pwc-archive/papers-with-abstracts huggingface dataset:
A compartmental model for Xylella fastidiosa diseases with explicit vector seasonal dynamics
Authors: Àlex Giménez-Romero, Eduardo Moralejo, Manuel A. Matías
Date: 2022-12-10 00:00:00
Tasks: Bayesian Inference

The bacterium Xylella fastidiosa (Xf) is mainly transmitted by the spittlebug, Philaenus spumarius, in Europe, where it has caused significant economic damage to olive and almond trees. Understanding the factors that determine disease dynamics in pathosystems that share similarities can help design control strategies focused on minimizing transmission chains. Here we introduce a compartmental model for Xf-caused diseases in Europe that accounts for the main relevant epidemiological processes, including the seasonal dynamics of P. spumarius. The model was confronted with epidemiological data from the two major outbreaks of Xf in Europe, the olive quick disease syndrome (OQDS) in Apulia, Italy, caused by the subspecies pauca, and the almond leaf scorch disease (ALSD) in Majorca, Spain, caused by subspecies multiplex and fastidiosa. Using a Bayesian inference framework, we show how the model successfully reproduces the general field data in both diseases. In a global sensitivity analysis, the vector-plant and plant-vector transmission rates, together with the vector removal rate, were the most influential parameters in determining the time of the infected host population peak, the incidence peak and the final number of dead hosts. We also used our model to check different vector-based control strategies, showing that a joint strategy focused on increasing the rate of vector removal while lowering the number of annual newborn vectors is optimal for disease control.
Invasion and Interaction Determine Population Composition in an Open Evolving System
Authors: Youngjai Park, Takashi Shimada, Seung-Woo Son, Hye Jin Park
Date: 2022-12-10 00:00:00
Tasks: Diversity

It is well-known that interactions between species determine the population composition in an ecosystem. Conventional studies have focused on fixed population structures to reveal how interactions shape population compositions. However, interaction structures are not fixed, but change over time due to invasions. Thus, invasion and interaction play an important role in shaping communities. Despite its importance, however, the interplay between invasion and interaction has not been well explored. Here, we investigate how invasion affects the population composition with interactions in open evolving systems considering generalized Lotka-Volterra-type dynamics. Our results show that the system has two distinct regimes. One is characterized by low diversity with abrupt changes of dominant species in time, appearing when the interaction between species is strong and invasion slowly occurs. On the other hand, frequent invasions can induce higher diversity with slow changes in abundances despite strong interactions. It is because invasion happens before the system reaches its equilibrium, which drags the system from its equilibrium all the time. All species have similar abundances in this regime, which implies that fast invasion induces regime shift. Therefore, whether invasion or interaction dominates determines the population composition.
Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package
Authors: Rafael A. Moral, Rishabh Vishwakarma, John Connolly, Laura Byrne, Catherine Hurley, John A. Finn, Caroline Brophy
Date: 2022-12-09 00:00:00
Tasks: Diversity

BEF studies aim to understand how ecosystems respond to a gradient of species diversity. Diversity-Interactions (DI) models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The number of species in the community (richness) is also implicitly modelled through this approach. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. In this paper, we introduce the DImodels R package for implementing DI models. We also compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. We show that using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference.
Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery
Authors: Jianing Xi, Zhen Deng, Yang Liu, Qian Wang, Wen Shi
Date: 2022-12-09 00:00:00
Tasks: Specificity, Vocal Bursts Type Prediction

Driver event discovery is a crucial demand for breast cancer diagnosis and therapy. Especially, discovering subtype-specificity of drivers can prompt the personalized biomarker discovery and precision treatment of cancer patients. still, most of the existing computational driver discovery studies mainly exploit the information from DNA aberrations and gene interactions. Notably, cancer driver events would occur due to not only DNA aberrations but also RNA alternations, but integrating multi-type aberrations from both DNA and RNA is still a challenging task for breast cancer drivers. On the one hand, the data formats of different aberration types also differ from each other, known as data format incompatibility. One the other hand, different types of aberrations demonstrate distinct patterns across samples, known as aberration type heterogeneity. To promote the integrated analysis of subtype-specific breast cancer drivers, we design a "splicing-and-fusing" framework to address the issues of data format incompatibility and aberration type heterogeneity respectively. To overcome the data format incompatibility, the "splicing-step" employs a knowledge graph structure to connect multi-type aberrations from the DNA and RNA data into a unified formation. To tackle the aberration type heterogeneity, the "fusing-step" adopts a dynamic mapping gene space integration approach to represent the multi-type information by vectorized profiles. The experiments also demonstrate the advantages of our approach in both the integration of multi-type aberrations from DNA and RNA and the discovery of subtype-specific breast cancer drivers. In summary, our "splicing-and-fusing" framework with knowledge graph connection and dynamic mapping gene space fusion of multi-type aberrations data from DNA and RNA can successfully discover potential breast cancer drivers with subtype-specificity indication.
Practical and scalable simulations of non-Markovian stochastic processes and temporal networks with individual node properties
Authors: Aurelien Pelissier, Miroslav Phan, Didier Le Bail, Niko Beerenwinkel, Maria Rodriguez Martinez
Date: 2022-12-09 00:00:00
Tasks: Attribute, Epidemiology, Sociology

