pwc-archive/papers-with-abstracts huggingface dataset:
Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules
Authors: Yan Ding, Xiaoqian Jiang, Yejin Kim
Date: 2021-07-04 00:00:00

Evaluating the blood-brain barrier (BBB) permeability of drug molecules is a critical step in brain drug development. Traditional methods for the evaluation require complicated in vitro or in vivo testing. Alternatively, in silico predictions based on machine learning have proved to be a cost-efficient way to complement the in vitro and in vivo methods. However, the performance of the established models has been limited by their incapability of dealing with the interactions between drugs and proteins, which play an important role in the mechanism behind the BBB penetrating behaviors. To address this limitation, we employed the relational graph convolutional network (RGCN) to handle the drug-protein interactions as well as the properties of each individual drug. The RGCN model achieved an overall accuracy of 0.872, an AUROC of 0.919 and an AUPRC of 0.838 for the testing dataset with the drug-protein interactions and the Mordred descriptors as the input. Introducing drug-drug similarity to connect structurally similar drugs in the data graph further improved the testing results, giving an overall accuracy of 0.876, an AUROC of 0.926 and an AUPRC of 0.865. In particular, the RGCN model was found to greatly outperform the LightGBM base model when evaluated with the drugs whose BBB penetration was dependent on drug-protein interactions. Our model is expected to provide high-confidence predictions of BBB permeability for drug prioritization in the experimental screening of BBB-penetrating drugs.
A decision support tool for ship biofouling management in the Baltic Sea
Authors: Emilia Luoma, Mirka Laurila-Pant, Elias Altarriba, Inari Helle, Lena Granhag, Maiju Lehtiniemi, Greta Srėbalienė, Sergej Olenin, Annukka Lehikoinen
Date: 2021-06-29 00:00:00
Tasks: Management

Biofouling of ships causes major environmental and economic consequences all over the world. In addition, biofouling management of ship hulls causes both social, environmental and economic risks that should all be considered reaching well-balanced decisions. In addition, each case is unique and thus optimal management strategy must be considered case-specifically. We produced a novel decision support tool using Bayesian networks to promote the comprehensive understanding about the complex biofouling management issue in the Baltic Sea and to identify potential management options and their consequences. The tool compares the biofouling management strategies in relation to NIS (non-indigenous species) introduction risk, eco-toxicological risk due to biocidal coating, carbon dioxide emissions resulting from fuel consumption and costs related to fuel consumption, in-water cleaning and coating. According to the results, the optimal biofouling management strategy would consist of a biocidal-free coating with regular in-water cleaning and with devices collecting the material. However, the best biocidal-free coating type and the optimal in-water cleaning interval varies and depends e.g. on the operational profile of the ship. The decision support tool can increase the multi-perspective understanding about the issue and support the implementation of the optimal biofouling management strategies in the Baltic Sea.
The Infinite Horizon Investment-Consumption Problem for Epstein-Zin Stochastic Differential Utility
Authors: David Hobson, Martin Herdegen, Joseph Jerome
Date: 2021-07-14 00:00:00

In this article we consider the optimal investment-consumption problem for an agent with preferences governed by Epstein-Zin stochastic differential utility who invests in a constant-parameter Black-Scholes-Merton market. The paper has three main goals: first, to provide a detailed introduction to infinite-horizon Epstein-Zin stochastic differential utility, including a discussion of which parameter combinations lead to a well-formulated problem; second, to prove existence and uniqueness of infinite horizon Epstein-Zin stochastic differential utility under a restriction on the parameters governing the agent's risk aversion and temporal variance aversion; and third, to provide a verification argument for the candidate optimal solution to the investment-consumption problem among all admissible consumption streams. To achieve these goals, we introduce a slightly different formulation of Epstein-Zin stochastic differential utility to that which is traditionally used in the literature. This formulation highlights the necessity and appropriateness of certain restrictions on the parameters governing the stochastic differential utility function.
Arbitrage-free pricing of CVA for cross-currency swap with wrong-way risk under stochastic correlation modeling framework
Authors: Ashish Kumar, Laszlo Markus, Norbert Hari
Date: 2021-07-13 00:00:00

