató-Borràs, Carlos Gracia-Lázaro, Laura Hernández, Yamir Moreno
Date: 2023-11-25 00:00:00

Mutualistic interactions, where species interact to obtain mutual benefits, constitute an essential component of natural ecosystems. The use of ecological networks to represent the species and their ecological interactions allows the study of structural and dynamic patterns common to different ecosystems. However, by neglecting the temporal dimension of mutualistic communities, relevant insights into the organization and functioning of natural ecosystems can be lost. Therefore, it is crucial to incorporate empirical phenology -- the cycles of species' activity within a season -- to fully understand the effects of temporal variability on network architecture. In this paper, by using two empirical datasets together with a set of synthetic models, we propose a framework to characterize phenology on ecological networks and assess the effect of temporal variability. Analyses reveal that non-trivial information is missed when portraying the network of interactions as static, which leads to overestimating the value of fundamental structural features. We discuss the implications of our findings for mutualistic relationships and intra-guild competition for common resources. We show that recorded interactions and species' activity duration are pivotal factors in accurately replicating observed patterns within mutualistic communities. Furthermore, our exploration of synthetic models underscores the system-specific character of the mechanisms driving phenology, increasing our understanding of the complexities of natural ecosystems.
Population mobility, well-mixed clustering and disease spread: a look at COVID-19 Spread in the United States and preventive policy insights
Authors: David Lyver, Mihai Nica, Corentin Cot, Giacomo Cacciapaglia, Zahra Mohammadi, Edward W. Thommes, Monica-Gabriela Cojocaru
Date: 2023-11-25 00:00:00
Tasks: Epidemiology

The epidemiology of pandemics is classically viewed using geographical and political borders; however, these artificial divisions can result in a misunderstanding of the current epidemiological state within a given region. To improve upon current methods, we propose a clustering algorithm which is capable of recasting regions into well-mixed clusters such that they have a high level of interconnection while minimizing the external flow of the population towards other clusters. Moreover, we analyze and identify so called core clusters, clusters that retain their features over time (temporally stable) and independent of the presence or absence of policy measures. In order to demonstrate the capabilities of this algorithm, we use US county-level cellular mobility data to divide the country into such clusters. Herein, we show a more granular spread of SARS-CoV-2 throughout the first weeks of the pandemic. Moreover, we are able to identify areas (groups of counties) that were experiencing above average levels of transmission within a state, as well as pan-state areas (clusters overlapping more than one state) with very similar disease spread. Therefore, our method enables policymakers to make more informed decisions on the use of public health interventions within their jurisdiction, as well as guide collaboration with surrounding regions to benefit the general population in controlling the spread of communicable diseases.
Robust Conditional Wald Inference for Over-Identified IV
Authors: David S. Lee, Justin McCrary, Marcelo J. Moreira, Jack Porter, Luther Yap
Date: 2023-11-27 00:00:00

For the over-identified linear instrumental variables model, researchers commonly report the 2SLS estimate along with the robust standard error and seek to conduct inference with these quantities. If errors are homoskedastic, one can control the degree of inferential distortion using the first-stage F critical values from Stock and Yogo (2005), or use the robust-to-weak instruments Conditional Wald critical values of Moreira (2003). If errors are non-homoskedastic, these methods do not apply. We derive the generalization of Conditional Wald critical values that is robust to non-homoskedastic errors (e.g., heteroskedasticity or clustered variance structures), which can also be applied to nonlinear weakly-identified models (e.g. weakly-identified GMM).
Valid Wald Inference with Many Weak Instruments
Authors: Luther Yap
Date: 2023-11-27 00:00:00
Tasks: valid

This paper proposes three novel test procedures that yield valid inference in an environment with many weak instrumental variables (MWIV). It is observed that the t statistic of the jackknife instrumental variable estimator (JIVE) has an asymptotic distribution that is identical to the two-stage-least squares (TSLS) t statistic in the just-identified environment. Consequently, test procedures that were valid for TSLS t are also valid for the JIVE t. Two such procedures, i.e., VtF and conditional Wald, are adapted directly. By exploiting a feature of MWIV environments, a third, more powerful, one-sided VtF-based test procedure can be obtained.
On Quantile Treatment Effects, Rank Similarity, and Variation of Instrumental Variables
Authors: Sukjin Han, Haiqing Xu
Date: 2023-11-27 00:00:00
Tasks: counterfactual