Discrete stochastic processes are prevalent in natural systems, with applications in physics, biochemistry, epidemiology, sociology, and finance. While analytic solutions often cannot be derived, existing simulation frameworks can generate stochastic trajectories compatible with the dynamical laws underlying the random phenomena. Still, most simulation algorithms assume the system dynamics are memoryless (Markovian assumption), under which assumption, future occurrences only depend on the system's present state. This enables efficient and exact simulation via the Gillespie algorithm. However, many real-world systems are inherently non-Markovian and exhibit memory effects. Such systems are difficult to study analytically, and current numerical methods are often computationally expensive or limited by strong simplifying assumptions that conflict with empirical data. To address these limitations, we introduce the Rejection-based Gillespie algorithm for non-Markovian Reactions (REGIR), a general and scalable framework for simulating non-Markovian stochastic systems with arbitrary inter-event time distributions. REGIR provides user-defined accuracy while preserving the same asymptotic computational complexity as the classical Gillespie algorithm. We derive a lower bound on REGIR's approximation accuracy and demonstrate its capabilities across three representative classes of non-Markovian systems: (1) reaction channels with delays, (2) stochastic processes driven by individual reactant properties, and (3) temporal networks governed by node activity. In all cases, REGIR accurately captures memory-dependent dynamics and outperforms existing approaches in terms of flexibility and efficiency.
The diagnostic utility of endocytoscopy for the detection of esophageal lesions: a systematic review and meta-analysis
Authors: Lu Wang, Bofu Tang, Feifei Liu, Zhenyu Jiang, Xianmei Meng
Date: 2022-12-11 00:00:00
Tasks: Diagnostic, Retrieval, Specificity

Objective: To systematically evaluate the value of endocytoscopy (ECS) in the diagnosis of early esophageal cancer (EC). Methods: Pubmed, Ovid and EMbase databases were searched to collect diagnostic tests of ECS assisted diagnosis of early EC. The retrieval time was from the establishment of the database to August 2022. Review manager 5.4, Stata 16.0 and Meta-Disc 1.4 were used for meta-analysis after two researchers independently screened literature, extracted data and evaluated the bias risk of included studies. Results: A total of 7 studies were included, including 520 lesions. Meta-analysis results showed that the combined sensitivity(SE), specificity(SP), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and positive posterior probability (PPP) of ECS screening for early EC were 0.95[95%CI: 0.84, 0.98], 0.92 [95%CI: 0.83, 0.96], 11.8 [95%CI: 5.3, 26.1], 0.06 [95%CI: 0.02, 0.18], 203 [95%CI: 50, 816], and 75%, respectively. The area (AUC) under the receiver Operating Characteristic curve (SROC) was 0.98[95%CI: 0.96, 0.99]. Conclusions: Current evidence suggests that ECS can be used as an effective screening tool for early EC. Due to the limited number and quality of included studies, it is imperative to conduct more high-quality studies to verify the above conclusions.
Saddle-Point Approach to Large-Time Volatility Smile
Authors: Chun Yat Yeung, Ali Hirsa
Date: 2022-12-12 00:00:00

We extend upon the saddle-point equation presented in [1] to derive large-time model-implied volatility smiles, providing its theoretical foundation and studying its applications in classical models. As long as characteristic function fulfills a L\'evy-type scaling behavior in large time, the approach allows us to study analytically the large-time smile behaviors under specific models, and moreover, to reach a very wide class of arbitrage-free model-inspired parametrizations, in the same manner as stochastic-volatility-inspired (SVI).
Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network
Authors: Shuhua Xiao, Jiali Ma, Li Xia, Shushang Zhu
Date: 2022-12-10 00:00:00
Tasks: Management

