pwc-archive/papers-with-abstracts huggingface dataset:
Mostly electric assisted airplanes (MEAP) for regional aviation: A South Asian perspective
Authors: Vinamra Chaturvedi
Date: 2021-03-24 00:00:00

Aircraft manufacturing relies on pre-order bookings. The configuration of the to be assembled aircraft is fixed by the design assisted market surveys. The sensitivity of the supply chain to the market conditions, makes, the relationship between the product (aircraft) and the associated service (aviation), precarious. Traditional model to mitigate this risk to profitability rely on increasing the scales of operations. However, the emergence of new standards of air quality monitoring and insistence on the implementation, demands additional corrective measures. In the quest for a solution, this research commentary establishes a link, between the airport taxes and the nature of the transporting unit. It warns, that merely, increasing the number of mid haulage range aircrafts (MHA) in the fleet, may not be enough, to overcome this challenge. In a two-pronged approach, the communication proposes, the use of mostly electric assisted air planes, and small sized airports as the key to solving this complex problem. As a side-note the appropriateness of South Asian region, as a test-bed for MEAP based aircrafts is also investigated. The success of this the idea can be potentially extended, to any other aviation friendly region of the world.
Pricing Energy Derivatives in Markets Driven by Tempered Stable and CGMY Processes of Ornstein-Uhlenbeck Type
Authors: Piergiacomo Sabino
Date: 2021-03-24 00:00:00

In this study we consider the pricing of energy derivatives when the evolution of spot prices follows a tempered stable or a CGMY driven Ornstein- Uhlenbeck process. To this end, we first calculate the characteristic function of the transition law of such processes in closed form. This result is instrumental for the derivation of non-arbitrage conditions such that the spot dynamics is consistent with the forward curve. Moreover, based on the results of Cufaro Petroni and Sabino (2020), we also conceive efficient algorithms for the exact simulation of the skeleton of such processes and propose a novel procedure when they coincide with compound Poisson processes of Ornstein-Uhlenbeck type. We illustrate the applicability of the theoretical findings and the simulation algorithms in the context of the pricing different contracts namely, strips of daily call options, Asian options with European style and swing options. Finally, we present an extension to future markets.
Assets Defending Differential Games with Partial Information and Selected Observations
Authors: Yunhan Huang, Juntao Chen, Quanyan Zhu
Date: 2021-03-24 00:00:00

In this paper, we consider a linear-quadratic-Gaussian defending assets differential game (DADG) where the attacker and the defender do not know each other's state information while they know the trajectory of a moving asset. Both players can choose to observe the other player's state information by paying a cost. The defender and the attacker have to craft both control strategies and observation strategies. We obtain a closed-form feedback solution that characterizes the Nash control strategies. We show that the trajectory of the asset does not affect both players' observation choices. Moreover, we show that the observation choices of the defender and the attacker can be decoupled and the Nash observation strategies can be found by solving two independent optimization problems. A set of necessary conditions is developed to characterize the optimal observation instances. Based on the necessary conditions, an effective algorithm is proposed to numerically compute the optimal observation instances. A case study is presented to demonstrate the effectiveness of the optimal observation instances.
Simple Diagnostics for Two-Way Fixed Effects
Authors: Pamela Jakiela
Date: 2021-03-24 00:00:00
Tasks: Vocal Bursts Valence Prediction

Difference-in-differences estimation is a widely used method of program evaluation. When treatment is implemented in different places at different times, researchers often use two-way fixed effects to control for location-specific and period-specific shocks. Such estimates can be severely biased when treatment effects change over time within treated units. I review the sources of this bias and propose several simple diagnostics for assessing its likely severity. I illustrate these tools through a case study of free primary education in Sub-Saharan Africa.
An investigation of higher order moments of empirical financial data and the implications to risk
Authors: Luke De Clerk, Sergey Savel'ev
Date: 2021-03-24 00:00:00
Tasks: Time Series, Time Series Analysis

Here, we analyse the behaviour of the higher order standardised moments of financial time series when we truncate a large data set into smaller and smaller subsets, referred to below as time windows. We look at the effect of the economic environment on the behaviour of higher order moments in these time windows. We observe two different scaling relations of higher order moments when the data sub sets' length decreases; one for longer time windows and another for the shorter time windows. These scaling relations drastically change when the time window encompasses a financial crisis. We also observe a qualitative change of higher order standardised moments compared to the gaussian values in response to a shrinking time window. We extend this analysis to incorporate the effects these scaling relations have upon risk. We decompose the return series within these time windows and carry out a Value-at-Risk calculation. In doing so, we observe the manifestation of the scaling relations through the change in the Value-at-Risk level. Moreover, we model the observed scaling laws by analysing the hierarchy of rare events on higher order moments.
Topology Design for GNSSs Considering Both Inter-satellite Links and Ground-satellite Links
Authors: Z. Yan, K. Zhao, W. Li, C. Kang, J. Zheng, H. Yang, S. Du
Date: 2021-03-24 00:00:00
Tasks: Data Interaction, Management

