pwc-archive/papers-with-abstracts huggingface dataset:
A Pipeline for Integrated Theory and Data-Driven Modeling of Genomic and Clinical Data
Authors: Vineet K Raghu, Xiaoyu Ge, Arun Balajee, Daniel J. Shirer, Isha Das, Panayiotis V. Benos, Panos K. Chrysanthis
Date: 2020-05-05 00:00:00
Tasks: Decision Making, Variable Selection

High throughput genome sequencing technologies such as RNA-Seq and Microarray have the potential to transform clinical decision making and biomedical research by enabling high-throughput measurements of the genome at a granular level. However, to truly understand causes of disease and the effects of medical interventions, this data must be integrated with phenotypic, environmental, and behavioral data from individuals. Further, effective knowledge discovery methods that can infer relationships between these data types are required. In this work, we propose a pipeline for knowledge discovery from integrated genomic and clinical data. The pipeline begins with a novel variable selection method, and uses a probabilistic graphical model to understand the relationships between features in the data. We demonstrate how this pipeline can improve breast cancer outcome prediction models, and can provide a biologically interpretable view of sequencing data.
Phonetic and Visual Priors for Decipherment of Informal Romanization
Authors: Maria Ryskina, Matthew R. Gormley, Taylor Berg-Kirkpatrick
Date: 2020-05-05 00:00:00
Proceeding: ACL 2020 6
Tasks: Decipherment, Inductive Bias

Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards. Character substitution choices differ between users but have been shown to be governed by the same main principles observed across a variety of languages---namely, character pairs are often associated through phonetic or visual similarity. We propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion. We train our model directly on romanized data from two languages: Egyptian Arabic and Russian. We demonstrate that adding inductive bias through phonetic and visual priors on character mappings substantially improves the model's performance on both languages, yielding results much closer to the supervised skyline. Finally, we introduce a new dataset of romanized Russian, collected from a Russian social network website and partially annotated for our experiments.
A Metric Space for Point Process Excitations
Authors: Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan
Date: 2020-05-05 00:00:00
Tasks: Point Processes

A multivariate Hawkes process enables self- and cross-excitations through a triggering matrix that behaves like an asymmetrical covariance structure, characterizing pairwise interactions between the event types. Full-rank estimation of all interactions is often infeasible in empirical settings. Models that specialize on a spatiotemporal application alleviate this obstacle by exploiting spatial locality, allowing the dyadic relationships between events to depend only on separation in time and relative distances in real Euclidean space. Here we generalize this framework to any multivariate Hawkes process, and harness it as a vessel for embedding arbitrary event types in a hidden metric space. Specifically, we propose a Hidden Hawkes Geometry (HHG) model to uncover the hidden geometry between event excitations in a multivariate point process. The low dimensionality of the embedding regularizes the structure of the inferred interactions. We develop a number of estimators and validate the model by conducting several experiments. In particular, we investigate regional infectivity dynamics of COVID-19 in an early South Korean record and recent Los Angeles confirmed cases. By additionally performing synthetic experiments on short records as well as explorations into options markets and the Ebola epidemic, we demonstrate that learning the embedding alongside a point process uncovers salient interactions in a broad range of applications.
Quest: Practical and Oblivious Mitigation Strategies for COVID-19 using WiFi Datasets
Authors: Peeyush Gupta, Sharad Mehrotra, Nisha Panwar, Shantanu Sharma, Nalini Venkatasubramanian, Guoxi Wang
Date: 2020-05-05 00:00:00
Tasks: Privacy Preserving

Contact tracing has emerged as one of the main mitigation strategies to prevent the spread of pandemics such as COVID-19. Recently, several efforts have been initiated to track individuals, their movements, and interactions using technologies, e.g., Bluetooth beacons, cellular data records, and smartphone applications. Such solutions are often intrusive, potentially violating individual privacy rights and are often subject to regulations (e.g., GDPR and CCPR) that mandate the need for opt-in policies to gather and use personal information. In this paper, we introduce Quest, a system that empowers organizations to observe individuals and spaces to implement policies for social distancing and contact tracing using WiFi connectivity data in a passive and privacy-preserving manner. The goal is to ensure the safety of employees and occupants at an organization, while protecting the privacy of all parties. Quest incorporates computationally- and information-theoretically-secure protocols that prevent adversaries from gaining knowledge of an individual's location history (based on WiFi data); it includes support for accurately identifying users who were in the vicinity of a confirmed patient, and then informing them via opt-in mechanisms. Quest supports a range of privacy-enabled applications to ensure adherence to social distancing, monitor the flow of people through spaces, identify potentially impacted regions, and raise exposure alerts. We describe the architecture, design choices, and implementation of the proposed security/privacy techniques in Quest. We, also, validate the practicality of Quest and evaluate it thoroughly via an actual campus-scale deployment at UC Irvine over a very large dataset of over 50M tuples.
MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models
Authors: Mandy Guo, Yinfei Yang, Daniel Cer, Qinlan Shen, Noah Constant
Date: 2020-05-05 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval, Sentence

Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus (Ahmad et al.,2019).This paper presents MultiReQA, anew multi-domain ReQA evaluation suite com-posed of eight retrieval QA tasks drawn from publicly available QA datasets. We provide the first systematic retrieval based evaluation over these datasets using two supervised neural models, based on fine-tuning BERT andUSE-QA models respectively, as well as a surprisingly strong information retrieval baseline,BM25. Five of these tasks contain both train-ing and test data, while three contain test data only. Performance on the five tasks with train-ing data shows that while a general model covering all domains is achievable, the best performance is often obtained by training exclusively on in-domain data.
A generative adversarial network approach to calibration of local stochastic volatility models
Authors: Christa Cuchiero, Wahid Khosrawi, Josef Teichmann
Date: 2020-05-05 00:00:00
Tasks: Generative Adversarial Network

We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed-forward neural networks and learn their parameters directly from the available market option prices. This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying, possibly in an adversarial manner, distances to market prices. The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows the calculation of model prices and model implied volatilities in an accurate way using only small sets of sample paths. For numerical illustration we implement a SABR-type LSV model and conduct a thorough statistical performance analysis on many samples of implied volatility smiles, showing the accuracy and stability of the method.
Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning
Authors: Semih Yagli, Alex Dytso, H. Vincent Poor
Date: 2020-05-05 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning

Machine learning algorithms operating on mobile networks can be characterized into three different categories. First is the classical situation in which the end-user devices send their data to a central server where this data is used to train a model. Second is the distributed setting in which each device trains its own model and send its model parameters to a central server where these model parameters are aggregated to create one final model. Third is the federated learning setting in which, at any given time $t$, a certain number of active end users train with their own local data along with feedback provided by the central server and then send their newly estimated model parameters to the central server. The server, then, aggregates these new parameters, updates its own model, and feeds the updated parameters back to all the end users, continuing this process until it converges. The main objective of this work is to provide an information-theoretic framework for all of the aforementioned learning paradigms. Moreover, using the provided framework, we develop upper and lower bounds on the generalization error together with bounds on the privacy leakage in the classical, distributed and federated learning settings. Keywords: Federated Learning, Distributed Learning, Machine Learning, Model Aggregation.
Mimicry: Towards the Reproducibility of GAN Research
Authors: Kwot Sin Lee, Christopher Town
Date: 2020-05-05 00:00:00
Proceeding: AI for Content Creation Workshop at CVPR 2020 2020 5

Advancing the state of Generative Adversarial Networks (GANs) research requires one to make careful and accurate comparisons with existing works. Yet, this is often difficult to achieve in practice when models are often implemented differently using varying frameworks, and evaluated using different procedures even when the same metric is used. To mitigate these issues, we introduce Mimicry, a lightweight PyTorch library that provides implementations of popular state-of-the-art GANs and evaluation metrics to closely reproduce reported scores in the literature. We provide comprehensive baseline performances of different GANs on seven widely-used datasets by training these GANs under the same conditions, and evaluating them across three popular GAN metrics using the same procedures. The library can be found at https://github.com/kwotsin/mimicry.
A Framework for Designing and Evaluating Solar Flare Forecasting Systems
Authors: T. Cinto, A. L. S. Gradvohl, G. P. Coelho, A. E. A. da Silva
Date: 2020-05-05 00:00:00
Tasks: feature selection

Disturbances in space weather can negatively affect several fields, including aviation and aerospace, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares are the most significant events that can affect the Earth's atmosphere, thus leading researchers to drive efforts on their forecasting. The related literature is comprehensive and holds several systems proposed for flare forecasting. However, most techniques are tailor-made and designed for specific purposes, not allowing researchers to customize them in case of changes in data input or in the prediction algorithm. This paper proposes a framework to design, train, and evaluate flare prediction systems which present promising results. Our proposed framework involves model and feature selection, randomized hyper-parameters optimization, data resampling, and evaluation under operational settings. Compared to baseline predictions, our framework generated some proof-of-concept models with positive recalls between 0.70 and 0.75 for forecasting $\geq M$ class flares up to 96 hours ahead while keeping the area under the ROC curve score at high levels.
A Ladder of Causal Distances
Authors: Maxime Peyrard, Robert West
Date: 2020-05-05 00:00:00
Tasks: Benchmarking, Causal Discovery, counterfactual

