pwc-archive/papers-with-abstracts huggingface dataset:
Unifying Epidemic Models with Mixtures
Authors: Arnab Sarker, Ali Jadbabaie, Devavrat Shah
Date: 2022-01-07 00:00:00
Tasks: Time Series, Time Series Analysis

The COVID-19 pandemic has emphasized the need for a robust understanding of epidemic models. Current models of epidemics are classified as either mechanistic or non-mechanistic: mechanistic models make explicit assumptions on the dynamics of disease, whereas non-mechanistic models make assumptions on the form of observed time series. Here, we introduce a simple mixture-based model which bridges the two approaches while retaining benefits of both. The model represents time series of cases and fatalities as a mixture of Gaussian curves, providing a flexible function class to learn from data compared to traditional mechanistic models. Although the model is non-mechanistic, we show that it arises as the natural outcome of a stochastic process based on a networked SIR framework. This allows learned parameters to take on a more meaningful interpretation compared to similar non-mechanistic models, and we validate the interpretations using auxiliary mobility data collected during the COVID-19 pandemic. We provide a simple learning algorithm to identify model parameters and establish theoretical results which show the model can be efficiently learned from data. Empirically, we find the model to have low prediction error. The model is available live at covidpredictions.mit.edu. Ultimately, this allows us to systematically understand the impacts of interventions on COVID-19, which is critical in developing data-driven solutions to controlling epidemics.
ChartText: Linking Text with Charts in Documents
Authors: Joao Pinheiro, Jorge Poco
Date: 2022-01-13 00:00:00
Tasks: Reading Comprehension

Recent works show that interactive documents connecting text with visualizations facilitate reading comprehension. However, creating this type of content requires specialized knowledge. We present ChartText, a method that links text with visualizations in this work. Our approach supports documents that include bar charts, line charts, and scatter plots. ChartText receives the visual encoding of the visualization and its associated text as input. It then performs the linking in two stages: The matching stage creates individual links relating simple phrases between the text and the chart. Then, it combines the individual links according to the visual channels in the grouping stage, building more meaningful connections. We use two datasets to design and evaluate our method; the first comes from web documents (24 bar charts and texts) and the second from academic documents (25 bar charts, 25 line charts, and 25 scatter plots with their texts). Our experiments show that our method obtains F1 scores of 0.50 and 0.66 on both datasets. We can also use a semi-automatic approach correcting individual links; in this case, the scores rise to 0.68 and 0.84, respectively. To show the usefulness of our technique, we implement two proofs of concept. We create interactive documents using graphic overlays in the first one, facilitating the reading experience. We use voice instead of text to annotate charts in real-time in the second. For example, in a videoconference, our technique can automatically annotate a chart following the presenter's description.
Ontological model identification based on data from heterogeneous sources
Authors: A. Kalinin, E. Shikov, D. Vaganov, A. Lysenko
Date: 2022-01-13 00:00:00

The development of a company often entails the emergence of autonomous data sources with different structural and technological organization. This can lead to the inability of data analysis at a high level and a violation of the integrity and reliability of data within the organization, hindering the adoption of high-quality decisions and further development of the company. This problem can be solved by implementing a higher abstraction, representing heterogeneous organization data in a single space by combining them into a single knowledge graph. We propose a framework capable of autonomous construction of an organization's knowledge graph based on semi-structured data from various sources by finding links between sources based on data with an arbitrary structure, and combining document collections into single entities. The results of tests show the applicability of the developed approach for constructing a knowledge graph based on partially-structured data from various sources and the high efficiency of the approach based on the metrics of completeness of data storage subsystems coverage (11 out of 11) and filtering false connections (there are only 2.5 connections of collections with neighbors on average in the final graph).
Event-centric Query Suggestion for Online News
Authors: Sachin, Dhaval Patel
Date: 2022-01-12 00:00:00
Tasks: Articles, Information Retrieval, Retrieval

Query suggestion refers to the task of suggesting relevant and related queries to a search engine user to help in query formulation process and to expedite information retrieval with minimum amount of effort. It is highly useful in situations where the search requirements are not well understood and hence it has been widely adopted by search engines to guide users' search activity. For news websites, user queries have a time sensitive nature inherent in them. When some new event happens, there is a sudden burst in queries related to that event and such queries are sustained over a period of time before fading away with that event. In addition to this temporal aspect of search queries fired at news websites, they have an addition distinct quality, i.e., they are intended to get event related information majority of the times. Existing work on generating query suggestions involves analyzing query logs to suggest queries which are relevant and related to the search intent of the user. But in case of news websites, when there is a sudden burst in information related to a particular event, there are not enough search queries fired by other users which leads to lack of click data, and hence giving query suggestions related to some old event or even some irrelevant suggestions altogether. Another problem with query logs in the context of online news is that, they mostly contain queries related to popular events and hence fail to capture less popular events or events which got overshadowed by some other more sensational event. We propose a novel approach to generate event-centric query suggestions using metadata of news articles published by news media. We compared our proposed framework with existing state of the art query suggestion mechanisms provided by Google News, Bing News, Google Search and Bing Search on various parameters.
CompanyName2Vec: Company Entity Matching Based on Job Ads
Authors: Ran Ziv, Ilan Gronau, Michael Fire
Date: 2022-01-12 00:00:00
Tasks: Data Visualization

