pwc-archive/papers-with-abstracts huggingface dataset:
COVID-19 and the gig economy in Poland
Authors: Maciej Beręsewicz, Dagmara Nikulin
Date: 2021-07-23 00:00:00
Tasks: counterfactual, Time Series, Time Series Analysis

We use a dataset covering nearly the entire target population based on passively collected data from smartphones to measure the impact of the first COVID-19 wave on the gig economy in Poland. In particular, we focus on transportation (Uber, Bolt) and delivery (Wolt, Takeaway, Glover, DeliGoo) apps, which make it possible to distinguish between the demand and supply part of this market. Based on Bayesian structural time-series models, we estimate the causal impact of the first COVID-19 wave on the number of active drivers and couriers. We show a significant relative increase for Wolt and Glover (15% and 24%) and a slight relative decrease for Uber and Bolt (-3% and -7%) in comparison to a counterfactual control. The change for Uber and Bolt can be partially explained by the prospect of a new law (the so-called Uber Lex), which was already announced in 2019 and is intended to regulate the work of platform drivers.
Where do I rank? Am I happy?: learning income position and subjective-wellbeing in an internet experiment
Authors: Eiji Yamamura
Date: 2021-07-21 00:00:00
Tasks: Position, Survey

A tailor-made internet survey experiment provides individuals with information on their income positions to examine their effects on subjective well-being. In the first survey, respondents were asked about their household income and subjective well-being. Based on the data collected, three different respondents' income positions within the residential locality, within a group of the same educational background, and cohort were obtained. In the follow-up survey for the treatment group, respondents are informed of their income positions and then asked for subjective well-being. Key findings are that, after obtaining information, a higher individual's income position improves their subjective well-being. The effects varied according to individual characteristics and proxies.
Generation of annotated multimodal ground truth datasets for abdominal medical image registration
Authors: Dominik F. Bauer, Tom Russ, Barbara I. Waldkirch, Christian Tönnes, William P. Segars, Lothar R. Schad, Frank G. Zöllner, Alena-Kathrin Golla
Date: 2020-12-02 00:00:00
Tasks: Computed Tomography (CT), Image Registration, Image Segmentation, Medical Image Registration, Semantic Segmentation

Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets. We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom, therefore the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed. Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging (MRI), computed tomography (CT), and cone beam CT (CBCT) images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation. Our proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms.
Three-Dimensional Localization of Active Aerial Targets Using a Single Terrestrial Receiver Site
Authors: Saber Kaviani, Fereidoon Behnia
Date: 2021-07-23 00:00:00
Tasks: Position

This paper proposes a method for the three-dimensional localization of an active aerial target by a single ground based sensor. The proposed method employs the time and frequency differences of arrival of the signal received directly from the aerial target and the signals received after being reflected from some large auxiliary terrestrial targets (pseudo-sensors) with known positions on the ground. Due to the terrestrial nature of the main and the pseudo sensors, it is impossible to solve for the target's altitude using traditional methods. The proposed method employs target motion analysis to obtain target position including its altitude with acceptable accuracy and low computational complexity. Presented simulations confirm acceptable accuracy of the proposed method in determining three dimensional position of the target despite limited number of the pseudo sensors and its low computational complexity.
Adaptive Event Detection for Representative Load Signature Extraction
Authors: Lei Yan, Wei Tian, Jiayu Han, Zuyi Li
Date: 2021-07-23 00:00:00
Tasks: Event Detection, Non-Intrusive Load Monitoring

Event detection is the first step in event-based non-intrusive load monitoring (NILM) and it can provide useful transient information to identify appliances. However, existing event detection methods with fixed parameters may fail in case of unpredictable and complicated residential load changes such as high fluctuation, long transition, and near simultaneity. This paper proposes a dynamic time-window approach to deal with these highly complex load variations. Specifically, a window with adaptive margins, multi-timescale window screening, and adaptive threshold (WAMMA) method is proposed to detect events in aggregated home appliance load data with high sampling rate (>1Hz). The proposed method accurately captures the transient process by adaptively tuning parameters including window width, margin width, and change threshold. Furthermore, representative transient and steady-state load signatures are extracted and, for the first time, quantified from transient and steady periods segmented by detected events. Case studies on a 20Hz dataset, the 50Hz LIFTED dataset, and the 60Hz BLUED dataset show that the proposed method can robustly outperform other state-of-art event detection methods. This paper also shows that the extracted load signatures can improve NILM accuracy and help develop other applications such as load reconstruction to generate realistic load data for NILM research.
Compressed Ultrasound Imaging:from Sub-Nyquist Rates to Super-Resolution
Authors: Oded Drori, Alon Mamistvalov, Oren Solomon, Yonina C. Eldar
Date: 2021-07-23 00:00:00
Tasks: Super-Resolution