Causal discovery, the task of automatically constructing a causal model from data, is of major significance across the sciences. Evaluating the performance of causal discovery algorithms should ideally involve comparing the inferred models to ground-truth models available for benchmark datasets, which in turn requires a notion of distance between causal models. While such distances have been proposed previously, they are limited by focusing on graphical properties of the causal models being compared. Here, we overcome this limitation by defining distances derived from the causal distributions induced by the models, rather than exclusively from their graphical structure. Pearl and Mackenzie (2018) have arranged the properties of causal models in a hierarchy called the "ladder of causation" spanning three rungs: observational, interventional, and counterfactual. Following this organization, we introduce a hierarchy of three distances, one for each rung of the ladder. Our definitions are intuitively appealing as well as efficient to compute approximately. We put our causal distances to use by benchmarking standard causal discovery systems on both synthetic and real-world datasets for which ground-truth causal models are available. Finally, we highlight the usefulness of our causal distances by briefly discussing further applications beyond the evaluation of causal discovery techniques.
Efficient strategies for hierarchical text classification: External knowledge and auxiliary tasks
Authors: Kervy Rivas Rojas, Gina Bustamante, Arturo Oncevay, Marco A. Sobrevilla Cabezudo
Date: 2020-05-05 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, text-classification, Text Classification

In hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy. Most of the studies have focused on developing novels neural network architectures to deal with the hierarchical structure, but we prefer to look for efficient ways to strengthen a baseline model. We first define the task as a sequence-to-sequence problem. Afterwards, we propose an auxiliary synthetic task of bottom-up-classification. Then, from external dictionaries, we retrieve textual definitions for the classes of all the hierarchy's layers, and map them into the word vector space. We use the class-definition embeddings as an additional input to condition the prediction of the next layer and in an adapted beam search. Whereas the modified search did not provide large gains, the combination of the auxiliary task and the additional input of class-definitions significantly enhance the classification accuracy. With our efficient approaches, we outperform previous studies, using a drastically reduced number of parameters, in two well-known English datasets.
Cross-media Structured Common Space for Multimedia Event Extraction
Authors: Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, Shih-Fu Chang
Date: 2020-05-05 00:00:00
Proceeding: ACL 2020 6
Tasks: Articles, Event Extraction

We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents. We develop the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments. We propose a novel method, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information from textual and visual data into a common embedding space. The structures are aligned across modalities by employing a weakly supervised training strategy, which enables exploiting available resources without explicit cross-media annotation. Compared to uni-modal state-of-the-art methods, our approach achieves 4.0% and 9.8% absolute F-score gains on text event argument role labeling and visual event extraction. Compared to state-of-the-art multimedia unstructured representations, we achieve 8.3% and 5.0% absolute F-score gains on multimedia event extraction and argument role labeling, respectively. By utilizing images, we extract 21.4% more event mentions than traditional text-only methods.
Russian Natural Language Generation: Creation of a Language Modelling Dataset and Evaluation with Modern Neural Architectures
Authors: Zein Shaheen, Gerhard Wohlgenannt, Bassel Zaity, Dmitry Mouromtsev, Vadim Pak
Date: 2020-05-05 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Text Generation

Generating coherent, grammatically correct, and meaningful text is very challenging, however, it is crucial to many modern NLP systems. So far, research has mostly focused on English language, for other languages both standardized datasets, as well as experiments with state-of-the-art models, are rare. In this work, we i) provide a novel reference dataset for Russian language modeling, ii) experiment with popular modern methods for text generation, namely variational autoencoders, and generative adversarial networks, which we trained on the new dataset. We evaluate the generated text regarding metrics such as perplexity, grammatical correctness and lexical diversity.
Spatio-Temporal Event Segmentation and Localization for Wildlife Extended Videos
Authors: Ramy Mounir, Roman Gula, Jörn Theuerkauf, Sudeep Sarkar
Date: 2020-05-05 00:00:00
Tasks: Action Detection, Activity Detection, Continual Learning, Event Segmentation, Segmentation

