pwc-archive/papers-with-abstracts huggingface dataset:
The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships
Authors: Olivier Mailhot, Francois Major, Rafael Najmanovich
Date: 2022-07-07 00:00:00

Summary: The DynaSig-ML (Dynamical Signatures - Machine Learning) Python package allows the efficient, user-friendly exploration of 3D dynamics-function relationships in biomolecules, using datasets of experimental measures from large numbers of sequence variants. The DynaSig-ML package is built around the Elastic Network Contact Model (ENCoM), the first and only sequence-sensitive coarse-grained NMA model, which is used to generate the input Dynamical Signatures. Starting from in silico mutated structures, the whole pipeline can be run with just a few lines of Python and modest computational resources. The compute-intensive steps can also easily be parallelized in the case of either large biomolecules or vast amounts of sequence variants. As an example application, we use the DynaSig-ML package to predict the evolutionary fitness of the bacterial enzyme VIM-2 lactamase from deep mutational scan data. Availability and implementation: DynaSig-ML is open source software available at https: //github.com/gregorpatof/dynasigml package Contact: rafael.najmanovich@umontreal.ca
Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution
Authors: Adrian Jones, Steven E. Massey, Daoyu Zhang, Yuri Deigin, Steven C. Quay
Date: 2022-07-07 00:00:00

The only animals other than bats reported to have been infected with SARS-CoV-2-related coronaviruses (SARS2r-CoVs) prior to the COVID-19 pandemic are pangolins. In early 2020 multiple papers reported the identification of two clades of SARS2r-CoVs, GD and GX, infecting pangolins. However the RNA-Seq datasets supporting pangolin genome assembly were widely contaminated, contained synthetic vectors or were heavily enriched or filtered with little but coronavirus sequences left in the datasets. Here we investigate two pangolin fecal samples sequenced by Li et al. (2021) provided in support of GD PCoV infection of pangolins in Guangdong and find the read distribution consistent with PCR amplicon contamination and SARS-CoV-2 contamination, and further identify the presence of synthetic plasmid sequences. We also build upon our previous work to further analyze the dataset GX/P3B by Lam et al. (2020), which is the only non enriched/heavily filtered pangolin tissue dataset sequenced by Lam et al. (2020). We identify synthetic vectors and confirm human genomic origin samples in the dataset. Finally, we find human mitochondrial sequences in all pangolin organ datasets and mouse and tiger mitochondrial sequences in selected pangolin organ datasets sequenced by Liu et al. (2019). We infer that human and mouse genomic origin sequences were probably sourced from contamination prior to sequencing, while tiger origin sequence contamination may have occurred due to index hopping during sequencing. These observations are problematic for attributing pangolins as SARS2r-CoV hosts in the datasets examined. The forensic methods developed and used here can be applied to examine any third party SRA data sets.
Mortgage-Rate-Adjusted Home Prices
Authors: Honggao Cao
Date: 2022-07-06 00:00:00

In this paper, we investigate the impact of mortgage rates on home prices, and how the impact may be used to help property purchase discussions at individual buyer level and to adjust home price indices across time. A mortgage-rate-adjusted "effective price" is derived to measure near term property price in the presence of (expected) mortgage rate changes. A price-mortgage rate neutrality line is then constructed based on the "effective price" to help differentiate various market scenarios in the near term, which can be used by prospective buyers in their "to-buy or not-to-buy" deliberations. At the market level, effective home prices allow for neutralization of mortgage rates on the movement of the housing market. An application of the neutralization strategy to the Case-Shiller Home Price Index (HPI) indicates that the U.S. housing market has been considerably affected by the dynamics of mortgage rates in a long run. But mortgage rates do no appear a primary driver of the extraordinary home price increase during the COVID-19 pandemic.
Cost-efficient Payoffs under Model Ambiguity
Authors: Carole Bernard, Gero Junike, Thibaut Lux, Steven Vanduffel
Date: 2022-07-06 00:00:00
Tasks: model

