pwc-archive/papers-with-abstracts huggingface dataset:
Fine-grained prediction of food insecurity using news streams
Authors: Ananth Balashankar, Lakshminarayanan Subramanian, Samuel P. Fraiberger
Date: 2021-11-17 00:00:00
Tasks: Articles, Decision Making, Humanitarian, Prediction

Anticipating the outbreak of a food crisis is crucial to efficiently allocate emergency relief and reduce human suffering. However, existing food insecurity early warning systems rely on risk measures that are often delayed, outdated, or incomplete. Here, we leverage recent advances in deep learning to extract high-frequency precursors to food crises from the text of a large corpus of news articles about fragile states published between 1980 and 2020. Our text features are causally grounded, interpretable, validated by existing data, and allow us to predict 32% more food crises than existing models up to three months ahead of time at the district level across 15 fragile states. These results could have profound implications on how humanitarian aid gets allocated and open new avenues for machine learning to improve decision making in data-scarce environments.
Synthetic weather radar using hybrid quantum-classical machine learning
Authors: Graham R. Enos, Matthew J. Reagor, Maxwell P. Henderson, Christina Young, Kyle Horton, Mandy Birch, Chad Rigetti
Date: 2021-11-30 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, Decision Making

The availability of high-resolution weather radar images underpins effective forecasting and decision-making. In regions beyond traditional radar coverage, generative models have emerged as an important synthetic capability, fusing more ubiquitous data sources, such as satellite imagery and numerical weather models, into accurate radar-like products. Here, we demonstrate methods to augment conventional convolutional neural networks with quantum-assisted models for generative tasks in global synthetic weather radar. We show that quantum kernels can, in principle, perform fundamentally more complex tasks than classical learning machines on the relevant underlying data. Our results establish synthetic weather radar as an effective heuristic benchmark for quantum computing capabilities and set the stage for detailed quantum advantage benchmarking on a high-impact operationally relevant problem.
Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks
Authors: Aurélio Ribeiro Costa
Date: 2021-11-25 00:00:00
Tasks: Community Detection, reinforcement-learning, Reinforcement Learning (RL)

The identification of community structure in a social network is an important problem tackled in the literature of network analysis. There are many solutions to this problem using a static scenario, when facing a dynamic scenario some solutions may be adapted but others simply do not fit, moreover when considering the demand to analyze constantly growing networks. In this context, we propose an approach to the problem of community detection in dynamic networks based on a reinforcement learning strategy to deal with changes on big networks using a local optimization on the modularity score of the changed entities. An experiment using synthetic and real-world dynamic network data shows results comparable to static scenarios.
DiPD: Disruptive event Prediction Dataset from Twitter
Authors: Sanskar Soni, Dev Mehta, Vinush Vishwanath, Aditi Seetha, Satyendra Singh Chouhan
Date: 2021-11-25 00:00:00
Tasks: Prediction

Riots and protests, if gone out of control, can cause havoc in a country. We have seen examples of this, such as the BLM movement, climate strikes, CAA Movement, and many more, which caused disruption to a large extent. Our motive behind creating this dataset was to use it to develop machine learning systems that can give its users insight into the trending events going on and alert them about the events that could lead to disruption in the nation. If any event starts going out of control, it can be handled and mitigated by monitoring it before the matter escalates. This dataset collects tweets of past or ongoing events known to have caused disruption and labels these tweets as 1. We also collect tweets that are considered non-eventful and label them as 0 so that they can also be used to train a classification system. The dataset contains 94855 records of unique events and 168706 records of unique non-events, thus giving the total dataset 263561 records. We extract multiple features from the tweets, such as the user's follower count and the user's location, to understand the impact and reach of the tweets. This dataset might be useful in various event related machine learning problems such as event classification, event recognition, and so on.
Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational $N$-body Problem
Authors: Maxwell X. Cai, Simon Portegies Zwart, Damian Podareanu
Date: 2021-11-28 00:00:00
Tasks: Inductive Bias

The gravitational $N$-body problem, which is fundamentally important in astrophysics to predict the motion of $N$ celestial bodies under the mutual gravity of each other, is usually solved numerically because there is no known general analytical solution for $N>2$. Can an $N$-body problem be solved accurately by a neural network (NN)? Can a NN observe long-term conservation of energy and orbital angular momentum? Inspired by Wistom & Holman (1991)'s symplectic map, we present a neural $N$-body integrator for splitting the Hamiltonian into a two-body part, solvable analytically, and an interaction part that we approximate with a NN. Our neural symplectic $N$-body code integrates a general three-body system for $10^{5}$ steps without diverting from the ground truth dynamics obtained from a traditional $N$-body integrator. Moreover, it exhibits good inductive bias by successfully predicting the evolution of $N$-body systems that are no part of the training set.
Improving random walk rankings with feature selection and imputation
Authors: Ngoc Mai Tran, Yangxinyu Xie
Date: 2021-11-29 00:00:00
Tasks: feature selection, Imputation

