pwc-archive/papers-with-abstracts huggingface dataset:
Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model
Authors: Amartya Bhattacharya, Manish Gawali, Jitesh Seth, Viraj Kulkarni
Date: 2022-04-22 00:00:00
Tasks: Binary Classification, Federated Learning, X-ray Classification

While developing artificial intelligence (AI)-based algorithms to solve problems, the amount of data plays a pivotal role - large amount of data helps the researchers and engineers to develop robust AI algorithms. In the case of building AI-based models for problems related to medical imaging, these data need to be transferred from the medical institutions where they were acquired to the organizations developing the algorithms. This movement of data involves time-consuming formalities like complying with HIPAA, GDPR, etc.There is also a risk of patients' private data getting leaked, compromising their confidentiality. One solution to these problems is using the Federated Learning framework. Federated Learning (FL) helps AI models to generalize better and create a robust AI model by using data from different sources having different distributions and data characteristics without moving all the data to a central server. In our paper, we apply the FL framework for training a deep learning model to solve a binary classification problem of predicting the presence or absence of COVID-19. We took three different sources of data and trained individual models on each source. Then we trained an FL model on the complete data and compared all the model performances. We demonstrated that the FL model performs better than the individual models. Moreover, the FL model performed at par with the model trained on all the data combined at a central server. Thus Federated Learning leads to generalized AI models without the cost of data transfer and regulatory overhead.
Adversarial Estimators
Authors: Jonas Metzger
Date: 2022-04-22 00:00:00
Tasks: Causal Inference, Econometrics

We develop an asymptotic theory of adversarial estimators ('A-estimators'). They generalize maximum-likelihood-type estimators ('M-estimators') as their average objective is maximized by some parameters and minimized by others. This class subsumes the continuous-updating Generalized Method of Moments, Generative Adversarial Networks and more recent proposals in machine learning and econometrics. In these examples, researchers state which aspects of the problem may in principle be used for estimation, and an adversary learns how to emphasize them optimally. We derive the convergence rates of A-estimators under pointwise and partial identification, and the normality of functionals of their parameters. Unknown functions may be approximated via sieves such as deep neural networks, for which we provide simplified low-level conditions. As a corollary, we obtain the normality of neural-net M-estimators, overcoming technical issues previously identified by the literature. Our theory yields novel results about a variety of A-estimators, providing intuition and formal justification for their success in recent applications.
NLP Based Anomaly Detection for Categorical Time Series
Authors: Matthew Horak, Sowmya Chandrasekaran, Giovanni Tobar
Date: 2022-04-22 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Identifying anomalies in large multi-dimensional time series is a crucial and difficult task across multiple domains. Few methods exist in the literature that address this task when some of the variables are categorical in nature. We formalize an analogy between categorical time series and classical Natural Language Processing and demonstrate the strength of this analogy for anomaly detection and root cause investigation by implementing and testing three different machine learning anomaly detection and root cause investigation models based upon it.
Finite-Time Analysis of Temporal Difference Learning: Discrete-Time Linear System Perspective
Authors: Donghwan Lee, Do Wan Kim
Date: 2022-04-22 00:00:00
Tasks: Reinforcement Learning (RL)

TD-learning is a fundamental algorithm in the field of reinforcement learning (RL), that is employed to evaluate a given policy by estimating the corresponding value function for a Markov decision process. While significant progress has been made in the theoretical analysis of TD-learning, recent research has uncovered guarantees concerning its statistical efficiency by developing finite-time error bounds. This paper aims to contribute to the existing body of knowledge by presenting a novel finite-time analysis of tabular temporal difference (TD) learning, which makes direct and effective use of discrete-time stochastic linear system models and leverages Schur matrix properties. The proposed analysis can cover both on-policy and off-policy settings in a unified manner. By adopting this approach, we hope to offer new and straightforward templates that not only shed further light on the analysis of TD-learning and related RL algorithms but also provide valuable insights for future research in this domain.
Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions
Authors: Xin Li, Hsinchun Chen, Zan Huang, Hua Su, Jesse D. Martinez
Date: 2022-04-22 00:00:00