This paper investigates how certain relationship between observed and counterfactual distributions serves as an identifying condition for treatment effects when the treatment is endogenous, and shows that this condition holds in a range of nonparametric models for treatment effects. To this end, we first provide a novel characterization of the prevalent assumption restricting treatment heterogeneity in the literature, namely rank similarity. Our characterization demonstrates the stringency of this assumption and allows us to relax it in an economically meaningful way, resulting in our identifying condition. It also justifies the quest of richer exogenous variations in the data (e.g., multi-valued or multiple instrumental variables) in exchange for weaker identifying conditions. The primary goal of this investigation is to provide empirical researchers with tools that are robust and easy to implement but still yield tight policy evaluations.
Causal Models for Longitudinal and Panel Data: A Survey
Authors: Dmitry Arkhangelsky, Guido Imbens
Date: 2023-11-26 00:00:00
Tasks: Survey

In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, emphasizing practical advice for empirical researchers. It pays particular attention to heterogeneity in the causal effects, often in situations where few units are treated and with particular structures on the assignment pattern. The literature has extended earlier work on difference-in-differences or two-way-fixed-effect estimators. It has more generally incorporated factor models or interactive fixed effects. It has also developed novel methods using synthetic control approaches.
An Identification and Dimensionality Robust Test for Instrumental Variables Models
Authors: Manu Navjeevan
Date: 2023-11-25 00:00:00

Using modifications of Lindeberg's interpolation technique, I propose a new identification-robust test for the structural parameter in a heteroskedastic instrumental variables model. While my analysis allows the number of instruments to be much larger than the sample size, it does not require many instruments, making my test applicable in settings that have not been well studied. Instead, the proposed test statistic has a limiting chi-squared distribution so long as an auxiliary parameter can be consistently estimated. This is possible using machine learning methods even when the number of instruments is much larger than the sample size. To improve power, a simple combination with the sup-score statistic of Belloni et al. (2012) is proposed. I point out that first-stage F-statistics calculated on LASSO selected variables may be misleading indicators of identification strength and demonstrate favorable performance of my proposed methods in both empirical data and simulation study.
A Review of Cross-Sectional Matrix Exponential Spatial Models
Authors: Ye Yang, Osman Dogan, Suleyman Taspinar, Fei Jin
Date: 2023-11-24 00:00:00
Tasks: Econometrics, Model Selection

The matrix exponential spatial models exhibit similarities to the conventional spatial autoregressive model in spatial econometrics but offer analytical, computational, and interpretive advantages. This paper provides a comprehensive review of the literature on the estimation, inference, and model selection approaches for the cross-sectional matrix exponential spatial models. We discuss summary measures for the marginal effects of regressors and detail the matrix-vector product method for efficient estimation. Our aim is not only to summarize the main findings from the spatial econometric literature but also to make them more accessible to applied researchers. Additionally, we contribute to the literature by introducing some new results. We propose an M-estimation approach for models with heteroskedastic error terms and demonstrate that the resulting M-estimator is consistent and has an asymptotic normal distribution. We also consider some new results for model selection exercises. In a Monte Carlo study, we examine the finite sample properties of various estimators from the literature alongside the M-estimator.
Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash
Authors: Yixin Tang, Yicong Lin, Navdeep S. Sahni
Date: 2023-11-10 00:00:00
Tasks: Decision Making

This paper investigates an approach to both speed up business decision-making and lower the cost of learning through experimentation by factorizing business policies and employing fractional factorial experimental designs for their evaluation. We illustrate how this method integrates with advances in the estimation of heterogeneous treatment effects, elaborating on its advantages and foundational assumptions. We empirically demonstrate the implementation and benefits of our approach and assess its validity in evaluating consumer promotion policies at DoorDash, which is one of the largest delivery platforms in the US. Our approach discovers a policy with 5% incremental profit at 67% lower implementation cost.
Application of Process Mining and Sequence Clustering in Recognizing an Industrial Issue
Authors: Hamza Saad
Date: 2023-11-26 00:00:00
Tasks: Clustering