Dybvig (1988a,b) solves in a complete market setting the problem of finding a payoff that is cheapest possible in reaching a given target distribution ("cost-efficient payoff"). In the presence of ambiguity, the distribution of a payoff is, however, no longer known with certainty. We study the problem of finding the cheapest possible payoff whose worst-case distribution stochastically dominates a given target distribution ("robust cost-efficient payoff") and determine solutions under certain conditions. We study the link between "robust cost-efficiency" and the maxmin expected utility setting of Gilboa and Schmeidler, as well as more generally with robust preferences in a possibly non-expected utility setting. Specifically, we show that solutions to maxmin robust expected utility are necessarily robust cost-efficient. We illustrate our study with examples involving uncertainty both on the drift and on the volatility of the risky asset.
An optimal investment strategy aimed at maximizing the expected utility across all intermediate capital levels
Authors: J. Cerda-Hernandez, A. Sikov, A. Ramos
Date: 2022-07-06 00:00:00
Tasks: All

This study investigates an optimal investment problem for an insurance company operating under the Cramer-Lundberg risk model, where investments are made in both a risky asset and a risk-free asset. In contrast to other literature that focuses on optimal investment and/or reinsurance strategies to maximize the expected utility of terminal wealth within a given time horizon, this work considers the expected value of utility accumulation across all intermediate capital levels of the insurer. By employing the Dynamic Programming Principle, we prove a verification theorem, in order to show that any solution to the Hamilton-Jacobi-Bellman (HJB) equation solves our optimization problem. Subject to some regularity conditions on the solution of the HJB equation, we establish the existence of the optimal investment strategy. Finally, to illustrate the applicability of the theoretical findings, we present numerical examples.
Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes
Authors: Mnacho Echenim, Emmanuel Gobet, Anne-Claire Maurice
Date: 2022-07-06 00:00:00

We design a novel calibration procedure that is designed to handle the specific characteristics of options on cryptocurrency markets, namely large bid-ask spreads and the possibility of missing or incoherent prices in the considered data sets. We show that this calibration procedure is significantly more robust and accurate than the standard one based on trade and mid-prices.
Efficient fine-grained road segmentation using superpixel-based CNN and CRF models
Authors: Farnoush Zohourian, Jan Siegemund, Mirko Meuter, Josef Pauli
Date: 2022-06-22 00:00:00
Tasks: Computational Efficiency, Road Segmentation, Scene Segmentation, Segmentation, Semantic Segmentation

Towards a safe and comfortable driving, road scene segmentation is a rudimentary problem in camera-based advance driver assistance systems (ADAS). Despite of the great achievement of Convolutional Neural Networks (CNN) for semantic segmentation task, the high computational efforts of CNN based methods is still a challenging area. In recent work, we proposed a novel approach to utilise the advantages of CNNs for the task of road segmentation at reasonable computational effort. The runtime benefits from using irregular super pixels as basis for the input for the CNN rather than the image grid, which tremendously reduces the input size. Although, this method achieved remarkable low computational time in both training and testing phases, the lower resolution of the super pixel domain yields naturally lower accuracy compared to high cost state of the art methods. In this work, we focus on a refinement of the road segmentation utilising a Conditional Random Field (CRF).The refinement procedure is limited to the super pixels touching the predicted road boundary to keep the additional computational effort low. Reducing the input to the super pixel domain allows the CNNs structure to stay small and efficient to compute while keeping the advantage of convolutional layers and makes them eligible for ADAS. Applying CRF compensate the trade off between accuracy and computational efficiency. The proposed system obtained comparable performance among the top performing algorithms on the KITTI road benchmark and its fast inference makes it particularly suitable for realtime applications.
Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models
Authors: Xuhong LI, Haoyi Xiong, Yi Liu, Dingfu Zhou, Zeyu Chen, Yaqing Wang, Dejing Dou
Date: 2022-07-04 00:00:00
Tasks: Classification, image-classification, Image Classification, Image Segmentation, Segmentation, Semantic Segmentation

