pwc-archive/papers-with-abstracts huggingface dataset:
Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation
Authors: Zoey Liu, Emily Prud'hommeaux
Date: 2022-01-05 00:00:00

Common designs of model evaluation typically focus on monolingual settings, where different models are compared according to their performance on a single data set that is assumed to be representative of all possible data for the task at hand. While this may be reasonable for a large data set, this assumption is difficult to maintain in low-resource scenarios, where artifacts of the data collection can yield data sets that are outliers, potentially making conclusions about model performance coincidental. To address these concerns, we investigate model generalizability in crosslinguistic low-resource scenarios. Using morphological segmentation as the test case, we compare three broad classes of models with different parameterizations, taking data from 11 languages across 6 language families. In each experimental setting, we evaluate all models on a first data set, then examine their performance consistency when introducing new randomly sampled data sets with the same size and when applying the trained models to unseen test sets of varying sizes. The results demonstrate that the extent of model generalization depends on the characteristics of the data set, and does not necessarily rely heavily on the data set size. Among the characteristics that we studied, the ratio of morpheme overlap and that of the average number of morphemes per word between the training and test sets are the two most prominent factors. Our findings suggest that future work should adopt random sampling to construct data sets with different sizes in order to make more responsible claims about model evaluation.
KUDO Interpreter Assist: Automated Real-time Support for Remote Interpretation
Authors: claudio Fantinuoli, Giulia Marchesini, David Landan, Lukas Horak
Date: 2022-01-05 00:00:00

High-quality human interpretation requires linguistic and factual preparation as well as the ability to retrieve information in real-time. This situation becomes particularly relevant in the context of remote simultaneous interpreting (RSI) where time-to-event may be short, posing new challenges to professional interpreters and their commitment to delivering high-quality services. In order to mitigate these challenges, we present Interpreter Assist, a computer-assisted interpreting tool specifically designed for the integration in RSI scenarios. Interpreter Assist comprises two main feature sets: an automatic glossary creation tool and a real-time suggestion system. In this paper, we describe the overall design of our tool, its integration into the typical RSI workflow, and the results achieved on benchmark tests both in terms of quality and relevance of glossary creation as well as in precision and recall of the real-time suggestion feature.
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation
Authors: Somayajulu L. N. Dhulipala, Michael D. Shields, Promit Chakroborty, Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Vincent M. Laboure, Zachary M. Prince, Chandrakanth Bolisetti, Yifeng Che
Date: 2022-01-06 00:00:00
Tasks: Active Learning, Prediction

Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However, TRISO failure probabilities are small and the associated computational models are expensive. We used coupled active learning, multifidelity modeling, and subset simulation to estimate the failure probabilities of TRISO fuels using several 1D and 2D models. With multifidelity modeling, we replaced expensive high-fidelity (HF) model evaluations with information fusion from two low-fidelity (LF) models. For the 1D TRISO models, we considered three multifidelity modeling strategies: only Kriging, Kriging LF prediction plus Kriging correction, and deep neural network (DNN) LF prediction plus Kriging correction. While the results across these multifidelity modeling strategies compared satisfactorily, strategies employing information fusion from two LF models consistently called the HF model least often. Next, for the 2D TRISO model, we considered two multifidelity modeling strategies: DNN LF prediction plus Kriging correction (data-driven) and 1D TRISO LF prediction plus Kriging correction (physics-based). The physics-based strategy, as expected, consistently required the fewest calls to the HF model. However, the data-driven strategy had a lower overall simulation time since the DNN predictions are instantaneous, and the 1D TRISO model requires a non-negligible simulation time.
An Homogeneous Unbalanced Regularized Optimal Transport model with applications to Optimal Transport with Boundary
Authors: Théo Lacombe
Date: 2022-01-06 00:00:00

This work studies how the introduction of the entropic regularization term in unbalanced Optimal Transport (OT) models may alter their homogeneity with respect to the input measures. We observe that in common settings (including balanced OT and unbalanced OT with Kullback-Leibler divergence to the marginals), although the optimal transport cost itself is not homogeneous, optimal transport plans and the so-called Sinkhorn divergences are indeed homogeneous. However, homogeneity does not hold in more general Unbalanced Regularized Optimal Transport (UROT) models, for instance those using the Total Variation as divergence to the marginals. We propose to modify the entropic regularization term to retrieve an UROT model that is homogeneous while preserving most properties of the standard UROT model. We showcase the importance of using our Homogeneous UROT (HUROT) model when it comes to regularize Optimal Transport with Boundary, a transportation model involving a spatially varying divergence to the marginals for which the standard (inhomogeneous) UROT model would yield inappropriate behavior.
Spectral Clustering with Variance Information for Group Structure Estimation in Panel Data
Authors: Lu Yu, Jiaying Gu, Stanislav Volgushev
Date: 2022-01-05 00:00:00
Tasks: Clustering