The Science4cast Competition consists of predicting new links in a semantic network, with each node representing a concept and each edge representing a link proposed by a paper relating two concepts. This network contains information from 1994-2017, with a discretization of days (which represents the publication date of the underlying papers). Team Hash Brown's final submission, \emph{ee5a}, achieved a score of 0.92738 on the test set. Our team's score ranks \emph{second place}, 0.01 below the winner's score. This paper details our model, its intuition, and the performance of its variations in the test set.
Radio-Frequency Multi-Mode OAM Detection Based on UCA Samples Learning
Authors: Jiabei Fan, Rui Chen, Wen-Xuan Long, Marco Moretti, Jiandong Li
Date: 2021-11-29 00:00:00

Orbital angular momentum (OAM) at radio-frequency provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies. However, classical phase gradient-based OAM mode detection methods require perfect alignment of transmit and receive antennas, which greatly challenges the practical application of OAM communications. In this paper, we first show the effect of non-parallel misalignment on the OAM phase structure, and then propose the OAM mode detection method based on uniform circular array (UCA) samples learning for the more general alignment or non-parallel misalignment case. Specifically, we applied three classifiers: K-nearest neighbor (KNN), support vector machine (SVM), and back-propagation neural network (BPNN) to both single-mode and multi-mode OAM detection. The simulation results validate that the proposed learning-based OAM mode detection methods are robust to misalignment errors and especially BPNN classifier has the best generalization performance.
Survey Descent: A Multipoint Generalization of Gradient Descent for Nonsmooth Optimization
Authors: X. Y. Han, Adrian S. Lewis
Date: 2021-11-30 00:00:00

For strongly convex objectives that are smooth, the classical theory of gradient descent ensures linear convergence relative to the number of gradient evaluations. An analogous nonsmooth theory is challenging. Even when the objective is smooth at every iterate, the corresponding local models are unstable and the number of cutting planes invoked by traditional remedies is difficult to bound, leading to convergences guarantees that are sublinear relative to the cumulative number of gradient evaluations. We instead propose a multipoint generalization of the gradient descent iteration for local optimization. While designed with general objectives in mind, we are motivated by a ``max-of-smooth'' model that captures the subdifferential dimension at optimality. We prove linear convergence when the objective is itself max-of-smooth, and experiments suggest a more general phenomenon.
Studying Hadronization by Machine Learning Techniques
Authors: Gábor Bíró, Bence Tankó-Bartalis, Gergely Gábor Barnaföldi
Date: 2021-11-30 00:00:00
Tasks: BIG-bench Machine Learning

Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Computer Vision and Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and non-perturbative features of the physical processes. In this study, results of two ResNet networks are presented by investigating global and kinematical quantities, indeed jet- and event-shape variables. The widely used Lund string fragmentation model is applied as a baseline in $\sqrt{s}= 7$ TeV proton-proton collisions to predict the most relevant observables at further LHC energies.
Georacle: Enabling Geospatially Aware Smart Contracts
Authors: Taha Azzaoui
Date: 2021-11-29 00:00:00

Smart contracts have enabled a paradigm shift in computing by leveraging decentralized networks of trust to achieve consensus at scale. Oracle networks further extend the power of smart contracts by solving the so-called "oracle problem". Such networks enable smart contracts to make use of the vast amount pre-existing data available on the web today without jeopardizing the integrity of the underlying network of trust. By leveraging oracle networks, smart contracts can make decisions based on data corresponding to the physical world. To this end, we introduce Georacle - an oracle service that enables geospatially aware smart contracts in a way that respects the space constrained nature of blockchain environments. Contracts can query the location of objects in a given area, map between street addresses and coordinates, and retrieve the geometry of a desired region of space while conserving gas consumption and avoiding unnecessary data processing.
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
Authors: Wei Guo, Can Zhang, ZhiCheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Ruiming Tang, Xiuqiang He, Rui Zhang
Date: 2021-11-30 00:00:00
Tasks: Click-Through Rate Prediction, Contrastive Learning, Recommendation Systems, Representation Learning, Self-Supervised Learning