Inter-satellite links (ISLs) are adopted in global navigation satellite systems (GNSSs) for high-precision orbit determination and space-based end-to-end telemetry telecommand control and communications. Due to limited onboard ISL terminals, the polling time division duplex (PTDD) mechanism is usually proposed for space link layer networking. By extending the polling mechanism to ground-satellite links (GSLs), a unified management system of the space segment and the ground segment can be realized. However, under the polling system how to jointly design the topology of ISLs and GSLs during every slot to improve data interaction has not been studied. In this paper, we formulate the topology design problem as an integer linear programming, aiming at minimizing the average delay of data delivery from satellites to ground stations while satisfying the ranging requirement for the orbit determination. To tackle the computational complexity problem, we first present a novel modeling method of delay to reduce the number of decision variables. Further, we propose a more efficient heuristic based on maximum weight matching algorithms. Simulation results demonstrate the feasibility of the proposed methods for practical operation in GNSSs. Comparing the two methods, the heuristic can achieve similar performance with respect to average delay but with significantly less complexity.
RSS-based Cooperative Localization and Orientation Estimation Exploiting Directive Antenna Patterns
Authors: Lukas Wielandner, Erik Leitinger, Klaus Witrisal
Date: 2021-03-24 00:00:00
Tasks: Position

In this paper, we propose a factor-graph-based cooperative positioning algorithm that uses RSS radio measurements and accounts for the directivity of the antennas. This is achieved by modeling the directivity with a parametric antenna pattern and jointly estimating position and orientation of the agents. We propose two different approaches whereas the first one uses a continuous representation of the orientation state and the second one a discrete representation. We validate our proposed methods with simulations and measurements in a static sensor network with more than 900 agents in an indoor environment and show that the positioning accuracy can be improved significantly by considering the influence of orientations.
Evaluating Policies Early in a Pandemic: Bounding Policy Effects with Nonrandomly Missing Data
Authors: Brantly Callaway, Tong Li
Date: 2020-05-19 00:00:00

During the early part of the Covid-19 pandemic, national and local governments introduced a number of policies to combat the spread of Covid-19. In this paper, we propose a new approach to bound the effects of such early-pandemic policies on Covid-19 cases and other outcomes while dealing with complications arising from (i) limited availability of Covid-19 tests, (ii) differential availability of Covid-19 tests across locations, and (iii) eligibility requirements for individuals to be tested. We use our approach study the effects of Tennessee's expansion of Covid-19 testing early in the pandemic and find that the policy decreased Covid-19 cases.
Coevolution of the reckless prey and the patient predator
Authors: Cecilia Berardo, Stefan Geritz
Date: 2021-03-24 00:00:00

The war of attrition in game theory is a model of a stand-off situation between two opponents where the winner is determined by its persistence. We model a stand-off between a predator and a prey when the prey is hiding and the predator is waiting for the prey to come out from its refuge, or when the two are locked in a situation of mutual threat of injury or even death. The stand-off is resolved when the predator gives up or when the prey tries to escape. Instead of using the asymmetric war of attrition, we embed the stand-off as an integral part of the predator-prey model of Rosenzweig and MacArthur derived from first principles. We apply this model to study the coevolution of the giving-up rates of the prey and the predator, using the adaptive dynamics approach. We find that the long term evolutionary process leads to three qualitatively different scenarios: the predator gives up immediately, while the prey never gives up; the predator never gives up, while the prey adopts any giving-up rate greater than or equal to a given positive threshold value; the predator goes extinct. We observe that some results are the same as for the asymmetric war of attrition, but others are quite different.
CUHK-EE Voice Cloning System for ICASSP 2021 M2VoC Challenge
Authors: Daxin Tan, Hingpang Huang, Guangyan Zhang, Tan Lee
Date: 2021-03-08 00:00:00
Tasks: Voice Cloning