A positive correlation between exposure and counterparty credit risk gives rise to the so-called Wrong-Way Risk (WWR). Even after a decade of the financial crisis, addressing WWR in both sound and tractable ways remains challenging. Academicians have proposed arbitrage-free set-ups through copula methods but those are computationally expensive and hard to use in practice. Resampling methods are proposed by the industry but they lack mathematical foundations. The purpose of this article is to bridge this gap between the approaches used by academicians and industry. To this end, we propose a stochastic correlation approach to asses WWR. The methods based on constant correlation to model the dependency between exposure and counterparty credit risk assume a linear dependency, thus fail to capture the tail dependence. Using a stochastic correlation we move further away from the Gaussian copula and can capture the tail risk. This effect is reflected in the results where the impact of stochastic correlation on calculated CVA is substantial when compared to the case when a high constant correlation is assumed between exposure and credit. Given the uncertainty inherent to CVA, the proposed method is believed to provide a promising way to model WWR.
Correlation scenarios and correlation stress testing
Authors: N. Packham, F. Woebbeking
Date: 2021-07-14 00:00:00
Tasks: Variable Selection

We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or highest density regions (HDR) on the joint risk factor distribution allows to infer worst-case correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks.
Single Event Transition Risk: A Measure for Long Term Carbon Exposure
Authors: Suryadeepto Nag, Siddhartha P. Chakrabarty, Sankarshan Basu
Date: 2021-07-14 00:00:00

Although there is a growing consensus that a low-carbon transition will be necessary to mitigate the accelerated climate change, the magnitude of transition-risk for investors is difficult to measure exactly. Investors are therefore constrained by the unavailability of suitable measures to quantify the magnitude of the risk and are forced to use the likes of absolute emissions data or ESG scores in order to manage their portfolios. In this article, we define the Single Event Transition Risk (SETR) and illustrate how it can be used to approximate the magnitude of the total exposure of the price of a share to low-carbon transition. We also discuss potential applications of the single event framework and the SETR as a risk measure and discuss future direction on how this can be extended to a system with multiple transition events.
On the short term stability of financial ARCH price processes
Authors: Gilles Zumbach
Date: 2021-07-14 00:00:00
Tasks: Time Series, Time Series Analysis

For many financial applications, it is important to have reliable and tractable models for the behavior of assets and indexes, for example in risk evaluation. A successful approach is based on ARCH processes, which strike the right balance between statistical properties and ease of computation. This study focuses on quadratic ARCH processes and the theoretical conditions to have a stable long-term behavior. In particular, the weights for the variance estimators should sum to 1, and the variance of the innovations should be 1. Using historical data, the realized empirical innovations can be computed, and their statistical properties assessed. Using samples of 3 to 5 decades, the variance of the empirical innovations are always significantly above 1, for a sample of stock indexes, commodity indexes and FX rates. This departure points to a short term instability, or to a fast adaptability due to changing conditions. Another theoretical condition on the innovations is to have a zero mean. This condition is also investigated empirically, with some time series showing significant departure from zero.
Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
Authors: Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, N. Siddharth, Samuel J. Gershman, Joshua B. Tenenbaum
Date: 2021-07-04 00:00:00
Proceeding: ICLR 2022 4
Tasks: Scene Understanding, Time Series, Time Series Analysis

Modeling complex phenomena typically involves the use of both discrete and continuous variables. Such a setting applies across a wide range of problems, from identifying trends in time-series data to performing effective compositional scene understanding in images. Here, we propose Hybrid Memoised Wake-Sleep (HMWS), an algorithm for effective inference in such hybrid discrete-continuous models. Prior approaches to learning suffer as they need to perform repeated expensive inner-loop discrete inference. We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization. We evaluate HMWS in the GP-kernel learning and 3D scene understanding domains, and show that it outperforms current state-of-the-art inference methods.
Compressive Representations of Weather Scenes for Strategic Air Traffic Flow Management
Authors: Sandip Roy
Date: 2021-07-02 00:00:00
Tasks: Management