CTR prediction is essential for modern recommender systems. Ranging from early factorization machines to deep learning based models in recent years, existing CTR methods focus on capturing useful feature interactions or mining important behavior patterns. Despite the effectiveness, we argue that these methods suffer from the risk of label sparsity (i.e., the user-item interactions are highly sparse with respect to the feature space), label noise (i.e., the collected user-item interactions are usually noisy), and the underuse of domain knowledge (i.e., the pairwise correlations between samples). To address these challenging problems, we propose a novel Multi-Interest Self-Supervised learning (MISS) framework which enhances the feature embeddings with interest-level self-supervision signals. With the help of two novel CNN-based multi-interest extractors,self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item). Based on that, contrastive learning losses are further applied to the augmented views of interest representations, which effectively improves the feature representation learning. Furthermore, our proposed MISS framework can be used as an plug-in component with existing CTR prediction models and further boost their performances. Extensive experiments on three large-scale datasets show that MISS significantly outperforms the state-of-the-art models, by up to 13.55% in AUC, and also enjoys good compatibility with representative deep CTR models.
A Fast Non-parametric Approach for Local Causal Structure Learning
Authors: Mona Azadkia, Armeen Taeb, Peter Bühlmann
Date: 2021-11-29 00:00:00
Tasks: Variable Selection

We study the problem of causal structure learning with essentially no assumptions on the functional relationships and noise. We develop DAG-FOCI, a computationally fast algorithm for this setting that is based on the FOCI variable selection algorithm in~\cite{azadkia2021simple}. DAG-FOCI outputs the set of parents of a response variable of interest. We provide theoretical guarantees of our procedure when the underlying graph does not contain any (undirected) cycle containing the response variable of interest. Furthermore, in the absence of this assumption, we give a conservative guarantee against false positive causal claims when the set of parents is identifiable. We demonstrate the applicability of DAG-FOCI on simulated as well as a real dataset from computational biology~\cite{sachs2005causal}.
On the challenges of using D-Wave computers to sample Boltzmann Random Variables
Authors: Thomas Pochart, Paulin Jacquot, Joseph Mikael
Date: 2021-11-30 00:00:00

Sampling random variables following a Boltzmann distribution is an NP-hard problem involved in various applications such as training of \textit{Boltzmann machines}, a specific kind of neural network. Several attempts have been made to use a D-Wave quantum computer to sample such a distribution, as this could lead to significant speedup in these applications. Yet, at present, several challenges remain to efficiently perform such sampling. We detail the various obstacles and explain the remaining difficulties in solving the sampling problem on a D-wave machine.
Modelling hetegeneous treatment effects by quantitle local polynomial decision tree and forest
Authors: Lai Xinglin
Date: 2021-11-30 00:00:00

To further develop the statistical inference problem for heterogeneous treatment effects, this paper builds on Breiman's (2001) random forest tree (RFT)and Wager et al.'s (2018) causal tree to parameterize the nonparametric problem using the excellent statistical properties of classical OLS and the division of local linear intervals based on covariate quantile points, while preserving the random forest trees with the advantages of constructible confidence intervals and asymptotic normality properties [Athey and Imbens (2016),Efron (2014),Wager et al.(2014)\citep{wager2014asymptotic}], we propose a decision tree using quantile classification according to fixed rules combined with polynomial estimation of local samples, which we call the quantile local linear causal tree (QLPRT) and forest (QLPRF).
Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach
Authors: Mithun Das, Somnath Banerjee, Punyajoy Saha
Date: 2021-11-27 00:00:00
Tasks: Abusive Language

Online hatred is a growing concern on many social media platforms. To address this issue, different social media platforms have introduced moderation policies for such content. They also employ moderators who can check the posts violating moderation policies and take appropriate action. Academicians in the abusive language research domain also perform various studies to detect such content better. Although there is extensive research in abusive language detection in English, there is a lacuna in abusive language detection in low resource languages like Hindi, Urdu etc. In this FIRE 2021 shared task - "HASOC- Abusive and Threatening language detection in Urdu" the organizers propose an abusive language detection dataset in Urdu along with threatening language detection. In this paper, we explored several machine learning models such as XGboost, LGBM, m-BERT based models for abusive and threatening content detection in Urdu based on the shared task. We observed the Transformer model specifically trained on abusive language dataset in Arabic helps in getting the best performance. Our model came First for both abusive and threatening content detection with an F1scoreof 0.88 and 0.54, respectively.
Trend and Thoughts: Understanding Climate Change Concern using Machine Learning and Social Media Data
Authors: Zhongkai Shangguan, Zihe Zheng, Lei Lin
Date: 2021-11-06 00:00:00

