pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic fracture of a bicontinuously nanostructured copolymer: A deep-learning analysis of big-data-generating experiment
Authors: Hanxun Jin, Tong Jiao, Rodney J. Clifton, Kyung-Suk Kim
Date: 2021-12-03 00:00:00

Here, we report measurements of detailed dynamic cohesive properties (DCPs) beyond the dynamic fracture toughness of a bicontinuously nanostructured copolymer, polyurea, under an extremely loading rate, from deep-learning analyses of a dynamic big-data-generating experiment. We first describe a new Dynamic Line-Image Shearing Interferometer (DL-ISI), which uses a streak camera to record optical fringes of displacement-gradient vs time profile along a line on sample's rear surface. This system enables us to detect crack initiation and growth processes in plate-impact experiments. Then, we present a convolutional neural network (CNN) based deep-learning framework, trained by extensive finite-element simulations, that inversely determines the accurate DCPs from the DL-ISI fringe images. For the measurements, plate-impact experiments were performed on a set of samples with a mid-plane crack. A Conditional Generative Adversarial Networks (cGAN) was employed first to reconstruct missing DL-ISI fringes with recorded partial DL-ISI fringes. Then, the CNN and a correlation method were applied to the fully reconstructed fringes to get the dynamic fracture toughness, 12.1kJ/m^2, cohesive strength, 302 MPa, and maximum cohesive separation, 80.5 um, within 0.4%, 2.7%, and 2.2% differences, respectively. For the first time, the DCPs of polyurea have been successfully obtained by the DL-ISI with the pre-trained CNN and correlation analyses of cGAN-reconstructed data sets. The dynamic cohesive strength is found to be nearly three times higher than the dynamic-failure-initiation strength. The high dynamic fracture toughness is found to stem from both high dynamic cohesive strength and high ductility of the dynamic cohesive separation.
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning
Authors: Grace W. Lindsay, Josh Merel, Tom Mrsic-Flogel, Maneesh Sahani
Date: 2021-12-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Artificial neural systems trained using reinforcement, supervised, and unsupervised learning all acquire internal representations of high dimensional input. To what extent these representations depend on the different learning objectives is largely unknown. Here we compare the representations learned by eight different convolutional neural networks, each with identical ResNet architectures and trained on the same family of egocentric images, but embedded within different learning systems. Specifically, the representations are trained to guide action in a compound reinforcement learning task; to predict one or a combination of three task-related targets with supervision; or using one of three different unsupervised objectives. Using representational similarity analysis, we find that the network trained with reinforcement learning differs most from the other networks. Using metrics inspired by the neuroscience literature, we find that the model trained with reinforcement learning has a sparse and high-dimensional representation wherein individual images are represented with very different patterns of neural activity. Further analysis suggests these representations may arise in order to guide long-term behavior and goal-seeking in the RL agent. Finally, we compare the representations learned by the RL agent to neural activity from mouse visual cortex and find it to perform as well or better than other models. Our results provide insights into how the properties of neural representations are influenced by objective functions and can inform transfer learning approaches.
Multilingual training for Software Engineering
Authors: Toufique Ahmed, Premkumar Devanbu
Date: 2021-12-03 00:00:00
Tasks: Code Summarization, Retrieval, Type prediction

Well-trained machine-learning models, which leverage large amounts of open-source software data, have now become an interesting approach to automating many software engineering tasks. Several SE tasks have all been subject to this approach, with performance gradually improving over the past several years with better models and training methods. More, and more diverse, clean, labeled data is better for training; but constructing good-quality datasets is time-consuming and challenging. Ways of augmenting the volume and diversity of clean, labeled data generally have wide applicability. For some languages (e.g., Ruby) labeled data is less abundant; in others (e.g., JavaScript) the available data maybe more focused on some application domains, and thus less diverse. As a way around such data bottlenecks, we present evidence suggesting that human-written code in different languages (which performs the same function), is rather similar, and particularly preserving of identifier naming patterns; we further present evidence suggesting that identifiers are a very important element of training data for software engineering tasks. We leverage this rather fortuitous phenomenon to find evidence that available multilingual training data (across different languages) can be used to amplify performance. We study this for 3 different tasks: code summarization, code retrieval, and function naming. We note that this data-augmenting approach is broadly compatible with different tasks, languages, and machine-learning models.
Deep learning method for identifying mass composition of ultra-high-energy cosmic rays
Authors: O. Kalashev, I. Kharuk, M. Kuznetsov, G. Rubtsov, T. Sako, Y. Tsunesada, Ya. Zhezher
Date: 2021-12-03 00:00:00