While fine-tuning pre-trained networks has become a popular way to train image segmentation models, such backbone networks for image segmentation are frequently pre-trained using image classification source datasets, e.g., ImageNet. Though image classification datasets could provide the backbone networks with rich visual features and discriminative ability, they are incapable of fully pre-training the target model (i.e., backbone+segmentation modules) in an end-to-end manner. The segmentation modules are left to random initialization in the fine-tuning process due to the lack of segmentation labels in classification datasets. In our work, we propose a method that leverages Pseudo Semantic Segmentation Labels (PSSL), to enable the end-to-end pre-training for image segmentation models based on classification datasets. PSSL was inspired by the observation that the explanation results of classification models, obtained through explanation algorithms such as CAM, SmoothGrad and LIME, would be close to the pixel clusters of visual objects. Specifically, PSSL is obtained for each image by interpreting the classification results and aggregating an ensemble of explanations queried from multiple classifiers to lower the bias caused by single models. With PSSL for every image of ImageNet, the proposed method leverages a weighted segmentation learning procedure to pre-train the segmentation network en masse. Experiment results show that, with ImageNet accompanied by PSSL as the source dataset, the proposed end-to-end pre-training strategy successfully boosts the performance of various segmentation models, i.e., PSPNet-ResNet50, DeepLabV3-ResNet50, and OCRNet-HRNetW18, on a number of segmentation tasks, such as CamVid, VOC-A, VOC-C, ADE20K, and CityScapes, with significant improvements. The source code is availabel at https://github.com/PaddlePaddle/PaddleSeg.
Application of Complex Split-Activation Feedforward Networks to Beamforming
Authors: Swaroop Appadwedula
Date: 2021-10-31 00:00:00
Proceeding: Asilomar Conference on Signals, Systems & Computers 2021 10

In increasingly congested RF environments and for jamming at closer ranges, amplifiers may introduce nonlinearities that linear adaptive beamforming techniques can’t mitigate. Machine learning architectures are intended to solve such nonlinear least squares problems, but much of the current work and available software is limited to signals represented as real sequences. In this paper, neural networks using complex numbers to represent the complex baseband RF signals are considered. A complex backpropagation approach that calculates gradients and a Jacobian, allows for fast optimization of the neural networks. Through simulations, it is shown that complex neural networks require less training samples than their real counterparts and may generalize better in dynamic environments.
Experiencing the communication advantage of the Indefinite Causal Orders
Authors: Daniele Cuomo; Marcello Caleffi; Angela Sara Cacciapuoti
Date: 2021-11-19 00:00:00
Proceeding: IEEE SPAWC 2021 11
Tasks: Noise Estimation

Many recent studies deal with the Superposition of Causal Orders, a quantum operation with promising advantages in both communication or computing. To experience the advantages, there are several way of implementing it. In literature, most of the set-ups are photonic-based. Instead, our interest is witnessing the Superpositon of Causal Orders within a programmable technology, based on superconductors. To do that, we focus on a specific case of the subject operation, which could be useful for the future of quantum communication.
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation.
Authors: Muhammad Umair Nasir, Innocent Mchechesi
Date: 2022-07-01 00:00:00
Proceeding: NAACL (MIA) 2022 7
Tasks: Machine Translation, Transfer Learning, Translation

Stemming from the limited availability of datasets and textual resources for low-resource languages such as isiZulu, there is a significant need to be able to harness knowledge from pre-trained models to improve low resource machine translation. Moreover, a lack of techniques to handle the complexities of morphologically rich languages has compounded the unequal development of translation models, with many widely spoken African languages being left behind. This study explores the potential benefits of transfer learning in an English-isiZulu translation framework. The results indicate the value of transfer learning from closely related languages to enhance the performance of low-resource translation models, thus providing a key strategy for low-resource translation going forward. We gathered results from 8 different language corpora, including one multi-lingual corpus, and saw that isiXhosa-isiZulu outperformed all languages, with a BLEU score of 8.56 on the test set which was better from the multi-lingual corpora pre-trained model by 2.73. We also derived a new coefficient, Nasir’s Geographical Distance Coefficient (NGDC) which provides an easy selection of languages for the pre-trained models. NGDC also indicated that isiXhosa should be selected as the language for the pre-trained model.
An Annotated Dataset and Automatic Approaches for Discourse Mode Identification in Low-resource Bengali Language
Authors: Salim Sazzed
Date: 2022-07-01 00:00:00
Proceeding: NAACL (MIA) 2022 7
Tasks: Descriptive, Language Modeling, Language Modelling, Sentence

