pwc-archive/papers-with-abstracts huggingface dataset:
Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment
Authors: Zhenyue Zhang, Hongyuan Zha
Date: 2002-12-07 00:00:00
Tasks: Data Visualization, Dimensionality Reduction

Nonlinear manifold learning from unorganized data points is a very challenging unsupervised learning and data visualization problem with a great variety of applications. In this paper we present a new algorithm for manifold learning and nonlinear dimension reduction. Based on a set of unorganized data points sampled with noise from the manifold, we represent the local geometry of the manifold using tangent spaces learned by fitting an affine subspace in a neighborhood of each data point. Those tangent spaces are aligned to give the internal global coordinates of the data points with respect to the underlying manifold by way of a partial eigendecomposition of the neighborhood connection matrix. We present a careful error analysis of our algorithm and show that the reconstruction errors are of second-order accuracy. We illustrate our algorithm using curves and surfaces both in 2D/3D and higher dimensional Euclidean spaces, and 64-by-64 pixel face images with various pose and lighting conditions. We also address several theoretical and algorithmic issues for further research and improvements.
Design-Based Uncertainty for Quasi-Experiments
Authors: Ashesh Rambachan, Jonathan Roth
Date: 2020-08-03 00:00:00
Tasks: valid

Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This paper develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment assignment can be viewed as the realization of some stochastic process but there is concern about unobserved selection into treatment. In our framework, treatments are stochastic, but units may differ in their probabilities of receiving treatment, thereby allowing for rich forms of selection. We provide conditions under which the estimands of popular quasi-experimental estimators correspond to interpretable finite-population causal parameters. We characterize the biases and distortions to inference that arise when these conditions are violated. These results can be used to conduct sensitivity analyses when there are concerns about selection into treatment. Taken together, our results establish a rigorous foundation for quasi-experimental analyses that more closely aligns with the way empirical researchers discuss the variation in the data.
Assessing Sensitivity to Unconfoundedness: Estimation and Inference
Authors: Matthew A. Masten, Alexandre Poirier, Linqi Zhang
Date: 2020-12-31 00:00:00
Tasks: Sensitivity

This paper provides a set of methods for quantifying the robustness of treatment effects estimated using the unconfoundedness assumption (also known as selection on observables or conditional independence). Specifically, we estimate and do inference on bounds on various treatment effect parameters, like the average treatment effect (ATE) and the average effect of treatment on the treated (ATT), under nonparametric relaxations of the unconfoundedness assumption indexed by a scalar sensitivity parameter c. These relaxations allow for limited selection on unobservables, depending on the value of c. For large enough c, these bounds equal the no assumptions bounds. Using a non-standard bootstrap method, we show how to construct confidence bands for these bound functions which are uniform over all values of c. We illustrate these methods with an empirical application to effects of the National Supported Work Demonstration program. We implement these methods in a companion Stata module for easy use in practice.
A Bayesian viewpoint on the price formation process
Authors: Joffrey Derchu
Date: 2020-12-31 00:00:00

We introduce a simple framework in which market participants update their prior about an efficient price with a model-based learning process. We show that exponential intensities for the arrival of aggressive orders arise naturally in this setting. Our approach allows us to fully describe market dynamics in the case with Brownian efficient price and informed market takers. We are also able to revisit the emergence of market impact due to meta-order splitting, making several connections with existing literature.
Interactive SARS-CoV-2 mutation timemaps
Authors: Rene L. Warren, Inanc Birol
Date: 2020-12-31 00:00:00
Tasks: Vector Graphics