Using offline training schemes, researchers have tackled the event segmentation problem by providing full or weak-supervision through manually annotated labels or self-supervised epoch-based training. Most works consider videos that are at most 10's of minutes long. We present a self-supervised perceptual prediction framework capable of temporal event segmentation by building stable representations of objects over time and demonstrate it on long videos, spanning several days. The approach is deceptively simple but quite effective. We rely on predictions of high-level features computed by a standard deep learning backbone. For prediction, we use an LSTM, augmented with an attention mechanism, trained in a self-supervised manner using the prediction error. The self-learned attention maps effectively localize and track the event-related objects in each frame. The proposed approach does not require labels. It requires only a single pass through the video, with no separate training set. Given the lack of datasets of very long videos, we demonstrate our method on video from 10 days (254 hours) of continuous wildlife monitoring data that we had collected with required permissions. We find that the approach is robust to various environmental conditions such as day/night conditions, rain, sharp shadows, and windy conditions. For the task of temporally locating events, we had an 80% recall rate at 20% false-positive rate for frame-level segmentation. At the activity level, we had an 80% activity recall rate for one false activity detection every 50 minutes. We will make the dataset, which is the first of its kind, and the code available to the research community.
A new design of a flying robot, with advanced computer vision techniques to perform self-maintenance of smart grids
Authors: Abdullatif Baba
Date: 2020-05-05 00:00:00
Tasks: Region Proposal

In this paper, we present a full design of a flying robot to investigate the state of power grid components and to perform the appropriate maintenance procedures according to each fail or defect that could be recognized. To realize this purpose; different types of sensors including thermal and aerial vision-based systems are employed in this design. The main features and technical specifications of this robot are presented and discussed here in detail. Some essential and advanced computer vision techniques are exploited in this work to take some readings and measurements from the robot's surroundings. From each given image, many sub-images containing different electrical components are extracted using a new region proposal approach that relies on Discrete Wavelet Transform, to be classified later by utilizing a Convolutional Neural Network.
Iris segmentation techniques to recognize the behavior of a vigilant driver
Authors: Abdullatif Baba
Date: 2020-05-05 00:00:00
Tasks: Iris Segmentation, Position

In this paper, we clarify how to recognize different levels of vigilance for vehicle drivers. In order to avoid the classical problems of crisp logic, we preferred to employ a fuzzy logic-based system that depends on two variables to make the final decision. Two iris segmentation techniques are well illustrated. A new technique for pupil position detection is also provided here with the possibility to correct the pupil detected position when dealing with some noisy cases.
Contextualizing Hate Speech Classifiers with Post-hoc Explanation
Authors: Brendan Kennedy, Xisen Jin, Aida Mostafazadeh Davani, Morteza Dehghani, Xiang Ren
Date: 2020-05-05 00:00:00
Proceeding: ACL 2020 6

Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like "gay" or "black" are used in offensive or prejudiced ways. Such biases manifest in false positives when these identifiers are present, due to models' inability to learn the contexts which constitute a hateful usage of identifiers. We extract SOC post-hoc explanations from fine-tuned BERT classifiers to efficiently detect bias towards identity terms. Then, we propose a novel regularization technique based on these explanations that encourages models to learn from the context of group identifiers in addition to the identifiers themselves. Our approach improved over baselines in limiting false positives on out-of-domain data while maintaining or improving in-domain performance. Project page: https://inklab.usc.edu/contextualize-hate-speech/.
Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery
Authors: Hiroshi Sasaki, Chris G. Willcocks, Toby P. Breckon
Date: 2020-05-05 00:00:00
Tasks: Classification, Data Augmentation, domain classification, General Classification, object-detection, Object Detection

Machine learning driven object detection and classification within non-visible imagery has an important role in many fields such as night vision, all-weather surveillance and aviation security. However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery, in contrast to the high data availability of visible-band imagery that readily enables contemporary deep learning driven detection and classification approaches. To address this problem, this paper proposes and evaluates a novel data augmentation approach that leverages the more readily available visible-band imagery via a generative domain transfer model. The model can synthesise large volumes of non-visible domain imagery by image-to-image (I2I) translation from the visible image domain. Furthermore, we show that the generation of interpolated mixed class (non-visible domain) image examples via our novel Conditional CycleGAN Mixup Augmentation (C2GMA) methodology can lead to a significant improvement in the quality of non-visible domain classification tasks that otherwise suffer due to limited data availability. Focusing on classification within the Synthetic Aperture Radar (SAR) domain, our approach is evaluated on a variation of the Statoil/C-CORE Iceberg Classifier Challenge dataset and achieves 75.4% accuracy, demonstrating a significant improvement when compared against traditional data augmentation strategies (Rotation, Mixup, and MixCycleGAN).