This paper presents the CUHK-EE voice cloning system for ICASSP 2021 M2VoC challenge. The challenge provides two Mandarin speech corpora: the AIShell-3 corpus of 218 speakers with noise and reverberation and the MST corpus including high-quality speech of one male and one female speakers. 100 and 5 utterances of 3 target speakers in different voice and style are provided in track 1 and 2 respectively, and the participants are required to synthesize speech in target speaker's voice and style. We take part in the track 1 and carry out voice cloning based on 100 utterances of target speakers. An end-to-end voicing cloning system is developed to accomplish the task, which includes: 1. a text and speech front-end module with the help of forced alignment, 2. an acoustic model combining Tacotron2 and DurIAN to predict melspectrogram, 3. a Hifigan vocoder for waveform generation. Our system comprises three stages: multi-speaker training stage, target speaker adaption stage and target speaker synthesis stage. Our team is identified as T17. The subjective evaluation results provided by the challenge organizer demonstrate the effectiveness of our system. Audio samples are available at our demo page: https://daxintan-cuhk.github.io/CUHK-EE-system-M2VoC-challenge/ .
An evidential classifier based on Dempster-Shafer theory and deep learning
Authors: Zheng Tong, Philippe Xu, Thierry Denœux
Date: 2021-03-25 00:00:00
Tasks: Classification, General Classification

We propose a new classifier based on Dempster-Shafer (DS) theory and a convolutional neural network (CNN) architecture for set-valued classification. In this classifier, called the evidential deep-learning classifier, convolutional and pooling layers first extract high-dimensional features from input data. The features are then converted into mass functions and aggregated by Dempster's rule in a DS layer. Finally, an expected utility layer performs set-valued classification based on mass functions. We propose an end-to-end learning strategy for jointly updating the network parameters. Additionally, an approach for selecting partial multi-class acts is proposed. Experiments on image recognition, signal processing, and semantic-relationship classification tasks demonstrate that the proposed combination of deep CNN, DS layer, and expected utility layer makes it possible to improve classification accuracy and to make cautious decisions by assigning confusing patterns to multi-class sets.
Matched sample selection with GANs for mitigating attribute confounding
Authors: Chandan Singh, Guha Balakrishnan, Pietro Perona
Date: 2021-03-24 00:00:00
Tasks: Attribute, Face Generation, Gender Bias Detection, Generative Adversarial Network, Selection bias

Measuring biases of vision systems with respect to protected attributes like gender and age is critical as these systems gain widespread use in society. However, significant correlations between attributes in benchmark datasets make it difficult to separate algorithmic bias from dataset bias. To mitigate such attribute confounding during bias analysis, we propose a matching approach that selects a subset of images from the full dataset with balanced attribute distributions across protected attributes. Our matching approach first projects real images onto a generative adversarial network (GAN)'s latent space in a manner that preserves semantic attributes. It then finds image matches in this latent space across a chosen protected attribute, yielding a dataset where semantic and perceptual attributes are balanced across the protected attribute. We validate projection and matching strategies with qualitative, quantitative, and human annotation experiments. We demonstrate our work in the context of gender bias in multiple open-source facial-recognition classifiers and find that bias persists after removing key confounders via matching. Code and documentation to reproduce the results here and apply the methods to new data is available at https://github.com/csinva/matching-with-gans .
SSLayout360: Semi-Supervised Indoor Layout Estimation from 360-Degree Panorama
Authors: Phi Vu Tran
Date: 2021-03-25 00:00:00
Tasks: 3D Room Layouts From A Single RGB Panorama

Recent years have seen flourishing research on both semi-supervised learning and 3D room layout reconstruction. In this work, we explore the intersection of these two fields to advance the research objective of enabling more accurate 3D indoor scene modeling with less labeled data. We propose the first approach to learn representations of room corners and boundaries by using a combination of labeled and unlabeled data for improved layout estimation in a 360-degree panoramic scene. Through extensive comparative experiments, we demonstrate that our approach can advance layout estimation of complex indoor scenes using as few as 20 labeled examples. When coupled with a layout predictor pre-trained on synthetic data, our semi-supervised method matches the fully supervised counterpart using only 12% of the labels. Our work takes an important first step towards robust semi-supervised layout estimation that can enable many applications in 3D perception with limited labeled data.
DCcov: Repositioning of Drugs and Drug Combinations for SARS-CoV-2 Infected Lung through Constraint-Based Modelling
Authors: Ali Kishk, Maria Pires Pacheco, Thomas Sauter
Date: 2021-03-25 00:00:00