As the year 2020 draws to an end, several new strains have been reported for the SARS-CoV-2 coronavirus, the agent responsible for the COVID-19 pandemic that has afflicted us all this past year. However, it is difficult to comprehend the scale, in sequence space, geographical location and time, at which SARS-CoV-2 mutates and evolves in its human hosts. To get an appreciation for the rapid evolution of the coronavirus, we built interactive scalable vector graphics maps that show daily nucleotide variations in genomes from the six most populated continents compared to that of the initial, ground-zero SARS-CoV-2 isolate sequenced at the beginning of the year. Availability: Mutation time maps are available from https://bcgsc.github.io/SARS2/
Exploring a strongly non-Markovian animal behavior
Authors: Vasyl Alba, Gordon J. Berman, William Bialek, Joshua W. Shaevitz
Date: 2020-12-31 00:00:00

A freely walking fly visits roughly 100 stereotyped states in a strongly non-Markovian sequence. To explore these dynamics, we develop a generalization of the information bottleneck method, compressing the large number of behavioral states into a more compact description that maximally preserves the correlations between successive states. Surprisingly, preserving these short time correlations with a compression into just two states captures the long ranged correlations seen in the raw data. Having reduced the behavior to a binary sequence, we describe the distribution of these sequences by an Ising model with pairwise interactions, which is the maximum entropy model that matches the two-point correlations. Matching the correlation function at longer and longer times drives the resulting model toward the Ising model with inverse square interactions and near zero magnetic field. The emergence of this statistical physics problem from the analysis real data on animal behavior is unexpected.
Evidences for the decomposition of perovskite materials in TEM characterization
Authors: YH Deng
Date: 2020-12-29 00:00:00

【Common Phase and Structure Misidentifications in High-Resolution TEM Characterization of Perovskite Materials；Deng, Y.-H. Common Phase and Structure Misidentifications in High-Resolution TEM Characterization of Perovskite Materials. Condens. Matter 2021, 6, 1.】

Abstract：High-resolution TEM (HRTEM) is a powerful tool for structure characterization. However, methylammonium lead iodide (MAPbI3) perovskite is highly sensitive to electron beams and easily decomposes into lead iodide (PbI2). Misidentifications, such as PbI2 being incorrectly labeled as perovskite, are widely present in HRTEM characterization and would negatively affect the development of perovskite research field. Here misidentifications in MAPbI3 perovskite are summarized, classified, and corrected based on low-dose imaging and electron diffraction (ED) simulations. Corresponding crystallographic parameters of intrinsic tetragonal MAPbI3 and the confusable hexagonal PbI2 are presented unambiguously. Finally, the method of proper phase identification and some strategies to control the radiation damage in HRTEM are provided. This warning paves the way to avoid future misinterpretations in HRTEM characterization of perovskite and other electron beam-sensitive materials. 
Keywords: MAPbI3 perovskite; transmission electron microscopy (TEM); electron diffraction (ED); phase identification; electron beam-sensitive
Towards a standard model for research in agent-based modeling and simulation
Authors: Nuno Fachada​, Vitor V. Lopes, Rui C. Martins, Agostinho C. Rosa
Date: 2015-11-25 00:00:00
Tasks: Decision Making

Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. ABMs are very sensitive to implementation details. Thus, it is very easy to inadvertently introduce changes which modify model dynamics. Such problems usually arise due to the lack of transparency in model descriptions, which constrains how models are assessed, implemented and replicated. In this paper, we present PPHPC, a model which aims to serve as a standard in agent based modeling research, namely, but not limited to, conceptual model specification, statistical analysis of simulation output, model comparison and parallelization studies. This paper focuses on the first two aspects (conceptual model specification and statistical analysis of simulation output), also providing a canonical implementation of PPHPC. The paper serves as a complete reference to the presented model, and can be used as a tutorial for simulation practitioners who wish to improve the way they communicate their ABMs.
Spectrometric differentiation of yeast strains using minimum volume increase and minimum direction change clustering criteria
Authors: Nuno Fachada, Mário A. T. Figueiredo, Vitor V. Lopes, Rui C. Martins, Agostinho C.Rosa
Date: 2014-03-28 00:00:00
Tasks: Clustering