The bailout strategy is crucial to cushion the massive loss caused by systemic risk in the financial system. There is no closed-form formulation of the optimal bailout problem, making solving it difficult. In this paper, we regard the issue of the optimal bailout (capital injection) as a black-box optimization problem, where the black box is characterized as a fixed-point system that follows the E-N framework for measuring the systemic risk of the financial system. We propose the so-called ``Prediction-Gradient-Optimization'' (PGO) framework to solve it, where the ``Prediction'' means that the objective function without a closed-form is approximated and predicted by a neural network, the ``Gradient'' is calculated based on the former approximation, and the ``Optimization'' procedure is further implemented within a gradient projection algorithm to solve the problem. Comprehensive numerical simulations demonstrate that the proposed approach is promising for systemic risk management.
Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period
Authors: Weilin Fu, Zhuoran Li, Yupeng Zhang, Xingyou Zhou
Date: 2022-12-10 00:00:00
Tasks: Time Series, Time Series Analysis

Every financial crisis has caused a dual shock to the global economy. The shortage of market liquidity, such as default in debt and bonds, has led to the spread of bankruptcies, such as Lehman Brothers in 2008. Using the data for the ETFs of the S&P 500, Nasdaq 100, and Dow Jones Industrial Average collected from Yahoo Finance, this study implemented Deep Learning, Neuro Network, and Time-series to analyze the trend of the American Stock Market in the post-COVID-19 period. LSTM model in Neuro Network to predict the future trend, which suggests the US stock market keeps falling for the post-COVID-19 period. This study reveals a reasonable allocation method of Long Short-Term Memory for which there is strong evidence.
NETpred: Network-based modeling and prediction of multiple connected market indices
Authors: Alireza Jafari, Saman Haratizadeh
Date: 2022-12-02 00:00:00

Market prediction plays a major role in supporting financial decisions. An emerging approach in this domain is to use graphical modeling and analysis to for prediction of next market index fluctuations. One important question in this domain is how to construct an appropriate graphical model of the data that can be effectively used by a semi-supervised GNN to predict index fluctuations. In this paper, we introduce a framework called NETpred that generates a novel heterogeneous graph representing multiple related indices and their stocks by using several stock-stock and stock-index relation measures. It then thoroughly selects a diverse set of representative nodes that cover different parts of the state space and whose price movements are accurately predictable. By assigning initial predicted labels to such a set of nodes, NETpred makes sure that the subsequent GCN model can be successfully trained using a semi-supervised learning process. The resulting model is then used to predict the stock labels which are finally aggregated to infer the labels for all the index nodes in the graph. Our comprehensive set of experiments shows that NETpred improves the performance of the state-of-the-art baselines by 3%-5% in terms of F-score measure on different well-known data sets.
IMDB Spoiler Dataset
Authors: Rishabh Misra
Date: 2022-09-17 00:00:00

User-generated reviews are often our first point of contact when we consider watching a movie or a TV show. However, beyond telling us the qualitative aspects of the media we want to consume, reviews may inevitably contain undesired revelatory information (i.e. 'spoilers') such as the surprising fate of a character in a movie, or the identity of a murderer in a crime-suspense movie, etc. In this paper, we present a high-quality movie-review based spoiler dataset to tackle the problem of spoiler detection and describe various research questions it can answer.
News Headlines Dataset For Sarcasm Detection
Authors: Rishabh Misra
Date: 2022-09-17 00:00:00
Tasks: Articles, Sarcasm Detection

Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets, and detecting sarcasm in these requires the availability of contextual tweets. To overcome the limitations related to noise in Twitter datasets, we curate News Headlines Dataset from two news websites: TheOnion aims at producing sarcastic versions of current events, whereas HuffPost publishes real news. The dataset contains about 28K headlines out of which 13K are sarcastic. To make it more useful, we have included the source links of the news articles so that more data can be extracted as needed. In this paper, we describe various details about the dataset and potential use cases apart from Sarcasm Detection.
Chinese Discourse Annotation Reference Manual
Authors: Siyao Peng, Yang Janet Liu, Amir Zeldes
Date: 2022-10-11 00:00:00

This document provides extensive guidelines and examples for Rhetorical Structure Theory (RST) annotation in Mandarin Chinese. The guideline is divided into three sections. We first introduce preprocessing steps to prepare data for RST annotation. Secondly, we discuss syntactic criteria to segment texts into Elementary Discourse Units (EDUs). Lastly, we provide examples to define and distinguish discourse relations in different genres. We hope that this reference manual can facilitate RST annotations in Chinese and accelerate the development of the RST framework across languages.
Large Discourse Treebanks from Scalable Distant Supervision
Authors: Patrick Huber, Giuseppe Carenini
Date: 2022-10-18 00:00:00
Tasks: Discourse Parsing, Sentiment Analysis