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no effective antiviral drug except treatments for symptomatic therapy. Flux balance analysis is an efficient method to analyze metabolic networks. It allows optimizing for a metabolic function and thus e.g., predicting the growth rate of a specific cell or the production rate of a metabolite of interest. Here flux balance analysis was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the replication of the SARS-CoV-2 virus within the host tissue. Making use of expression data sets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then host-specific essential genes and gene-pairs were determined through in-silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, as well as ferroptosis, sphingolipid metabolism, cysteine metabolism, and fat digestion. By in-silico screening of FDA approved drugs on the putative disease-specific essential genes and gene-pairs, 45 drugs and 99 drug combinations were predicted as promising candidates for COVID-19 focused drug repositioning (https://github.com/sysbiolux/DCcov). Among the 45 drug candidates, six antiviral drugs were found and seven drugs that are being tested in clinical trials against COVID-19. Other drugs like gemcitabine, rosuvastatin and acetylcysteine, and drug combinations like azathioprine-pemetrexed might offer new chances for treating COVID-19.
Evidential fully convolutional network for semantic segmentation
Authors: Zheng Tong, Philippe Xu, Thierry Denœux
Date: 2021-03-25 00:00:00
Tasks: Decoder, Scene Parsing, Segmentation, Semantic Segmentation

We propose a hybrid architecture composed of a fully convolutional network (FCN) and a Dempster-Shafer layer for image semantic segmentation. In the so-called evidential FCN (E-FCN), an encoder-decoder architecture first extracts pixel-wise feature maps from an input image. A Dempster-Shafer layer then computes mass functions at each pixel location based on distances to prototypes. Finally, a utility layer performs semantic segmentation from mass functions and allows for imprecise classification of ambiguous pixels and outliers. We propose an end-to-end learning strategy for jointly updating the network parameters, which can make use of soft (imprecise) labels. Experiments using three databases (Pascal VOC 2011, MIT-scene Parsing and SIFT Flow) show that the proposed combination improves the accuracy and calibration of semantic segmentation by assigning confusing pixels to multi-class sets.
ModGNN: Expert Policy Approximation in Multi-Agent Systems with a Modular Graph Neural Network Architecture
Authors: Ryan Kortvelesy, Amanda Prorok
Date: 2021-03-24 00:00:00
Tasks: Graph Neural Network

Recent work in the multi-agent domain has shown the promise of Graph Neural Networks (GNNs) to learn complex coordination strategies. However, most current approaches use minor variants of a Graph Convolutional Network (GCN), which applies a convolution to the communication graph formed by the multi-agent system. In this paper, we investigate whether the performance and generalization of GCNs can be improved upon. We introduce ModGNN, a decentralized framework which serves as a generalization of GCNs, providing more flexibility. To test our hypothesis, we evaluate an implementation of ModGNN against several baselines in the multi-agent flocking problem. We perform an ablation analysis to show that the most important component of our framework is one that does not exist in a GCN. By varying the number of agents, we also demonstrate that an application-agnostic implementation of ModGNN possesses an improved ability to generalize to new environments.
Predicting Directionality in Causal Relations in Text
Authors: Pedram Hosseini, David A. Broniatowski, Mona Diab
Date: 2021-03-25 00:00:00
Tasks: Sentence

In this work, we test the performance of two bidirectional transformer-based language models, BERT and SpanBERT, on predicting directionality in causal pairs in the textual content. Our preliminary results show that predicting direction for inter-sentence and implicit causal relations is more challenging. And, SpanBERT performs better than BERT on causal samples with longer span length. We also introduce CREST which is a framework for unifying a collection of scattered datasets of causal relations.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Authors: Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo
Date: 2021-03-25 00:00:00
Proceeding: ICCV 2021 10
Tasks: image-classification, Image Classification, Instance Segmentation, Object Detection, Real-Time Object Detection, Semantic Segmentation, Thermal Image Segmentation

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with \textbf{S}hifted \textbf{win}dows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures. The code and models are publicly available at~\url{https://github.com/microsoft/Swin-Transformer}.
An Empirical Analysis of Image-Based Learning Techniques for Malware Classification
Authors: Pratikkumar Prajapati, Mark Stamp
Date: 2021-03-24 00:00:00
Tasks: Deep Learning, General Classification, Malware Classification, Transfer Learning

In this paper, we consider malware classification using deep learning techniques and image-based features. We employ a wide variety of deep learning techniques, including multilayer perceptrons (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU). Amongst our CNN experiments, transfer learning plays a prominent role specifically, we test the VGG-19 and ResNet152 models. As compared to previous work, the results presented in this paper are based on a larger and more diverse malware dataset, we consider a wider array of features, and we experiment with a much greater variety of learning techniques. Consequently, our results are the most comprehensive and complete that have yet been published.