Entity Matching is an essential part of all real-world systems that take in structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and integration processes require completion before any data analytics, or further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights, supports data visualization, and guides further analytic approaches to the data. Here, we focus on organization entity matching. We introduce CompanyName2Vec, a novel algorithm to solve company entity matching (CEM) using a neural network model to learn company name semantics from a job ad corpus, without relying on any information on the matched company besides its name. Based on a real-world data, we show that CompanyName2Vec outperforms other evaluated methods and solves the CEM challenge with an average success rate of 89.3%.
A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
Authors: Mauro. F. Pinto, Adriana Leal, Fábio Lopes, António Dourado, Pedro Martins & César A. Teixeira
Date: 2021-02-09 00:00:00
Proceeding: Scientific Reports 2021 2
Tasks: EEG, Electroencephalogram (EEG), Prediction, Seizure prediction

Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is a critical step. Past approaches failed to attain real-world applicability due to lack of generalization capacity. More recently, deep learning techniques may outperform traditional classifiers and handle time dependencies. However, despite the existing efforts for providing interpretable insights, clinicians may not be willing to make high-stake decisions based on them. Furthermore, a disadvantageous aspect of the more usual seizure prediction pipeline is its modularity and significant independence between stages. An alternative could be the construction of a search algorithm that, while considering pipeline stages’ synergy, fine-tunes the selection of a reduced set of features that are widely used in the literature and computationally efficient. With extracranial recordings from 19 patients suffering from temporal-lobe seizures, we developed a patient-specific evolutionary optimization strategy, aiming to generate the optimal set of features for seizure prediction with a logistic regression classifier, which was tested prospectively in a total of 49 seizures and 710 h of continuous recording and performed above chance for 32% of patients, using a surrogate predictor. These results demonstrate the hypothesis of pre-ictal period identification without the loss of interpretability, which may help understanding brain dynamics leading to seizures and improve prediction algorithms.
Generative Adversarial Network for Text-to-Face Synthesis and Manipulation with Pretrained BERT Model
Authors: Yutong Zhou，Nobutaka Shimada
Date: 2022-01-12 00:00:00
Proceeding: FG 2022 1
Tasks: Attribute, Face Generation, Generative Adversarial Network, SSIM, Text-to-Face Generation

This work proposes a cyclic generative adversarial network with spatial-wise and channel-wise attention modules for text-to-face synthesis and manipulation. Then, we explore the pre-trained transformer-based BERT model to obtain text embedding. Furthermore, dual-layer perceptual loss and SSIM loss are introduced to reinforce the delicate features and preserve facial identity during the manipulation task. Additionally, we adopt a novel Flickr-Faces-HQ with Text descriptions (FFHQ-Text) dataset with numerous facial attribute annotations to advance the development of the text-to-face task. In particular, by introducing the StyleGAN encoder for learning latent representations to our proposed post-processing method, we demonstrate that even training on a smaller text-to-face dataset can synthesize more realistic images. Experimental results reveal the effectiveness of our approach, which generates photo-realistic facial images, edits the specific facial attribute with the correlated keywords manipulation, outperforms previous state-of-the-art methods both in quality and quantity, and suggests promising future directions.
Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm
Authors: Kasra Rafiezadeh Shahi, Pedram Ghamisi, Behnood Rasti, Paul Scheunders, Richard Gloaguen
Date: 2021-12-06 00:00:00
Proceeding: IEEE 2021 12
Tasks: Clustering, Deep Clustering, Earth Observation

The ever-growing developments in technology to capture different types of image data [e.g., hyperspectral imaging and light detection and ranging (LiDAR)-derived digital surface model (DSM)], along with new processing techniques, have led to a rising interest in imaging applications for Earth observation. However, analyzing such datasets in parallel, remains a challenging task. In this article, we propose a multisensor deep clustering (MDC) algorithm for the joint processing of multisource imaging data. The architecture of MDC is inspired by autoencoder (AE)-based networks. The MDC paradigm includes three parallel networks, a spectral network using an autoencoder structure, a spatial network using a convolutional autoencoder structure, and lastly, a fusion network that reconstructs the concatenated image information from the concatenated latent features from the spatial and spectral network. The proposed algorithm combines the reconstruction losses obtained by the aforementioned networks to optimize the parameters (i.e., weights and bias) of all three networks simultaneously. To validate the performance of the proposed algorithm, we use two multisensor datasets from different applications (i.e., geological and rural sites) as benchmarks. The experimental results confirm the superiority of our proposed deep clustering algorithm compared to a number of state-of-the-art clustering algorithms. The code will be available at [Online]. Available: https://github.com/Kasra2020/MDC .
Comparison of Classification Algorithms for COVID19 Detection using Cough Acoustic Signals
Authors: Yunus Emre Erdoğan, Ali Narin
Date: 2022-01-13 00:00:00
Tasks: Diagnostic