Consider a panel data setting where repeated observations on individuals are available. Often it is reasonable to assume that there exist groups of individuals that share similar effects of observed characteristics, but the grouping is typically unknown in advance. We first conduct a local analysis which reveals that the variances of the individual coefficient estimates contain useful information for the estimation of group structure. We then propose a method to estimate unobserved groupings for general panel data models that explicitly account for the variance information. Our proposed method remains computationally feasible with a large number of individuals and/or repeated measurements on each individual. The developed ideas can also be applied even when individual-level data are not available and only parameter estimates together with some quantification of estimation uncertainty are given to the researcher. A thorough simulation study demonstrates superior performance of our method than existing methods and we apply the method to two empirical applications.
Polytope-form games and Index/Degree Theories for Extensive-form games
Authors: Lucas Pahl
Date: 2022-01-06 00:00:00
Tasks: Form

We present an index theory of equilibria for extensive form games. This requires developing an index theory for games where the strategy sets of players are general polytopes and their payoff functions are multiaffine in the product of these polytopes. Such polytopes arise from identifying (topologically) equivalent mixed strategies of a normal form game.
Asymptotic Behavior of Inter-Event Times in Planar Systems under Event-Triggered Control
Authors: Anusree Rajan, Pavankumar Tallapragada
Date: 2022-01-06 00:00:00

This paper analyzes the asymptotic behavior of inter-event times in planar linear systems, under event-triggered control with a general class of scale-invariant event triggering rules. In this setting, the inter-event time is a function of the angle of the state at an event. This viewpoint allows us to analyze the inter-event times by studying the fixed points of the angle map, which represents the evolution of the angle of the state from one event to the next. We provide a sufficient condition for the convergence or non-convergence of inter-event times to a steady state value under a scale-invariant event-triggering rule. Following up on this, we further analyze the inter-event time behavior in the special case of threshold based event-triggering rule and we provide various conditions for convergence or non-convergence of inter-event times to a constant. We also analyze the asymptotic average inter-event time as a function of the angle of the initial state of the system. With the help of ergodic theory, we provide a sufficient condition for the asymptotic average inter-event time to be a constant for all non-zero initial states of the system. Then, we consider a special case where the angle map is an orientation-preserving homeomorphism. Using rotation theory, we comment on the asymptotic behavior of the inter-event times, including on whether the inter-event times converge to a periodic sequence. We illustrate the proposed results through numerical simulations.
Investigation of fast-NMPC and deep learning approach in fixed-point-based hierarchical control
Authors: Xuan-Huy Pham, Mazen Alamir, François Bonne
Date: 2022-01-06 00:00:00

This paper explores some variations of a hierarchical control framework that has been recently proposed. The framework is dedicated to control a network of interconnected subsystems such as the ones describing cryogenic processes or power plants. Recent investigations showed that handling constraints and nonlinearities might challenge the real-time feasibility of the approach. This paper investigates and combine two successful directions, namely, the use of truncated fast gradient and deep neural networks based controller modeling in order to reduce the computation time of the most critical subsystem. It is also shown that by doing so, the control updating period can be drastically reduced and the closed-loop performances highly improved. The paper can therefore be seen as a concrete implementation and validation of some key ideas in real-time distributed NMPC design. All the concepts are validated using the realistic and challenging example of real-life cryogenic refrigerator.
Event-based EV Charging Scheduling in A Microgrid of Buildings
Authors: Qilong Huang, Li Yang, Chen Hou, Zhiyong Zeng, Yaowen Qi
Date: 2022-01-06 00:00:00
Tasks: Scheduling

With the popularization of the electric vehicles (EVs), EV charging demand is becoming an important load in the building. Considering the mobility of EVs from building to building and their uncertain charging demand, it is of great practical interest to control the EV charging process in a microgrid of buildings to optimize the total operation cost while ensuring the transmission safety between the microgrid and the main grid. We consider this important problem in this paper and make the following contributions. First, we formulate this problem as a Markov decision process to capture the uncertain supply and EV charging demand in the microgrid of buildings. Besides reducing the total operation cost of buildings, the model also considers the power exchange limitation to ensure transmission safety. Second, this model is reformulated under event-based optimization framework to alleviate the impact of large state and action space. By appropriately defining the event and event-based action, the EV charging process can be optimized by searching a randomized parametric event-based control policy in the microgrid controller and implementing a selecting-to-charging rule in each building controller. Third, a constrained gradient-based policy optimzation method with adjusting mechanism is proposed to iteratively find the optimal event-based control policy for EV charging demand in each building. Numerical experiments considering a microgrid of three buildings are conducted to analyze the structure and the performance of the event-based control policy for EV charging.
Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise
Authors: Jiangbang Liu, Song Bo, Benjamin Decardi-Nelson, Jinfeng Liu, Jingtao Hu, Tao Zou
Date: 2022-01-05 00:00:00
Tasks: Model Predictive Control, Sensitivity