Terse representation of high-dimensional weather scene data is explored, in support of strategic air traffic flow management objectives. Specifically, we consider whether aviation-relevant weather scenes are compressible, in the sense that each scene admits a possibly-different sparse representation in a basis of interest. Here, compression of weather scenes extracted from METAR data (including temperature, flight categories, and visibility profiles for the contiguous United States) is examined, for the graph-spectral basis. The scenes are found to be compressible, with 75-95% of the scene content captured using 0.5-4% of the basis vectors. Further, the dominant basis vectors for each scene are seen to identify time-varying spatial characteristics of the weather, and reconstruction from the compressed representation is demonstrated. Finally, potential uses of the compressive representations in strategic TFM design are briefly scoped.
Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud
Authors: Dan Jia, Bastian Leibe
Date: 2021-07-03 00:00:00
Tasks: Human Detection

In this preliminary work we attempt to apply submanifold sparse convolution to the task of 3D person detection. In particular, we present Person-MinkUNet, a single-stage 3D person detection network based on Minkowski Engine with U-Net architecture. The network achieves a 76.4% average precision (AP) on the JRDB 3D detection benchmark.
A Generalized Lottery Ticket Hypothesis
Authors: Ibrahim Alabdulmohsin, Larisa Markeeva, Daniel Keysers, Ilya Tolstikhin
Date: 2021-07-03 00:00:00

We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters. We present evidence that the original results reported for the canonical basis continue to hold in this broader setting. We describe how structured pruning methods, including pruning units or factorizing fully-connected layers into products of low-rank matrices, can be cast as particular instances of this "generalized" lottery ticket hypothesis. The investigations reported here are preliminary and are provided to encourage further research along this direction.
Procedural Content Generation using Behavior Trees (PCGBT)
Authors: Anurag Sarkar, Seth Cooper
Date: 2021-06-24 00:00:00
Tasks: Game Design

Behavior trees (BTs) are a popular method for modeling NPC and enemy AI behavior and have been widely used in commercial games. In this work, rather than use BTs to model game playing agents, we use them for modeling game design agents, defining behaviors as content generation tasks rather than in-game actions. Similar to how traditional BTs enable modeling behaviors in a modular and dynamic manner, BTs for PCG enable simple subtrees for generating parts of levels to be combined modularly to form complex trees for generating whole levels as well as generators that can dynamically vary the generated content. We refer to this approach as Procedural Content Generation using Behavior Trees, or PCGBT, and demonstrate it by using BTs to model generators for Super Mario Bros., Mega Man and Metroid levels as well as dungeon layouts and discuss several ways in which this paradigm could be applied and extended in the future.
How to make qubits speak
Authors: Bob Coecke, Giovanni De Felice, Konstantinos Meichanetzidis, Alexis Toumi
Date: 2021-07-02 00:00:00
Tasks: Question Answering

This is a story about making quantum computers speak, and doing so in a quantum-native, compositional and meaning-aware manner. Recently we did question-answering with an actual quantum computer. We explain what we did, stress that this was all done in terms of pictures, and provide many pointers to the related literature. In fact, besides natural language, many other things can be implemented in a quantum-native, compositional and meaning-aware manner, and we provide the reader with some indications of that broader pictorial landscape, including our account on the notion of compositionality. We also provide some guidance for the actual execution, so that the reader can give it a go as well.
Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT
Authors: Addie Ira Borja Parico, Tofael Ahamed
Date: 2021-07-14 00:00:00
Proceeding: Sensors 2021 7
Tasks: Multi-Object Tracking, Multiple Object Tracking, Object Counting, object-detection, Object Detection, Object Tracking