Discourse parsing is an essential upstream task in Natural Language Processing with strong implications for many real-world applications. Despite its widely recognized role, most recent discourse parsers (and consequently downstream tasks) still rely on small-scale human-annotated discourse treebanks, trying to infer general-purpose discourse structures from very limited data in a few narrow domains. To overcome this dire situation and allow discourse parsers to be trained on larger, more diverse and domain-independent datasets, we propose a framework to generate "silver-standard" discourse trees from distant supervision on the auxiliary task of sentiment analysis.
Technological taxonomies for hypernym and hyponym retrieval in patent texts
Authors: You Zuo, Yixuan Li, Alma Parias García, Kim Gerdes
Date: 2022-11-14 00:00:00
Tasks: Patent classification, Retrieval

This paper presents an automatic approach to creating taxonomies of technical terms based on the Cooperative Patent Classification (CPC). The resulting taxonomy contains about 170k nodes in 9 separate technological branches and is freely available. We also show that a Text-to-Text Transfer Transformer (T5) model can be fine-tuned to generate hypernyms and hyponyms with relatively high precision, confirming the manually assessed quality of the resource. The T5 model opens the taxonomy to any new technological terms for which a hypernym can be generated, thus making the resource updateable with new terms, an essential feature for the constantly evolving field of technological terminology.
Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets
Authors: Ali Nazarizadeh, Touraj Banirostam, Minoo Sayyadpour
Date: 2022-11-09 00:00:00
Tasks: Articles, Sentiment Analysis

Sentiment analysis aims to extract people's emotions and opinion from their comments on the web. It widely used in businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Most of articles in this area have concentrated on the English language whereas there are limited resources for Persian language. In this review paper, recent published articles between 2018 and 2022 in sentiment analysis in Persian Language have been collected and their methods, approach and dataset will be explained and analyzed. Almost all the methods used to solve sentiment analysis are machine learning and deep learning. The purpose of this paper is to examine 40 different approach sentiment analysis in the Persian Language, analysis datasets along with the accuracy of the algorithms applied to them and also review strengths and weaknesses of each. Among all the methods, transformers such as BERT and RNN Neural Networks such as LSTM and Bi-LSTM have achieved higher accuracy in the sentiment analysis. In addition to the methods and approaches, the datasets reviewed are listed between 2018 and 2022 and information about each dataset and its details are provided.
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
Authors: Chengsheng Mao, Jie Xu, Luke Rasmussen, Yikuan Li, Prakash Adekkanattu, Jennifer Pacheco, Borna Bonakdarpour, Robert Vassar, Guoqian Jiang, Fei Wang, Jyotishman Pathak, Yuan Luo
Date: 2022-11-07 00:00:00

Objective: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from electronic health records (EHRs) to predict the risk of disease progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Materials and Methods: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000-2020. The progress notes no later than the first MCI diagnosis were used for the prediction. We first preprocessed the notes by deidentification, cleaning and splitting, and then pretrained a BERT model for AD (AD-BERT) based on the publicly available Bio+Clinical BERT on the preprocessed notes. The embeddings of all the sections of a patient's notes processed by AD-BERT were combined by MaxPooling to compute the probability of MCI-to-AD progression. For replication, we conducted a similar set of experiments on 2563 MCI patients identified at Weill Cornell Medicine (WCM) during the same timeframe. Results: Compared with the 7 baseline models, the AD-BERT model achieved the best performance on both datasets, with Area Under receiver operating characteristic Curve (AUC) of 0.8170 and F1 score of 0.4178 on NMEDW dataset and AUC of 0.8830 and F1 score of 0.6836 on WCM dataset. Conclusion: We developed a deep learning framework using BERT models which provide an effective solution for prediction of MCI-to-AD progression using clinical note analysis.
Machine Learning Approaches for Principle Prediction in Naturally Occurring Stories
Authors: Md Sultan Al Nahian, Spencer Frazier, Brent Harrison, Mark Riedl
Date: 2022-11-19 00:00:00

Value alignment is the task of creating autonomous systems whose values align with those of humans. Past work has shown that stories are a potentially rich source of information on human values; however, past work has been limited to considering values in a binary sense. In this work, we explore the use of machine learning models for the task of normative principle prediction on naturally occurring story data. To do this, we extend a dataset that has been previously used to train a binary normative classifier with annotations of moral principles. We then use this dataset to train a variety of machine learning models, evaluate these models and compare their results against humans who were asked to perform the same task. We show that while individual principles can be classified, the ambiguity of what "moral principles" represent, poses a challenge for both human participants and autonomous systems which are faced with the same task.