We introduce a novel method for identifying the mass composition of ultra-high-energy cosmic rays using deep learning. The key idea of the method is to use a chain of two neural networks. The first network predicts the type of a primary particle for individual events, while the second infers the mass composition of an ensemble of events. We apply this method to the Monte-Carlo data for the Telescope Array Surface Detectors readings, on which it yields an unprecedented low error of 7% for 4-component approximation. We also discuss the problems of applying the developed method to the experimental data, and the way they can be resolved.
MD-inferred neural network monoclinic finite-strain hyperelasticity models for $β$-HMX: Sobolev training and validation against physical constraints
Authors: Nikolaos N. Vlassis, Puhan Zhao, Ran Ma, Tommy Sewell, WaiChing Sun
Date: 2021-11-29 00:00:00
Tasks: Transfer Learning

We present a machine learning framework to train and validate neural networks to predict the anisotropic elastic response of the monoclinic organic molecular crystal $\beta$-HMX in the geometrical nonlinear regime. A filtered molecular dynamic (MD) simulations database is used to train the neural networks with a Sobolev norm that uses the stress measure and a reference configuration to deduce the elastic stored energy functional. To improve the accuracy of the elasticity tangent predictions originating from the learned stored energy, a transfer learning technique is used to introduce additional tangential constraints from the data while necessary conditions (e.g. strong ellipticity, crystallographic symmetry) for the correctness of the model are either introduced as additional physical constraints or incorporated in the validation tests. Assessment of the neural networks is based on (1) the accuracy with which they reproduce the bottom-line constitutive responses predicted by MD, (2) detailed examination of their stability and uniqueness, and (3) admissibility of the predicted responses with respect to continuum mechanics theory in the finite-deformation regime. We compare the neural networks' training efficiency under different Sobolev constraints and assess the models' accuracy and robustness against MD benchmarks for $\beta$-HMX.
Regularized Newton Method with Global $O(1/k^2)$ Convergence
Authors: Konstantin Mishchenko
Date: 2021-12-03 00:00:00

We present a Newton-type method that converges fast from any initialization and for arbitrary convex objectives with Lipschitz Hessians. We achieve this by merging the ideas of cubic regularization with a certain adaptive Levenberg--Marquardt penalty. In particular, we show that the iterates given by $x^{k+1}=x^k - \bigl(\nabla^2 f(x^k) + \sqrt{H\|\nabla f(x^k)\|} \mathbf{I}\bigr)^{-1}\nabla f(x^k)$, where $H>0$ is a constant, converge globally with a $\mathcal{O}(\frac{1}{k^2})$ rate. Our method is the first variant of Newton's method that has both cheap iterations and provably fast global convergence. Moreover, we prove that locally our method converges superlinearly when the objective is strongly convex. To boost the method's performance, we present a line search procedure that does not need prior knowledge of $H$ and is provably efficient.
DeepHawkes: Bridging the gap between prediction and understanding of information cascades
Authors: Qi Cao, HuaWei Shen, Keting Cen, Wentao Ouyang, Xueqi Cheng
Date: 2017-11-01 00:00:00
Proceeding: CIKM 2017 11

Online social media remarkably facilitates the production and delivery of information, intensifying the competition among vast information for users’ attention and highlighting the importance of predicting the popularity of information. Existing approaches for popularity prediction fall into two paradigms: feature-based approaches and generative approaches. Feature-based approaches extract various features (e.g., user, content, structural, and temporal features), and predict the future popularity of information by training a regression/classification model. Their predictive performance heavily depends on the quality of hand-crafted features. In contrast, generative approaches devote to characterizing and modeling the process that a piece of information accrues attentions, offering us high ease to understand the underlying mechanisms governing the popularity dynamics of information cascades. But they have less desirable predictive power since they are not optimized for popularity prediction. In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade. DeepHawkes inherits the high interpretability of Hawkes process and possesses the high predictive power of deep learning methods, bridging the gap between prediction and understanding of information cascades. We verify the effectiveness of DeepHawkes by applying it to predict retweet cascades of Sina Weibo and citation cascades of a longitudinal citation dataset. Experimental results demonstrate that DeepHawkes outperforms both feature-based and generative approaches.
FAWOS: Fairness-Aware Oversampling Algorithm Based on Distributions of Sensitive Attributes
Authors: Teresa Salazar, Miriam Seoane Santos, Helder Araújo, Pedro Henriques Abreu
Date: 2021-05-27 00:00:00
Proceeding: IEEE Access 2021 5
Tasks: Attribute, Fairness