The modes of discourse aid in comprehending the convention and purpose of various forms of languages used during communication. In this study, we introduce a discourse mode annotated corpus for the low-resource Bangla (also referred to as Bengali) language. The corpus consists of sentence-level annotation of three different discourse modes, narrative, descriptive, and informative of the text excerpted from a number of Bangla novels. We analyze the annotated corpus to expose various linguistic aspects of discourse modes, such as class distributions and average sentence lengths. To automatically determine the mode of discourse, we apply CML (classical machine learning) classifiers with n-gram based statistical features and a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) based language model. We observe that fine-tuned BERT-based approach yields more promising results than n-gram based CML classifiers. Our created discourse mode annotated dataset, the first of its kind in Bangla, and the evaluation, provide baselines for the automatic discourse mode identification in Bangla and can assist various downstream natural language processing tasks.
Complex Word Identification in Vietnamese: Towards Vietnamese Text Simplification
Authors: Phuong Nguyen, David Kauchak
Date: 2022-07-01 00:00:00
Proceeding: NAACL (MIA) 2022 7
Tasks: Complex Word Identification, Lexical Simplification, Text Simplification, Vietnamese Datasets

Text Simplification has been an extensively researched problem in English, but has not been investigated in Vietnamese. We focus on the Vietnamese-specific Complex Word Identification task, often the first step in Lexical Simplification (Shardlow, 2013). We examine three different Vietnamese datasets constructed for other Natural Language Processing tasks and show that, like in other languages, frequency is a strong signal in determining whether a word is complex, with a mean accuracy of 86.87%. Across the datasets, we find that the 10% most frequent words in many corpus can be labelled as simple, and the rest as complex, though this is more variable for smaller corpora. We also examine how human annotators perform at this task. Given the subjective nature, there is a fair amount of variability in which words are seen as difficult, though majority results are more consistent.
Benchmarking Language-agnostic Intent Classification for Virtual Assistant Platforms
Authors: Gengyu Wang, Cheng Qian, Lin Pan, Haode Qi, Ladislav Kunc, Saloni Potdar
Date: 2022-07-01 00:00:00
Proceeding: NAACL (MIA) 2022 7
Tasks: Benchmarking, Classification, intent-classification, Intent Classification

Current virtual assistant (VA) platforms are beholden to the limited number of languages they support. Every component, such as the tokenizer and intent classifier, is engineered for specific languages in these intricate platforms. Thus, supporting a new language in such platforms is a resource-intensive operation requiring expensive re-training and re-designing. In this paper, we propose a benchmark for evaluating language-agnostic intent classification, the most critical component of VA platforms. To ensure the benchmarking is challenging and comprehensive, we include 29 public and internal datasets across 10 low-resource languages and evaluate various training and testing settings with consideration of both accuracy and training time. The benchmarking result shows that Watson Assistant, among 7 commercial VA platforms and pre-trained multilingual language models (LMs), demonstrates close-to-best accuracy with the best accuracy-training time trade-off.
Zero-shot cross-lingual open domain question answering
Authors: Sumit Agarwal, Suraj Tripathi, Teruko Mitamura, Carolyn Penstein Rose
Date: 2022-07-01 00:00:00
Proceeding: NAACL (MIA) 2022 7
Tasks: Decoder, Open-Domain Question Answering, Question Answering