This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology for choosing the most suitable model for a desired application in agricultural sciences. In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0.50 of 98%. In terms of speed and computational cost, YOLOv4-tiny was found to be the ideal model, with a speed of more than 50 FPS and FLOPS of 6.8–14.5. If considering the balance in terms of accuracy, speed and computational cost, YOLOv4 was found to be most suitable and had the highest accuracy metrics while satisfying a real time speed of greater than or equal to 24 FPS. Between the two methods of counting with Deep SORT, the unique ID method was found to be more reliable, with an F1count of 87.85%. This was because YOLOv4 had a very low false negative in detecting pear fruits. The ROI line is more reliable because of its more restrictive nature, but due to flickering in detection it was not able to count some pears despite their being detected.
A Fuzzy Rank-based Ensemble of CNN Models for Classification of Cervical Cytology
Authors: Ankur Manna, Rohit Kundu, Dmitrii Kaplun, Aleksandr Sinitca, Ram Sarkar
Date: 2021-07-15 00:00:00
Proceeding: Scientific Reports 2021 7
Tasks: image-classification, Image Classification, Sensitivity

Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime importance for eradicating the disease from the patient’s body. However, regular population‑wise screening of cancer is limited by its expensive and labour intensive detection process, where clinicians need to classify individual cells from a stained slide consisting of more than 100,000 cervical cells, for malignancy detection. Thus, Computer‑Aided Diagnosis (CAD) systems are used as a viable alternative for easy and fast detection of cancer. In this paper, we develop such a method where we form an ensemble‑based classification model using three Convolutional Neural Network (CNN) architectures, namely Inception v3, Xception and DenseNet‑169 pre‑trained on ImageNet dataset for Pap stained single cell and whole‑slide image classification. The proposed ensemble scheme uses a fuzzy rank‑based fusion of classifiers by considering two non‑linear functions on the decision scores generated by said base learners. Unlike the simple fusion schemes that exist in the literature, the proposed ensemble technique makes the final predictions on the test samples by taking into consideration the confidence in the predictions of the base classifiers. The proposed model has been evaluated on two publicly available benchmark datasets, namely, the SIPaKMeD Pap Smear dataset and the Mendeley Liquid Based Cytology (LBC) dataset, using a 5‑fold cross‑validation scheme. On the SIPaKMeD Pap Smear dataset, the proposed framework achieves a classification accuracy of 98.55% and sensitivity of 98.52% in its 2‑class setting, and 95.43% accuracy and 98.52% sensitivity in its 5‑class setting. On the Mendeley LBC dataset, the accuracy achieved is 99.23% and sensitivity of 99.23%. The results obtained outperform many of the state‑of‑the‑art models, thereby justifying the effectiveness of the same. The relevant codes of this proposed model are publicly available on GitHub
Fuzzy Rank-based Fusion of CNN Models using Gompertz Function for Screening COVID-19 CT-Scans
Authors: Rohit Kundu, Hritam Basak, Pawan Kumar Singh, Ali Ahmadian, Massimiliano Ferrara, Ram Sarkar
Date: 2021-07-08 00:00:00
Proceeding: Scientific Reports 2021 7
Tasks: Image Classification, Transfer Learning

COVID‑19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown‑like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID‑19 to prevent the spread of the disease, even more. The current gold‑standard RT‑PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population‑wide screening. To this end, in this paper, we propose an automated COVID‑19 detection system that uses CT‑scan images of the lungs for classifying the same into COVID and Non‑COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning‑based convolutional neural network models are used, namely VGG‑11, Wide ResNet‑50‑2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state‑of‑the‑art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.
Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions
Authors: Guoliang Ji
Date: 2017-10-10 00:00:00
Proceeding: AAAI 2017 10
Tasks: Relation, Relation Extraction, Sentence, valid

Distant supervision for relation extraction is an efficient
method to scale relation extraction to very large corpora
which contains thousands of relations. However, the existing approaches have flaws on selecting valid instances and
lack of background knowledge about the entities. In this paper, we propose a sentence-level attention model to select the
valid instances, which makes full use of the supervision information from knowledge bases. And we extract entity descriptions from Freebase and Wikipedia pages to supplement
background knowledge for our task. The background knowledge not only provides more information for predicting relations, but also brings better entity representations for the
attention module. We conduct three experiments on a widely
used dataset and the experimental results show that our approach outperforms all the baseline systems significantly
Highly accurate protein structure prediction with AlphaFold
Authors: John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis
Date: 2021-07-15 00:00:00
Proceeding: Nature 2021 7
Tasks: Prediction, Protein Folding, Protein Structure Prediction

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’8, has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.