Gene/protein interactions provide critical information for a thorough understanding of cellular processes. Recently, considerable interest and effort has been focused on the construction and analysis of genome-wide gene networks. The large body of biomedical literature is an important source of gene/protein interaction information. Recent advances in text mining tools have made it possible to automatically extract such documented interactions from free-text literature. In this paper, we propose a comprehensive framework for constructing and analyzing large-scale gene functional networks based on the gene/protein interactions extracted from biomedical literature repositories using text mining tools. Our proposed framework consists of analyses of the network topology, network topology-gene function relationship, and temporal network evolution to distill valuable information embedded in the gene functional interactions in literature. We demonstrate the application of the proposed framework using a testbed of P53-related PubMed abstracts, which shows that literature-based P53 networks exhibit small-world and scale-free properties. We also found that high degree genes in the literature-based networks have a high probability of appearing in the manually curated database and genes in the same pathway tend to form local clusters in our literature-based networks. Temporal analysis showed that genes interacting with many other genes tend to be involved in a large number of newly discovered interactions.
Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach
Authors: Xin Li, Hsinchun Chen, Jiexun Li, Zhu Zhang
Date: 2022-04-22 00:00:00

Predicting gene functions is a challenge for biologists in the post genomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.
Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classification
Authors: Jared Mowery
Date: 2022-04-22 00:00:00
Tasks: Classification, Clustering, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification

Background: Neural networks produce biased classification results due to correlation bias (they learn correlations between their inputs and outputs to classify samples, even when those correlations do not represent cause-and-effect relationships). Objective: This study introduces a fully unsupervised method of mitigating correlation bias, demonstrated with sentiment classification on COVID-19 social media data. Methods: Correlation bias in sentiment classification often arises in conversations about controversial topics. Therefore, this study uses adversarial learning to contrast clusters based on sentiment classification labels, with clusters produced by unsupervised topic modeling. This discourages the neural network from learning topic-related features that produce biased classification results. Results: Compared to a baseline classifier, neural contrastive clustering approximately doubles accuracy on bias-prone sentences for human-labeled COVID-19 social media data, without adversely affecting the classifier's overall F1 score. Despite being a fully unsupervised approach, neural contrastive clustering achieves a larger improvement in accuracy on bias-prone sentences than a supervised masking approach. Conclusions: Neural contrastive clustering reduces correlation bias in sentiment text classification. Further research is needed to explore generalizing this technique to other neural network architectures and application domains.
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization
Authors: Hyunji Kim, Ahsan Pervaiz, Henry Hoffmann, Michael Carbin, Yi Ding
Date: 2022-04-22 00:00:00
Tasks: Safe Exploration

Modern computer systems need to execute under strict safety constraints (e.g., a power limit), but doing so often conflicts with their ability to deliver high performance (i.e. minimal latency). Prior work uses machine learning to automatically tune hardware resources such that the system execution meets safety constraints optimally. Such solutions monitor past system executions to learn the system's behavior under different hardware resource allocations before dynamically tuning resources to optimize the application execution. However, system behavior can change significantly between different applications and even different inputs of the same applications. Hence, the models learned using data collected a priori are often suboptimal and violate safety constraints when used with new applications and inputs. To address this limitation, we introduce the concept of an execution space, which is the cross product of hardware resources, input features, and applications. To dynamically and safely allocate hardware resources from the execution space, we present SCOPE, a resource manager that leverages a novel safe exploration framework. We evaluate SCOPE's ability to deliver improved latency while minimizing power constraint violations by dynamically configuring hardware while running a variety of Apache Spark applications. Compared to prior approaches that minimize power constraint violations, SCOPE consumes comparable power while improving latency by up to 9.5X. Compared to prior approaches that minimize latency, SCOPE achieves similar latency but reduces power constraint violation rates by up to 45.88X, achieving almost zero safety constraint violations across all applications.
EVOTER: Evolution of Transparent Explainable Rule-sets
Authors: Hormoz Shahrzad, Babak Hodjat, Risto Miikkulainen
Date: 2022-04-21 00:00:00

Most AI systems are black boxes generating reasonable outputs for given inputs. Some domains, however, have explainability and trustworthiness requirements that cannot be directly met by these approaches. Various methods have therefore been developed to interpret black-box models after training. This paper advocates an alternative approach where the models are transparent and explainable to begin with. This approach, EVOTER, evolves rule-sets based on simple logical expressions. The approach is evaluated in several prediction/classification and prescription/policy search domains with and without a surrogate. It is shown to discover meaningful rule sets that perform similarly to black-box models. The rules can provide insight into the domain, and make biases hidden in the data explicit. It may also be possible to edit them directly to remove biases and add constraints. EVOTER thus forms a promising foundation for building trustworthy AI systems for real-world applications in the future.
Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language
Authors: Mohammad Karimzadeh Farshbafan, Walid Saad, Merouane Debbah
Date: 2022-04-21 00:00:00
Tasks: Semantic Communication

Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), and they ignore the goal and effectiveness aspects of semantic transmissions. In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. The speaker, acting as an observer of the environment, utilizes the beliefs to transmit an initial description of its observation (called event) to the listener. The listener is then able to infer on the transmitted description and complete it by adding related beliefs to the transmitted beliefs of the speaker. As such, the listener reconstructs the observed event based on the completed description, and it then takes appropriate action in the environment based on the reconstructed event. An optimization problem is defined to determine the perfect and abstract description of the events while minimizing the transmission and inference costs with constraints on the task execution time and belief efficiency. Then, a novel bottom-up curriculum learning (CL) framework based on reinforcement learning is proposed to solve the optimization problem and enable the speaker and listener to gradually identify the structure of the belief set and the perfect and abstract description of the events. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution cost and time, reliability, and belief efficiency.
Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification
Authors: Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Jari Nurmi, Yevgeni Koucheryavy, Joaquín Huerta
Date: 2022-04-21 00:00:00

Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment. Convolutional neural networks (CNNs) are one of the most used neural networks (NNs) due to that they are capable of learning complex patterns from the input data. Another model used in indoor positioning solutions is the Extreme Learning Machine (ELM), which provides an acceptable generalization performance as well as a fast speed of learning. In this paper, we offer a lightweight combination of CNN and ELM, which provides a quick and accurate classification of building and floor, suitable for power and resource-constrained devices. As a result, the proposed model is 58\% faster than the benchmark, with a slight improvement in the classification accuracy (by less than 1\%
CycleSense: Detecting Near Miss Incidents in Bicycle Traffic from Mobile Motion Sensors
Authors: Ahmet-Serdar Karakaya, Thomas Ritter, Felix Biessmann, David Bermbach
Date: 2022-04-21 00:00:00

In cities worldwide, cars cause health and traffic problems whichcould be partly mitigated through an increased modal share of bicycles. Many people, however, avoid cycling due to a lack of perceived safety. For city planners, addressing this is hard as they lack insights intowhere cyclists feel safe and where they do not. To gain such insights,we have in previous work proposed the crowdsourcing platform SimRa,which allows cyclists to record their rides and report near miss incidentsvia a smartphone app. In this paper, we present CycleSense, a combination of signal pro-cessing and Machine Learning techniques, which partially automatesthe detection of near miss incidents, thus making the reporting of nearmiss incidents easier. Using the SimRa data set, we evaluate CycleSenseby comparing it to a baseline method used by SimRa and show that itsignificantly improves incident detection.
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting
Authors: Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen
Date: 2022-04-21 00:00:00
Tasks: model, STS, Time Series, Time Series Analysis

Probabilistic, hierarchically coherent forecasting is a key problem in many practical forecasting applications -- the goal is to obtain coherent probabilistic predictions for a large number of time series arranged in a pre-specified tree hierarchy. In this paper, we present an end-to-end deep probabilistic model for hierarchical forecasting that is motivated by a classical top-down strategy. It jointly learns the distribution of the root time series, and the (dirichlet) proportions according to which each parent time-series is split among its children at any point in time. The resulting forecasts are naturally coherent, and provide probabilistic predictions over all time series in the hierarchy. We experiment on several public datasets and demonstrate significant improvements of up to 26% on most datasets compared to state-of-the-art baselines. Finally, we also provide theoretical justification for the superiority of our top-down approach compared to the more traditional bottom-up modeling.
Staying the course: Locating equilibria of dynamical systems on Riemannian manifolds defined by point-clouds
Authors: Juan M. Bello-Rivas, Anastasia Georgiou, John Guckenheimer, Ioannis G. Kevrekidis
Date: 2022-04-21 00:00:00