The epidemic disease, called the new coronavirus (COVID19), firstly occurred in Wuhan, China in December 2019. COVID19 was announced as an epidemic by World Health Organization soon after. Some of the symptoms of this disease are fever, cough, shortness of breath and difficulty in breathing. In more severe cases, death may occur as a result of infection. The most significant question in fighting the pandemic and controlling the epidemic is the early diagnosis of COVID19(+) patients and the follow-up of these patients. Therefore, various diagnostic mechanisms are used. Additionally to the RT-PCR test, medical imaging methods have been utilized, especially in the detection of COVID19(+) patients. In this study, an alternative approach was proposed by using cough data, which is one of the most prominent symptoms of COVID19(+) patients. The cough acoustic public dataset on the Virufy website was used. The entire data was normalized using z-normalization technique. The performance of the features obtained via the 5-layer empirical mode decomposition method and the performances of different classifiers has been compared. As the classifier algorithm, 5 different algorithms were used. The highest accuracy and F1-score performances were obtained by using Ensemble-Bagged-Trees algorithm as 90.6% and 90.5%, respectively. On the other hand, other classification algorithms used in the study are Support Vector Machines, Logistic Regression, Linear Discriminant Analysis and k-Nearest Neigbors, respectively. According to the results obtained, choosing the right classifier algorithm provides high results. Thus, it is clear that using cough acoustic data, those with COVID19(+) can be detected easily and effectively.
A Minimax Framework for Two-Agent Scheduling with Inertial Constraints
Authors: Feihong Yang, Yuan Shen
Date: 2022-01-13 00:00:00
Tasks: Scheduling, Vocal Bursts Valence Prediction

Autonomous agents are promising in applications such as intelligent transportation and smart manufacturing, and scheduling of agents has to take their inertial constraints into consideration. Most current researches require the obedience of all agents, which is hard to achieve in non-dedicated systems such as traffic intersections. In this article, we establish a minimax framework for the scheduling of two inertially constrained agents with no cooperation assumptions. Specifically, we first provide a unified and sufficient representation for various types of situation information, and define a state value function characterizing the agent's preference of states under a given situation. Then, the minimax control policy along with the calculation methods is proposed which optimizes the worst-case state value function at each step, and the safety guarantee of the policy is also presented. Furthermore, several generalizations are introduced on the applicable scenarios of the proposed framework. Numerical simulations show that the minimax control policy can reduce the largest scheduling cost by $13.4\%$ compared with queueing and following policies. Finally, the effects of decision period, observation period and inertial constraints are also numerically discussed.
Passivity-based design and analysis of phase-locked loops
Authors: Daniele Zonetti, Oriol Gomis-Bellmunt, Eduardo Prieto-Araujo, Marc Cheah-Mane
Date: 2022-01-13 00:00:00

We consider a grid-connected voltage source converter (VSC) and address the problem of estimating the grid angle and frequency, information that is essential for an appropriate operation of the converter. We design phase-locked loop (PLL) algorithms with guaranteed stability properties, under the assumption that the grid is characterized by relatively high short-circuit-ratio (SCR) and inertia. In particular we derive, using passivity arguments, generalization of the conventional synchronous reference frame (SRF) and arctangent (ATAN) PLL, which are the standard solutions in power applications. The analysis is further extended to the case of connection of the VSC to a low-inertia grid, ensuring robustness of the algorithms in case of frequency variations. The results are validated on a benchmark including the grid, the VSC and related controllers.
Online State Estimation for Supervisor Synthesis in Discrete-Event Systems with Communication Delays and Losses
Authors: Yunfeng Hou, Yunfeng Ji, Gang Wang, Ching-Yen Weng, Qingdu Li
Date: 2022-01-13 00:00:00
Tasks: State Estimation