This paper proposes new clustering criteria for distinguishing Saccharomyces cerevisiae (yeast) strains using their spectrometric signature. These criteria are introduced in an agglomerative hierarchical clustering context, and consist of: (a) minimizing the total volume of clusters, as given by their respective convex hulls; and, (b) minimizing the global variance in cluster directionality. The method is deterministic and produces dendrograms, which are important features for microbiologists. A set of experiments, performed on yeast spectrometric data and on synthetic data, show the new approach outperforms several well-known clustering algorithms, including techniques commonly used for microorganism differentiation.
Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios
Authors: Silvia Tulli, Marta Couto, Miguel Vasco, Elmira Yadollahi, Francisco Melo, Ana Paiva
Date: 2020-11-06 00:00:00
Tasks: Explanation Generation

Revealing the internal workings of a robot can help a human better understand the robot’s behaviors. How to reveal such workings, e.g., via explanation generation, remains a significant challenge. This gets even more complex when these explanations are targeted towards children. Therefore, we propose a search-based approach to generate contrastive explanations using optimal and sub-optimal plans and implement it in a scenario for children. In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking. We report results around our explanation generation system that was successfully deployed among seven-year-old children. Our results show trends that the generated explanations were able to positively affect the children’s perceived difficulty in learning the zero-sum game.
Indirect Measurement of Hepatic Drug Clearance by Fitting Dynamical Models
Authors: Yoko Franchetti, Thomas D. Nolin, Franz Franchetti
Date: 2020-12-31 00:00:00
Tasks: CPU, Time Series, Time Series Analysis

We present an indirect signal processing-based measurement method for biological quantities in humans that cannot be directly measured. We develop the method by focusing on estimating hepatic enzyme and drug transporter activity through breath-biopsy samples clinically obtained via the erythromycin breath test (EBT): a small dose of radio-labeled drug is injected and the subsequent content of radio-labeled CO$_2$ is measured repeatedly in exhaled breath; the resulting time series is analyzed. To model EBT we developed a 14-variable non-linear reduced order dynamical model that describes the behavior of the drug and its metabolites in the human body well enough to capture all biological phenomena of interest. Based on this system of coupled non-linear ordinary differential equations (ODEs) we treat the measurement problem as inverse problem: we estimate the ODE parameters of individual patients from the measured EBT time series. These estimates then provide a measurement of the liver activity of interest. The parameters are hard to estimate as the ODEs are stiff and the problem needs to be regularized to ensure stable convergence. We develop a formal operator framework to capture and treat the specific non-linearities present, and perform perturbation analysis to establish properties of the estimation procedure and its solution. Development of the method required 150,000 CPU hours at a supercomputing center, and a single production run takes CPU 24 hours. We introduce and analyze the method in the context of future precision dosing of drugs for vulnerable patients (e.g., oncology, nephrology, or pediatrics) to eventually ensure efficacy and avoid toxicity.
Detecting Singleton Spams in Reviews via Learning Deep Anomalous Temporal Aspect-Sentiment Patterns
Authors: Yassien Shaalan, Xiuzhen Zhang, Jeffrey Chan, Mahsa Salehi
Date: 2021-01-02 00:00:00
Tasks: Feature Engineering

Customer reviews are an essential source of information to consumers. Meanwhile, opinion spams spread widely and the detection of spam reviews becomes critically important for ensuring the integrity of the echo system of online reviews. Singleton spam reviews—one-time reviews—have spread widely of late as spammers can create multiple accounts to purposefully cheat the system. Most available techniques fail to detect this cunning form of malicious reviews, mainly due to the scarcity of behaviour trails left behind by singleton spammers. Available approaches also require extensive feature engineering, expensive manual annotation and are less generalizable. Based on our thorough study of spam reviews, it was found that genuine opinions are usually directed uniformly towards important aspects of entities. In contrast, spammers attempt to counter the consensus towards these aspects while covering their malicious intent by adding more text but on less important aspects. Additionally, spammers usually target specific time periods along products’ lifespan to cause maximum bias to the public opinion. Based on these observations, we present an unsupervised singleton spam review detection model that runs in two steps. Unsupervised deep aspect-level sentiment model employing deep Boltzmann machines first learns fine-grained opinion representations from review texts. Then, an LSTM network is trained on opinion learned representation to track the evolution of opinions through the fluctuation of sentiments in a temporal context, followed by the application of a Robust Variational Autoencoder to identify spam instances. Experiments on three benchmark datasets widely used in the literature showed that our approach outperforms strong state-of-the-art baselines.
Transmission Electron Microscopy of Halide Perovskite Materials: Common Phase and Structure Misidentifications
Authors: by Yu-Hao Deng
Date: 2020-12-25 00:00:00