With the increased use of machine learning algorithms to make decisions which impact people's lives, it is of extreme importance to ensure that predictions do not prejudice subgroups of the population with respect to sensitive attributes such as race or gender. Discrimination occurs when the probability of a positive outcome changes across privileged and unprivileged groups defined by the sensitive attributes. It has been shown that this bias can be originated from imbalanced data contexts where one of the classes contains a much smaller number of instances than the other classes. It is also important to identify the nature of the imbalanced data, including the characteristics of the minority classes' distribution. This paper presents FAWOS: a Fairness-Aware oversampling algorithm which aims to attenuate unfair treatment by handling sensitive attributes' imbalance. We categorize different types of datapoints according to their local neighbourhood with respect to the sensitive attributes, identifying which are more difficult to learn by the classifiers. In order to balance the dataset, FAWOS oversamples the training data by creating new synthetic datapoints using the different types of datapoints identified. We test the impact of FAWOS on different learning classifiers and analyze which can better handle sensitive attribute imbalance. Empirically, we observe that this algorithm can effectively increase the fairness results of the classifiers while not neglecting the classification performance. Source code can be found at: https://github.com/teresalazar13/FAWOS
Scale up to infinity: the UWB Indoor Global Positioning System
Authors: Luca Santoro, Matteo Nardello, Davide Brunelli, Daniele Fontanelli
Date: 2021-12-03 00:00:00
Tasks: Position

Determining assets position with high accuracy and scalability is one of the most investigated technology on the market. The accuracy provided by satellites-based positioning systems (i.e., GLONASS or Galileo) is not always sufficient when a decimeter-level accuracy is required or when there is the need of localising entities that operate inside indoor environments. Scalability is also a recurrent problem when dealing with indoor positioning systems. This paper presents an innovative UWB Indoor GPS-Like local positioning system able to tracks any number of assets without decreasing measurements update rate. To increase the system's accuracy the mathematical model and the sources of uncertainties are investigated. Results highlight how the proposed implementation provides positioning information with an absolute maximum error below 20 cm. Scalability is also resolved thanks to DTDoA transmission mechanisms not requiring an active role from the asset to be tracked.
Socially Compatible Control Design of Automated Vehicle in Mixed Traffic
Authors: Mehmet Fatih Ozkan, Yao Ma
Date: 2021-12-03 00:00:00
Tasks: Decision Making

In the car-following scenarios, automated vehicles (AVs) usually plan motions without considering the impacts of their actions on the following human drivers. This paper aims to leverage such impacts to plan more efficient and socially desirable AV behaviors in human-AV interactions. Specifically, we introduce a socially compatible control design for the AV that benefits mixed traffic in the car-following scenarios. The proposed design enables the altruistic AV in human-AV interaction by integrating the social value orientation from psychology into its decision-making process. The altruistic AV generates socially desirable behaviors by optimizing both its own reward and courtesy to the following human driver's original plan in the longitudinal motion. The results show that as compared to the egoistic AV, the altruistic AV significantly avoids disrupting the following human driver's initial plan and leads the following human driver to achieve considerably smaller car-following gap distance and time headway. Moreover, we investigated the impacts of the socially compatible control design with different altruism levels of the AV using statistical assessments. The results collectively demonstrate the significant improvement in traffic-level metrics as a result of the AV's altruistic behaviors in human-AV interactions.
Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
Authors: JungHoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum, Taejoon Kim, David J. Love, Christopher G. Brinton
Date: 2021-12-03 00:00:00

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We consider a practical setting where (i) the IRS reflection coefficients are achieved by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not applicable in this setting due to the difficulty of channel estimation and the low feedback rate. Therefore, we develop a novel adaptive codebook-based limited feedback protocol where only a codeword index is transferred to the IRS. We propose two solutions for adaptive codebook design: random adjacency (RA) and deep neural network policy-based IRS control (DPIC), both of which only require the end-to-end compound channels. We further develop several augmented schemes based on the RA and DPIC. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by our schemes.
A Practical Method for Automated Modeling and Parametric Stability Analysis of VSC with Periodical Steady State
Authors: Chen Zhang, Jon Are Suul, Marta Molinas
Date: 2021-12-03 00:00:00

Linear Time Periodic (LTP) framework-based analysis of Voltage Source Converters (VSCs) is becoming popular, a driven factor is that many of the existing VSC applications inevitably exhibit the periodic steady-state (PSS), e.g., VSCs with unbalanced grid connections and the operation of a modular multilevel converter (MMC). In these studies, acquisition of the VSC's PSS conditions is a necessary precondition for proper linearization and stability analysis, and the efficiency of this process is particularly important for parametric studies. To this end, this work develops a computational method for automating the LTP analyses of VSCs with an integrated PSS solver. The core of the method lies in a unified frequency-domain iteration process that is developed by applying the generalized averaging principle. Given this, modeling, stability analysis, as well as the solution of PSS conditions can be unified in one process. The algorithmic implementation of the method in MATLAB is elaborated. Application of the obtained tool in impedance generation and parametric stability test is presented with an unbalanced grid-tied VSC as the exemplification. Finally, PSCAD/EMTDC simulations further consolidate the validity of the results.
Synthesis of Lyapunov Functions using Formal Verification
Authors: Lukas Munser, Grigory Devadze, Stefan Streif
Date: 2021-12-03 00:00:00