Nowadays social media platforms such as Twitter provide a great opportunity to understand public opinion of climate change compared to traditional survey methods. In this paper, we constructed a massive climate change Twitter dataset and conducted comprehensive analysis using machine learning. By conducting topic modeling and natural language processing, we show the relationship between the number of tweets about climate change and major climate events; the common topics people discuss climate change; and the trend of sentiment. Our dataset was published on Kaggle (\url{https://www.kaggle.com/leonshangguan/climate-change-tweets-ids-until-aug-2021}) and can be used in further research.
Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis
Authors: Ron Hochstenbach, Flavius Frasincar, Maria Mihaela Trusca
Date: 2021-11-29 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

The increasing popularity of the Web has subsequently increased the abundance of reviews on products and services. Mining these reviews for expressed sentiment is beneficial for both companies and consumers, as quality can be improved based on this information. In this paper, we consider the state-of-the-art HAABSA++ algorithm for aspect-based sentiment analysis tasked with identifying the sentiment expressed towards a given aspect in review sentences. Specifically, we train the neural network part of this algorithm using an adversarial network, a novel machine learning training method where a generator network tries to fool the classifier network by generating highly realistic new samples, as such increasing robustness. This method, as of yet never in its classical form applied to aspect-based sentiment analysis, is found to be able to considerably improve the out-of-sample accuracy of HAABSA++: for the SemEval 2015 dataset, accuracy was increased from 81.7% to 82.5%, and for the SemEval 2016 task, accuracy increased from 84.4% to 87.3%.
Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization
Authors: Brian Yan, Chunlei Zhang, Meng Yu, Shi-Xiong Zhang, Siddharth Dalmia, Dan Berrebbi, Chao Weng, Shinji Watanabe, Dong Yu
Date: 2021-11-29 00:00:00
Tasks: speech-recognition, Speech Recognition

Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type. In this work, we propose a general framework to jointly model the likelihoods of the monolingual and code-switch sub-tasks that comprise bilingual speech recognition. By defining the monolingual sub-tasks with label-to-frame synchronization, our joint modeling framework can be conditionally factorized such that the final bilingual output, which may or may not be code-switched, is obtained given only monolingual information. We show that this conditionally factorized joint framework can be modeled by an end-to-end differentiable neural network. We demonstrate the efficacy of our proposed model on bilingual Mandarin-English speech recognition across both monolingual and code-switched corpora.
Learning to Predict Persona Information forDialogue Personalization without Explicit Persona Description
Authors: Wangchunshu Zhou, Qifei Li, Chenle Li
Date: 2021-11-30 00:00:00

Personalizing dialogue agents is important for dialogue systems to generate more specific, consistent, and engaging responses. However, most current dialogue personalization approaches rely on explicit persona descriptions during inference, which severely restricts its application. In this paper, we propose a novel approach that learns to predict persona information based on the dialogue history to personalize the dialogue agent without relying on any explicit persona descriptions during inference. Experimental results on the PersonaChat dataset show that the proposed method can improve the consistency of generated responses when conditioning on the predicted profile of the dialogue agent (i.e. "self persona"), and improve the engagingness of the generated responses when conditioning on the predicted persona of the dialogue partner (i.e. "their persona"). We also find that a trained persona prediction model can be successfully transferred to other datasets and help generate more relevant responses.
Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency
Authors: Pakhi Bamdev, Manraj Singh Grover, Yaman Kumar Singla, Payman Vafaee, Mika Hama, Rajiv Ratn Shah
Date: 2021-11-30 00:00:00
Tasks: Feature Importance, regression

English proficiency assessments have become a necessary metric for filtering and selecting prospective candidates for both academia and industry. With the rise in demand for such assessments, it has become increasingly necessary to have the automated human-interpretable results to prevent inconsistencies and ensure meaningful feedback to the second language learners. Feature-based classical approaches have been more interpretable in understanding what the scoring model learns. Therefore, in this work, we utilize classical machine learning models to formulate a speech scoring task as both a classification and a regression problem, followed by a thorough study to interpret and study the relation between the linguistic cues and the English proficiency level of the speaker. First, we extract linguist features under five categories (fluency, pronunciation, content, grammar and vocabulary, and acoustic) and train models to grade responses. In comparison, we find that the regression-based models perform equivalent to or better than the classification approach. Second, we perform ablation studies to understand the impact of each of the feature and feature categories on the performance of proficiency grading. Further, to understand individual feature contributions, we present the importance of top features on the best performing algorithm for the grading task. Third, we make use of Partial Dependence Plots and Shapley values to explore feature importance and conclude that the best performing trained model learns the underlying rubrics used for grading the dataset used in this study.