Abstract: High-resolution TEM (HRTEM) is a powerful tool for structure characterization. However, methylammonium lead iodide (MAPbI3) perovskite is highly sensitive to electron beams and easily decomposes into lead iodide (PbI2). Misidentifications, such as PbI2 being incorrectly labeled as perovskite, are widely present in HRTEM characterization and would negatively affect the development of perovskite research field. Here misidentifications in MAPbI3 perovskite are summarized, classified, and corrected based on low-dose imaging and electron diffraction (ED) simulations. Corresponding crystallographic parameters of intrinsic tetragonal MAPbI3 and the confusable hexagonal PbI2 are presented unambiguously. Finally, the method of proper phase identification and some strategies to control the radiation damage in HRTEM are provided. This warning paves the way to avoid future misinterpretations in HRTEM characterization of perovskite and other electron beam-sensitive materials. 
【Condens. Matter 2021, 6(1), 1; https://doi.org/10.3390/condmat6010001】
Keywords: MAPbI3 perovskite; transmission electron microscopy (TEM); electron diffraction (ED); phase identification; electron beam-sensitive.
【Deng, Y.-H. Common Phase and Structure Misidentifications in High-Resolution TEM Characterization of Perovskite Materials. Condens. Matter 2021, 6, 1.】
Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection
Authors: Jay Sinha, Manollas M
Date: 2020-06-26 00:00:00
Tasks: Anomaly Detection, Intrusion Detection, Network Intrusion Detection

The need for Network Intrusion Detection systems has risen since usage of cloud technologies has become mainstream. With the ever growing network traffic, Network Intrusion Detection is a critical part of network security and a very efficient NIDS is a must, given new variety of attack arises frequently. These Intrusion Detection systems are built on either a pattern matching system or AI/ML based anomaly detection system. Pattern matching methods usually have a high False Positive Rates whereas the AI/ML based method, relies on finding metric/feature or correlation between set of metrics/features to predict the possibility of an attack. The most common of these is KNN, SVM etc., operate on a limited set of features and have less accuracy and still suffer from higher False Positive Rates. In this paper, we propose a deep learning model combining the distinct strengths of a Convolutional Neural Network and a Bi-directional LSTM to incorporate learning of spatial and temporal features of the data. For this paper, we use publicly available datasets NSL-KDD and UNSW-NB15 to train and test the model. The proposed model offers a high detection rate and comparatively lower False Positive Rate. The proposed model performs better than many state-of-the-art Network Intrusion Detection systems leveraging Machine Learning/Deep Learning models.
Scalable and Hybrid Ensemble-Based Causality Discovery
Authors: Pei Guo, Achuna Ofonedu, Jianwu Wang
Date: 2020-12-24 00:00:00
Tasks: Benchmarking, Distributed Computing, Time Series, Time Series Analysis

Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience. To discover causal relationships, many data-driven causality discovery methods, e.g., Granger causality, PCMCI and Dynamic Bayesian Network, have been proposed. Many of these causality discovery approaches mine time series data and generate a directed causality graph where each graph edge denotes a causeeffect relationship between the two connected graph nodes. Our benchmarking of different causality discovery approaches with real-world climate data shows these approaches often generate quite different causality results with the same input dataset due to their internal learning mechanism differences. Meanwhile, there are ever-increasing available data in virtually every discipline, which makes it more and more difficult to use existing causality discovery algorithms to produce causality results within reasonable time. To address these two challenges, this paper utilizes data partitioning and ensemble techniques, and proposes a two-phase hybrid causality ensemble framework. The framework first conducts phase 1 data ensemble for partitioned data and then conducts phase 2 algorithm ensemble from data ensemble results. To achieve scalability, we further parallelize the ensemble approaches via the Spark big data analytics engine. Our experiments show that our proposed approaches achieve good accuracy through ensemble and high scalability through data-parallelization in distributed computing environments.
Quickest Detection of COVID-19 Pandemic Onset
Authors: Paolo Braca, Domenico Gaglione, Stefano Marano, Leonardo M. Millefiori, Peter Willett, Krishna Pattipati
Date: 2020-11-20 00:00:00
Tasks: Time Series Analysis

This paper develops an easily-implementable version of Page's CUSUM quickest-detection test, designed to work in certain composite hypothesis scenarios with time-varying data statistics. The decision statistic can be cast in a recursive form and is particularly suited for on-line analysis. By back-testing our approach on publicly-available COVID-19 data we find reliable early warning of infection flare-ups, in fact sufficiently early that the tool may be of use to decision-makers on the timing of restrictive measures that may in the future need to be taken.
Life insurance policies with cash flows subject to random interest rate changes
Authors: David R. Baños
Date: 2020-12-31 00:00:00

The main purpose of this work is to derive a partial differential equation for the reserves of life insurance liabilities subject to stochastic interest rates where the benefits and premiums depend directly on changes in the interest rate curve. In particular, we allow the payment streams to depend on the performance of an overnight technical interest rate, making them stochastic as well. This opens up for considering new types of contracts based on the performance of the insurer's returns on their own investments. We provide explicit solutions for the reserves when the premiums and benefits vary according to interest rate levels or averages under the Vasicek model and conduct some simulations computing reserve surfaces numerically. We also give an example of a reinsurance treaty taking over pension payments when the insurer's average returns fall under some specified threshold.
Multi-direction Networks with Attentional Spectral Prior for Hyperspectral Image Classification
Authors: Bobo Xi, Jiaojiao Li, Yunsong Li, Rui Song, Yuchao Xiao, Yanzi Shi, Qian Du.
Date: 2020-12-11 00:00:00
Tasks: General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Convolutional neural networks (CNNs) have achieved prominent progress in recent years and demonstrated
remarkable properties in spectral-spatial hyperspectral image (HSI) classification. However, conventional spatial-context based CNNs commonly adopt the single patchwise scheme to represent the to-be-classified samples, which often fails to completely investigate the wealthy spectral-spatial information in complicated situations. For instance, it has great probability to cause misclassifications on the irregular or inhomogeneous areas, especially for the borders across different classes. To counteract this deficiency, we propose a unified multi-direction network (MDN) for HSI classification, which can exhaustively explore the abundant spectral and detailed spatial-context information through multi-direction samples. Additionally, considering the image-spectrum merged structure of the HSI, 3D Squeeze-and-Excitation residual (3DSERes) blocks are devised in each stream of the framework to consecutively learn the spectral and spatial from low-level to high-level features. Specifically, the 3DSERes can not only facilitate fluent gradient in backpropagation through skip-connections, but also emphasize the significant spectral-spatial features and constrain the futile ones. This characteristic is beneficial to enhance the model's generalization capability even with limited training samples. Furthermore, for properly aggregating the multi-direction deep features, we exploit the simple yet effective attentional spectral prior (ASP) creatively through leveraging the original spectral correlations. Extensive experiment results on three benchmark datasets indicate that the proposed MDN-ASP can achieve promising classification performance compared to the state-of-the-art methods.