Recent employments of SMT solvers within the Lyapunov function synthesis provided effective tools for automated construction of Lyapunov functions alongside with sound computer-assisted certificates. The main benefit of the suggested approach is the formal correctness and elimination of the numerical uncertainty. In the present work, we extend the SMT-based synthesis approach for wider classes of continuous and discrete-time systems. Additionally, we address constructions of Lyapunov functions for state-dependent switching systems. We illustrate our approach by means of various examples from the control systems literature.
Active Disturbance Rejection Control (ADRC) Toolbox for MATLAB/Simulink
Authors: Krzysztof Lakomy, Wojciech Giernacki, Jacek Michalski, Rafal Madonski
Date: 2021-12-02 00:00:00

In this study, an active disturbance rejection control (ADRC) toolbox for MATLAB/Simulink is introduced. Although ADRC has already been established as a powerful robust control framework with successful industrial implementations and strong theoretical foundations, a comprehensive tool for computer-aided design of ADRC has not been developed until now. The proposed open-source ADRC Toolbox is a response to the growing need in the scientific community and the control industry for a straightforward software application of the ADRC methodology. Its main purpose is to fill the gap between the current theories and applications of ADRC and to provide an easy-to-use solution for users in various control fields who want to employ the ADRC scheme in their applications. The ADRC Toolbox contains a single, general-purpose, drag-and-drop function block that allows the synthesis of a predefined ADRC-based strategy with minimal design effort. Additionally, its open structure allows creation of custom control solutions. The efficacy of the ADRC Toolbox is validated through both simulations and hardware experiments, which were conducted using a variety of problems known in the motion, process, and power control areas.
The conservative lock-in range for PLL with lead-lag filter and triangular phase detector characteristic
Authors: Blagov M. V., Kuznetsov N. V., Lobachev M. Y., Yuldashev M. V., Yuldashev R. V
Date: 2021-12-02 00:00:00

In the present work, a second-order PLL with lead-lag loop filter and triangular phase detector characteristic is analysed. An exact value of the conservative lock-in range is obtained for the considered model. The solution is based on analytical integration of the considered model on the linear segments.
Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty
Authors: Niko Hauzenberger, Florian Huber, Massimiliano Marcellino, Nico Petz
Date: 2021-12-03 00:00:00
Tasks: Time Series, Time Series Analysis

We develop a non-parametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specification is used to provide additional flexibility and control for heteroskedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out through an efficient and scalable algorithm which can handle large models. The GP-VAR is illustrated by means of simulated data and in a forecasting exercise with US data. Moreover, we use the GP-VAR to analyze the effects of macroeconomic uncertainty, with a particular emphasis on time variation and asymmetries in the transmission mechanisms.
Inference for ROC Curves Based on Estimated Predictive Indices
Authors: Yu-Chin Hsu, Robert P. Lieli
Date: 2021-12-03 00:00:00

We provide a comprehensive theory of conducting in-sample statistical inference about receiver operating characteristic (ROC) curves that are based on predicted values from a first stage model with estimated parameters (such as a logit regression). The term "in-sample" refers to the practice of using the same data for model estimation (training) and subsequent evaluation, i.e., the construction of the ROC curve. We show that in this case the first stage estimation error has a generally non-negligible impact on the asymptotic distribution of the ROC curve and develop the appropriate pointwise and functional limit theory. We propose methods for simulating the distribution of the limit process and show how to use the results in practice in comparing ROC curves.
Patient-Centered Appraisal of Race-Free Clinical Risk Assessment
Authors: Charles F. Manski
Date: 2021-12-02 00:00:00

Until recently, there has been a consensus that clinicians should condition patient risk assessments on all observed patient covariates with predictive power. The broad idea is that knowing more about patients enables more accurate predictions of their health risks and, hence, better clinical decisions. This consensus has recently unraveled with respect to a specific covariate, namely race. There have been increasing calls for race-free risk assessment, arguing that using race to predict patient outcomes contributes to racial disparities and inequities in health care. Writers calling for race-free risk assessment have not studied how it would affect the quality of clinical decisions. Considering the matter from the patient-centered perspective of medical economics yields a disturbing conclusion: Race-free risk assessment would harm patients of all races.