Process mining has become one of the best programs that can outline the event logs of production processes in visualized detail. We have addressed the important problem that easily occurs in the industrial process called Bottleneck. The analysis process was focused on extracting the bottlenecks in the production line to improve the flow of production. Given enough stored history logs, the field of process mining can provide a suitable answer to optimize production flow by mitigating bottlenecks in the production stream. Process mining diagnoses the productivity processes by mining event logs, this can help to expose the opportunities to optimize critical production processes. We found that there is a considerable bottleneck in the process because of the weaving activities. Through discussions with specialists, it was agreed that the main problem in the weaving processes, especially machines that were exhausted in overloading processes. The improvement in the system has measured by teamwork; the cycle time for process has improved to 91%, the worker's performance has improved to 96%,product quality has improved by 85%, and lead time has optimized from days and weeks to hours.
Learning Arousal-Valence Representation from Categorical Emotion Labels of Speech
Authors: Enting Zhou, You Zhang, Zhiyao Duan
Date: 2023-11-24 00:00:00
Tasks: Dimensionality Reduction, Emotion Classification, regression, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Dimensional representations of speech emotions such as the arousal-valence (AV) representation provide a continuous and fine-grained description and control than their categorical counterparts. They have wide applications in tasks such as dynamic emotion understanding and expressive text-to-speech synthesis. Existing methods that predict the dimensional emotion representation from speech cast it as a supervised regression task. These methods face data scarcity issues, as dimensional annotations are much harder to acquire than categorical labels. In this work, we propose to learn the AV representation from categorical emotion labels of speech. We start by learning a rich and emotion-relevant high-dimensional speech feature representation using self-supervised pre-training and emotion classification fine-tuning. This representation is then mapped to the 2D AV space according to psychological findings through anchored dimensionality reduction. Experiments show that our method achieves a Concordance Correlation Coefficient (CCC) performance comparable to state-of-the-art supervised regression methods on IEMOCAP without leveraging ground-truth AV annotations during training. This validates our proposed approach on AV prediction. Furthermore, visualization of AV predictions on MEAD and EmoDB datasets shows the interpretability of the learned AV representations.
Machine-to-Machine Transfer Function in Deep Learning-Based Quantitative Ultrasound
Authors: Ufuk Soylu, Michael L. Oelze
Date: 2023-11-27 00:00:00

A Transfer Function approach was recently demonstrated to mitigate data mismatches at the acquisition level for a single ultrasound scanner in deep learning (DL) based quantitative ultrasound (QUS). As a natural progression, we further investigate the transfer function approach and introduce a Machine-to-Machine (M2M) Transfer Function, which possesses the ability to mitigate data mismatches at a machine level, i.e., mismatches between two scanners over the same frequency band. This ability opens the door to unprecedented opportunities for reducing DL model development costs, enabling the combination of data from multiple sources or scanners, or facilitating the transfer of DL models between machines with ease. We tested the proposed method utilizing a SonixOne machine and a Verasonics machine. In the experiments, we used a L9-4 array and conducted two types of acquisitions to obtain calibration data: stable and free-hand, using two different calibration phantoms. Without the proposed calibration method, the mean classification accuracy when applying a model on data acquired from one system to data acquired from another system was approximately 50%, and the mean AUC was about 0.40. With the proposed method, mean accuracy increased to approximately 90%, and the AUC rose to the 0.99. Additional observations include that shifts in statistics for the z-score normalization had a significant impact on performance. Furthermore, the choice of the calibration phantom played an important role in the proposed method. Additionally, robust implementation inspired by Wiener filtering provided an effective method for transferring the domain from one machine to another machine, and it can succeed using just a single calibration view without the need for multiple independent calibration frames.