In the context of networked discrete-event systems (DESs), communication delays and losses exist between the plant and the supervisor for observation and between the supervisor and the actuator for control. In this paper, we first introduce a new framework for supervisory control of networked DESs. Under the introduced framework, we address the state estimation problem for supervisor synthesis of networked DESs with both communication delays and losses. The estimation algorithm considers the effect of the controls imposed on the system. Additionally, the estimation algorithm is based on the control decisions available up to the moment, and all the future control decisions are assumed to be unknowable. Two notions, called "observation channel configuration" for tracking observation delays and losses and "control channel configuration" for tracking control delays and losses, are defined. Then, we introduce an online approach for state estimation of the controlled system. Compared with the existing approach, the proposed approach under the introduced framework can estimate the state of the controlled system more accurately. As an application of the proposed approach, we finally show that the existing methods can be easily applied to synthesize maximally permissible and safe networked supervisors.
Towards Real-Time Monitoring and Control of Water Networks
Authors: Ahmed Elkhashap, Daniel Rüschen, Dirk Abel
Date: 2022-01-12 00:00:00

Water networks are used in numerous applications, all of which have the essential task of real-time monitoring and control of water states. A framework for the generation of efficient models of water networks suitable for real-time monitoring and control purposes is proposed. The proposed models preserve the distributed parameter character of the connected local elements. Hence, the spatial resolution of the property under consideration is recovered. The real-time feasibility of the network model is ensured by means of reduced order modeling of the models constituting components. A novel model order reduction procedure that preserves the model parametric dependency is introduced. The proposed concept is evaluated with the water temperature as the property under consideration. The formulated model is applied for the prediction of the water temperature within an experimental test bench of a 60 meter exemplary circulation network at the company VIEGA. A reduced order model (ROM) with a 50 mm spatial resolution, i.e. 1200 discretization points, is constructed and utilized as the identification model for a single path of the test bench. Afterwards, the ROM is evaluated in a Hardware in the Loop experiment for the prediction of the downstream temperature showing high prediction accuracy with mean relative error below 3.5 \%. The ROM single step computation time did not exceed 2 msec highlighting the real-time potential of the method. Moreover, full network model validation experiments featuring both diffusion and transport dominated parts were conducted. The network model is able to predict the temperature evolution, flow rate, and pressure accurately at the different paths of the network with mean relative errors below 4 \%, 2 \%, and 2 \%, respectively.
Machine Learning-enhanced Efficient Spectroscopic Ellipsometry Modeling
Authors: Ayush Arunachalam, S. Novia Berriel, Parag Banerjee, Kanad Basu
Date: 2022-01-01 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery

Over the recent years, there has been an extensive adoption of Machine Learning (ML) in a plethora of real-world applications, ranging from computer vision to data mining and drug discovery. In this paper, we utilize ML to facilitate efficient film fabrication, specifically Atomic Layer Deposition (ALD). In order to make advances in ALD process development, which is utilized to generate thin films, and its subsequent accelerated adoption in industry, it is imperative to understand the underlying atomistic processes. Towards this end, in situ techniques for monitoring film growth, such as Spectroscopic Ellipsometry (SE), have been proposed. However, in situ SE is associated with complex hardware and, hence, is resource intensive. To address these challenges, we propose an ML-based approach to expedite film thickness estimation. The proposed approach has tremendous implications of faster data acquisition, reduced hardware complexity and easier integration of spectroscopic ellipsometry for in situ monitoring of film thickness deposition. Our experimental results involving SE of TiO2 demonstrate that the proposed ML-based approach furnishes promising thickness prediction accuracy results of 88.76% within +/-1.5 nm and 85.14% within +/-0.5 nm intervals. Furthermore, we furnish accuracy results up to 98% at lower thicknesses, which is a significant improvement over existing SE-based analysis, thereby making our solution a viable option for thickness estimation of ultrathin films.
Stem-loop formation drives RNA folding in mechanical unzipping experiments
Authors: Paolo Rissone, Cristiano Valim Bizarro, Felix Ritort
Date: 2022-01-13 00:00:00

Accurate knowledge of RNA hybridization is essential for understanding RNA structure and function. Here we mechanically unzip and rezip a 2-kbp RNA hairpin and derive the 10 nearest-neighbor base pair (NNBP) RNA free energies in sodium and magnesium with 0.1 kcal/mol precision using optical tweezers. Notably, force-distance curves (FDCs) exhibit strong irreversible effects with hysteresis and several intermediates, precluding the extraction of the NNBP energies with currently available methods. The combination of a suitable RNA synthesis with a tailored pulling protocol allowed us to obtain the fully reversible FDCs necessary to derive the NNBP energies. We demonstrate the equivalence of sodium and magnesium free-energy salt corrections at the level of individual NNBP. To characterize the irreversibility of the unzipping-rezipping process, we introduce a barrier energy landscape of the stem-loop structures forming along the complementary strands, which compete against the formation of the native hairpin. This landscape correlates with the hysteresis observed along the FDCs. RNA sequence analysis shows that base stacking and base pairing stabilize the stem-loops that kinetically trap the long-lived intermediates observed in the FDC. Stem-loops formation appears as a general mechanism to explain a wide range of behaviors observed in RNA folding.