We introduce a method to successively locate equilibria (steady states) of dynamical systems on Riemannian manifolds. The manifolds need not be characterized by an a priori known atlas or by the zeros of a smooth map. Instead, they can be defined by point-clouds and sampled as needed through an iterative process. If the manifold is an Euclidean space, our method follows isoclines, curves along which the direction of the vector field $X$ is constant. For a generic vector field $X$, isoclines are smooth curves and every equilibrium lies on isoclines. We generalize the definition of isoclines to Riemannian manifolds through the use of parallel transport: generalized isoclines are curves along which the directions of $X$ are parallel transports of each other. As in the Euclidean case, generalized isoclines of generic vector fields $X$ are smooth curves that connect equilibria of $X$. Our algorithm can be regarded as an extension of the method of Newton trajectories to the manifold setting when the manifold is unknown. This work is motivated by computational statistical mechanics, specifically high dimensional (stochastic) differential equations that model the dynamics of molecular systems. Often, these dynamics concentrate near low-dimensional manifolds and have transitions (saddle points with a single unstable direction) between metastable equilibria. We employ iteratively sampled data and isoclines to locate these saddle points. Coupling a black-box sampling scheme (e.g., Markov chain Monte Carlo) with manifold learning techniques (diffusion maps in the case presented here), we show that our method reliably locates equilibria of $X$.
ICDBigBird: A Contextual Embedding Model for ICD Code Classification
Authors: George Michalopoulos, Michal Malyska, Nicola Sahar, Alexander Wong, Helen Chen
Date: 2022-04-21 00:00:00
Proceeding: BioNLP (ACL) 2022 5
Tasks: Classification, Code Classification, Decision Making, Management

The International Classification of Diseases (ICD) system is the international standard for classifying diseases and procedures during a healthcare encounter and is widely used for healthcare reporting and management purposes. Assigning correct codes for clinical procedures is important for clinical, operational, and financial decision-making in healthcare. Contextual word embedding models have achieved state-of-the-art results in multiple NLP tasks. However, these models have yet to achieve state-of-the-art results in the ICD classification task since one of their main disadvantages is that they can only process documents that contain a small number of tokens which is rarely the case with real patient notes. In this paper, we introduce ICDBigBird a BigBird-based model which can integrate a Graph Convolutional Network (GCN), that takes advantage of the relations between ICD codes in order to create 'enriched' representations of their embeddings, with a BigBird contextual model that can process larger documents. Our experiments on a real-world clinical dataset demonstrate the effectiveness of our BigBird-based model on the ICD classification task as it outperforms the previous state-of-the-art models.
Interpolation of Missing Swaption Volatility Data using Gibbs Sampling on Variational Autoencoders
Authors: Ivo Richert, Robert Buch
Date: 2022-04-21 00:00:00
Tasks: Imputation, Missing Values

Albeit of crucial interest for both financial practitioners and researchers, market-implied volatility data of European swaptions often exhibit large portions of missing quotes due to illiquidity of the various underlying swaption instruments. In this case, standard stochastic interpolation tools like the common SABR model often cannot be calibrated to observed implied volatility smiles, due to data being only available for the at-the-money quote of the respective underlying swaption. Here, we propose to infer the geometry of the full unknown implied volatility cube by learning stochastic latent representations of implied volatility cubes via variational autoencoders, enabling inference about the missing volatility data conditional on the observed data by an approximate Gibbs sampling approach. Imputed estimates of missing quotes can afterwards be used to fit a standard stochastic volatility model. Since training data for the employed variational autoencoder model is usually sparsely available, we test the robustness of the approach for a model trained on synthetic data on real market quotes and we show that SABR interpolated volatilites calibrated to reconstructed volatility cubes with artificially imputed missing values differ by not much more than two basis points compared to SABR fits calibrated to the complete cube. Moreover, we show how the imputation can be used to successfully set up delta-neutral portfolios for hedging purposes.
STD: A Seasonal-Trend-Dispersion Decomposition of Time Series
Authors: Grzegorz Dudek
Date: 2022-04-21 00:00:00
Tasks: Time Series, Time Series Analysis

The decomposition of a time series is an essential task that helps to understand its very nature. It facilitates the analysis and forecasting of complex time series expressing various hidden components such as the trend, seasonal components, cyclic components and irregular fluctuations. Therefore, it is crucial in many fields for forecasting and decision processes. In recent years, many methods of time series decomposition have been developed, which extract and reveal different time series properties. Unfortunately, they neglect a very important property, i.e. time series variance. To deal with heteroscedasticity in time series, the method proposed in this work -- a seasonal-trend-dispersion decomposition (STD) -- extracts the trend, seasonal component and component related to the dispersion of the time series. We define STD decomposition in two ways: with and without an irregular component. We show how STD can be used for time series analysis and forecasting.