Economic model predictive control and tracking model predictive control are two popular advanced process control strategies used in various of fields. Nevertheless, which one should be chosen to achieve better performance in the presence of noise is uncertain when designing a control system. To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them in this work. First, their controller gains around the optimal steady state are evaluated by calculating the sensitivities of corresponding constrained dynamic programming problems. Second, the controller gains are substituted into control loops to derive the propagation of process and measurement noise. Subsequently, the Taylor expansion is introduced to simplify the calculation of variance and mean of each variable. Finally, the tracking and economic performance surfaces are plotted and the performance indices are precisely calculated through integrating the objective functions and the probability density functions. Moreover, boundary moving (i.e., back off) and target moving can be pre-configured to guarantee the stability of controlled processes using the proposed approach. Extensive simulations under different cases illustrate the proposed approach can provide useful guidance on performance assessment and controller design.
Robust economic MPC of the absorption column in post-combustion carbon capture through zone tracking
Authors: Benjamin Decardi-Nelson, Jinfeng Liu
Date: 2022-01-05 00:00:00
Tasks: FLUE, Model Predictive Control

Several studies have reported the importance of optimally operating the absorption column in a post-combustion CO2 capture (PCC) plant. It has been demonstrated in our previous work how economic Model Predictive Control (EMPC) has a great potential to improve the operation of the PCC plant. However, the use of a general economic objective such as maximizing the absorption efficiency of the column can cause EMPC to drive the state of the system close to the constraints. This may often than not lead to solvent overcirculation and flooding which are undesirable. In this work, we present an EMPC with zone tracking algorithm as an effective means to address this problem. The proposed control algorithm incorporates a zone tracking objective and an economic objective to form a multi-objective optimal control problem. To ensure that the zone tracking objective is achieved in the presence of model uncertainties and time-varying flue gas flow rate, we propose a method to modify the original target zone with a control invariant set. The zone modification method combines both ellipsoidal control invariant set techniques and a back-off strategy. The use of ellipsoidal control invariant sets ensure that the method is applicable to large scale systems such as the absorption column. We present several simulation case studies that demonstrate the effectiveness and applicability of the proposed control algorithm to the absorption column in a post-combustion CO2 capture plant.
Numerical Techniques for Applications of Analytical Theories to Sequence-Dependent Phase Separations of Intrinsically Disordered Proteins
Authors: Yi-Hsuan Lin, Jonas Wessén, Tanmoy Pal, Suman Das, Hue Sun Chan
Date: 2022-01-06 00:00:00

Biomolecular condensates, physically underpinned to a significant extent by liquid-liquid phase separation (LLPS), are now widely recognized by numerous experimental studies to be of fundamental biological, biomedical, and biophysical importance. In the face of experimental discoveries, analytical formulations emerged as a powerful yet tractable tool in recent theoretical investigations of the role of LLPS in the assembly and dissociation of these condensates. The pertinent LLPS often involves, though not exclusively, intrinsically disordered proteins engaging in multivalent interactions that are governed by their amino acid sequences. For researchers interested in applying these theoretical methods, here we provide a practical guide to a set of computational techniques devised for extracting sequence-dependent LLPS properties from analytical formulations. The numerical procedures covered include those for the determinination of spinodal and binodal phase boundaries from a general free energy function with examples based on the random phase approximation in polymer theory, construction of tie lines for multiple-component LLPS, and field-theoretic simulation of multiple-chain heteropolymeric systems using complex Langevin dynamics. Since a more accurate physical picture often requires comparing analytical theory against explicit-chain model predictions, a commonly utilized methodology for coarse-grained molecular dynamics simulations of sequence-specific LLPS is also briefly outlined.
A Review of Mathematical and Computational Methods in Cancer Dynamics
Authors: Abicumaran Uthamacumaran, Hector Zenil
Date: 2022-01-05 00:00:00
Tasks: Time Series, Time Series Analysis

Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the signaling or gene expression state-space of cancer triggering processes. A class of these patterns, strange attractors, may be mathematical biomarkers of cancer progression. The emergence of intracellular chaos and chaotic cell population dynamics remains a new paradigm in systems oncology. As such, chaotic and complex dynamics are discussed as mathematical hallmarks of cancer cell fate dynamics herein. Given the assumption that time-resolved single-cell datasets are made available, a survey of interdisciplinary tools and algorithms from complexity theory, are hereby reviewed to investigate critical phenomena and chaotic dynamics in cancer ecosystems. To conclude, the perspective cultivates an intuition for computational systems oncology in terms of nonlinear dynamics, information theory, inverse problems and complexity. We highlight the limitations we see in the area of statistical machine learning but the opportunity at combining it with the symbolic computational power offered by the mathematical tools explored.
Earthquake Nowcasting with Deep Learning
Authors: Geoffrey Fox, John Rundle, Andrea Donnellan, Bo Feng
Date: 2021-12-18 00:00:00
Tasks: Deep Learning

We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and measures presenting promising initial results for a region of Southern California from 1950-2020. Earthquake activity is predicted as a function of 0.1-degree spatial bins for time periods varying from two weeks to four years. The overall quality is measured by the Nash Sutcliffe Efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open-source together with the preprocessed data from the USGS.
Direct multi-modal inversion of geophysical logs using deep learning
Authors: Sergey Alyaev, Ahmed H. Elsheikh
Date: 2021-11-29 00:00:00
Tasks: Deep Learning, Trajectory Prediction

Geosteering of wells requires fast interpretation of geophysical logs, which is a non-unique inverse problem. Current work presents a proof-of-concept approach to multi-modal probabilistic inversion of logs using a single evaluation of an artificial deep neural network (DNN). A mixture density DNN (MDN) is trained using the "multiple-trajectory-prediction" (MTP) loss functions, which avoids mode collapse typical for traditional MDNs, and allows multi-modal prediction ahead of data. The proposed approach is verified on the real-time stratigraphic inversion of gamma-ray logs. The multi-modal predictor outputs several likely inverse solutions/predictions, providing more accurate and realistic solutions than a deterministic regression using a DNN. For these likely stratigraphic curves, the model simultaneously predicts their probabilities, which are implicitly learned from the training geological data. The stratigraphy predictions and their probabilities obtained in milliseconds from the MDN can enable better real-time decisions under geological uncertainties.
Stationary social learning in a changing environment
Authors: Raphaël Lévy, Marcin Pęski, Nicolas Vieille
Date: 2022-01-06 00:00:00
Tasks: Informativeness

We consider social learning in a changing world. Society can remain responsive to state changes only if agents regularly act upon fresh information, which limits the value of social learning. When the state is close to persistent, a consensus whereby most agents choose the same action typically emerges. The consensus action is not perfectly correlated with the state though, because the society exhibits inertia following state changes. Phases of inertia may be longer when signals are more precise, even if agents draw large samples of past actions, as actions then become too correlated within samples, thereby reducing informativeness and welfare.
Reputational Bargaining and Inefficient Technology Adoption
Authors: Harry Pei, Maren Vairo
Date: 2022-01-05 00:00:00

A buyer and a seller bargain over the price of an object. Both players can build reputations for being obstinate by offering the same price over time. Before players bargain, the seller decides whether to adopt a new technology that can lower his cost of production. We show that even when the buyer cannot observe the seller's adoption decision, players' reputational incentives can lead to inefficient under-adoption and significant delays in reaching agreement, and that these inefficiencies arise in equilibrium if and only if the social benefit from adoption is large enough. Our result implies that an increase in the benefit from adoption may lower the probability of adoption and that the seller's opportunity to adopt a cost-saving technology may lower social welfare.
Reputation, Learning and Project Choice in Frictional Economies
Authors: Farzad Pourbabaee
Date: 2022-01-05 00:00:00

I introduce a dynamic model of learning and random meetings between a long-lived agent with unknown ability and heterogeneous projects with observable qualities. The outcomes of the agent's matches with the projects determine her posterior belief about her ability (i.e., her reputation). In a self-type learning framework with endogenous outside option, I find the optimal project selection strategy of the agent, that determines what types of projects the agent with a certain level of reputation will accept. Sections of the optimal matching set become increasing intervals, with different cutoffs across different types of the projects. Increasing the meeting rate has asymmetric effects on the sections of the matching sets: it unambiguously expands the section for the high type projects, while on some regions, it initially expands and then shrinks the section of the low type projects.