People speaking different kinds of languages search for information in a cross-lingual manner. They tend to ask questions in their language and expect the answer to be in the same language, despite the evidence lying in another language. In this paper, we present our approach for this task of cross-lingual open-domain question-answering. Our proposed method employs a passage reranker, the fusion-in-decoder technique for generation, and a wiki data entity-based post-processing system to tackle the inability to generate entities across all languages. Our end-2-end pipeline shows an improvement of 3 and 4.6 points on F1 and EM metrics respectively, when compared with the baseline CORA model on the XOR-TyDi dataset. We also evaluate the effectiveness of our proposed techniques in the zero-shot setting using the MKQA dataset and show an improvement of 5 points in F1 for high-resource and 3 points improvement for low-resource zero-shot languages. Our team, CMUmQA’s submission in the MIA-Shared task ranked 1st in the constrained setup for the dev and 2nd in the test setting.
The ethical role of computational linguistics in digital psychological formulation and suicide prevention.
Authors: Martin Orr, Kirsten Van Kessel, Dave Parry
Date: 2022-07-01 00:00:00
Proceeding: NAACL (CLPsych) 2022 7
Tasks: Diagnostic

Formulation is central to clinical practice. Formulation has a factor weighing, pattern recognition and explanatory hypothesis modelling focus. Formulation attempts to make sense of why a person presents in a certain state at a certain time and context, and how that state may be best managed to enhance mental health, safety and optimal change. Inherent to the clinical need for formulation is an appreciation of the complexities, uncertainty and limits of applying theoretical concepts and symptom, diagnostic and risk categories to human experience; or attaching meaning or weight to any particular factor in an individual?s history or mental state without considering the broader biopsychosocial and cultural context. With specific reference to suicide prevention, this paper considers the need and potential for the computer linguistic community to be both cognisant of and ethically contribute to the clinical formulation process.
Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks
Authors: Ayah Zirikly, Mark Dredze
Date: 2022-07-01 00:00:00
Proceeding: NAACL (CLPsych) 2022 7
Tasks: Multi-Task Learning

Models of mental health based on natural language processing can uncover latent signals of mental health from language. Models that indicate whether an individual is depressed, or has other mental health conditions, can aid in diagnosis and treatment. A critical aspect of integration of these models into the clinical setting relies on explaining their behavior to domain experts. In the case of mental health diagnosis, clinicians already rely on an assessment framework to make these decisions; that framework can help a model generate meaningful explanations.In this work we propose to use PHQ-9 categories as an auxiliary task to explaining a social media based model of depression. We develop a multi-task learning framework that predicts both depression and PHQ-9 categories as auxiliary tasks. We compare the quality of explanations generated based on the depression task only, versus those that use the predicted PHQ-9 categories. We find that by relying on clinically meaningful auxiliary tasks, we produce more meaningful explanations.
Identifying stable speech-language markers of autism in children: Preliminary evidence from a longitudinal telephony-based study
Authors: Sunghye Cho, Riccardo Fusaroli, Maggie Rose Pelella, Kimberly Tena, Azia Knox, Aili Hauptmann, Maxine Covello, Alison Russell, Judith Miller, Alison Hulink, Jennifer Uzokwe, Kevin Walker, James Fiumara, Juhi Pandey, Christopher Chatham, Christopher Cieri, Robert Schultz, Mark Liberman, Julia Parish-Morris
Date: 2022-07-01 00:00:00
Proceeding: NAACL (CLPsych) 2022 7

This study examined differences in linguistic features produced by autistic and neurotypical (NT) children during brief picture descriptions, and assessed feature stability over time. Weekly speech samples from well-characterized participants were collected using a telephony system designed to improve access for geographically isolated and historically marginalized communities. Results showed stable group differences in certain acoustic features, some of which may potentially serve as key outcome measures in future treatment studies. These results highlight the importance of eliciting semi-structured speech samples in a variety of contexts over time, and adds to a growing body of research showing that fine-grained naturalistic communication features hold promise for intervention research.