The multi-billion dollar, worldwide medical ultrasound (US) market continues to grow annually. Its non-ionizing nature, real-time capabilities and relatively low cost, compared to other imaging modalities, have led to significant applications in many different fields, including cardiology, angiology, obstetrics and emergency medicine. Facilitated by ongoing innovations, US continues to change rules and norms regarding patient screening, diagnosis and surgery. This huge and promising market is constantly driven by new imaging and processing techniques. From 3D images to sophisticated software, hardware and portability improvements, it is clear that the status of US as one of the leading medical imaging technologies is ensured for many years ahead. However, as imaging systems evolve, new engineering challenges emerge. Acquisition, transmission and processing of huge amounts of data are common for all ultrasound-based imaging modalities. Moreover, achieving higher resolution is constantly on demand, as improved diagnosis could be achieved by better visualization of organs and blood vessels deep within tissues. In this article, our goal is to motivate further interest and research in emerging processing techniques, as well as their applications in medical ultrasound, enabled by recent advancements in signal processing algorithms and deep learning. We address some of the primary challenges and potential remedies from a signal processing perspective, by exploiting the inherent structure of the received US signal.
Resource Constrained Neural Networks for 5G Direction-of-Arrival Estimation in Micro-controllers
Authors: Piyush Sahoo, Romesh Rajoria, Shivam Chandhok, S. J. Darak, Danilo Pau, Hem-Dutt Dabral
Date: 2021-07-23 00:00:00
Tasks: Direction of Arrival Estimation

With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency, and spatial domains. Narrow beam-forming makes it difficult to have spatial sensing (direction-of-arrival, DoA, estimation) in a centralized manner, and with the evolution of paradigms such as artificial intelligence of Things (AIOT), ultra-reliable low latency communication (URLLC) services and distributed networks, intelligence for edge devices (Edge-AI) is highly desirable. It helps to reduce the data-communication overhead compared to cloud-AI-centric networks and is more secure and free from scalability limitations. However, achieving desired functional accuracy is a challenge on edge devices such as microcontroller units (MCU) due to area, memory, and power constraints. In this work, we propose low complexity neural network-based algorithm for accurate DoA estimation and its efficient mapping on the off-the-self MCUs. An ad-hoc graphical-user interface (GUI) is developed to configure the STM32 NUCLEO-H743ZI2 MCU with the proposed algorithm and to validate its functionality. The performance of the proposed algorithm is analyzed for different signal-to-noise ratios (SNR), word-length, the number of antennas, and DoA resolution. In-depth experimental results show that it outperforms the conventional statistical spatial sensing approach.
Joint Communication and Trajectory Design for Intelligent Reflecting Surface Empowered UAV SWIPT Networks
Authors: Zhendong Li, Wen Chen, Huanqing Cao, Hongying Tang, Kunlun Wang, Jun Li
Date: 2021-07-23 00:00:00

Aiming at the limited battery capacity of widely deployed low-power smart devices in the Internet-of-things (IoT), this paper proposes a novel intelligent reflecting surface (IRS) empowered unmanned aerial vehicle (UAV) simultaneous wireless information and power transfer (SWIPT) network framework, in which IRS is used to reconstruct the wireless channel to enhance the wireless energy transmission efficiency and coverage area of the UAV SWIPT networks. In this paper, we formulate an achievable sum-rate maximization problem by jointly optimizing UAV trajectory, successive interference cancellation (SIC) decoding order, UAV transmit power allocation, power splitting (PS) ratio and IRS reflection coefficient while taking account of user non-orthogonal multiple access (NOMA) and a non-linear energy harvesting model. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. We first transform the problem, and then apply the alternating optimization (AO) algorithm framework to divide the transformed problem into four sub-problems to solve it. Specifically, by applying successive convex approximation (SCA), penalty function method and difference-convex (DC) programming, UAV trajectory, SIC decoding order, UAV transmit power allocation, PS ratio and IRS reflection coefficient are alternately optimized until the convergence is achieved. Numerical simulation results verify the effectiveness of our proposed algorithm compared to other algorithms.
Fechnerian Scaling: Dissimilarity Cumulation Theory
Authors: Ehtibar N. Dzhafarov, Hans Colonius
Date: 2021-07-23 00:00:00

This is a chapter for the third volume of the New Handbook of Mathematical Psychology. It presented mathematical foundations of Fechnerian Scaling, a method of metrizing stimulus spaces based on subjective measures of dissimilarity.
Microbial virus epidemics in the presence of contact-mediated host dormancy
Authors: Jochen Blath, András Tóbiás
Date: 2021-07-23 00:00:00

We investigate a stochastic individual-based model for the population dynamics of host-virus systems where the microbial hosts may transition into a dormant state upon contact with virions, thus evading infection. Such a contact-mediated defence mechanism was described in Bautista et al (2015) for an archaeal host, while Jackson and Fineran (2019) and Meeske et al (2019) describe a related, CRISPR-Cas induced, dormancy defense of bacterial hosts to curb phage epidemics. We first analyse the effect of the dormancy-related model parameters on the probability and time of invasion of a newly arriving virus into a resident host population. Given successful invasion in the stochastic system, we then show that the emergence (with high probability) of a persistent virus infection ('epidemic') in a large host population can be determined by the existence of a coexistence equilibrium for the dynamical system arising as the deterministic many-particle limit of our model. This is an extension of a dynamical system considered by Beretta and Kuang (1998) that is known to exhibit a Hopf bifurcation, giving rise to a 'paradox of enrichment'. In our system, we verify that the additional dormancy component can, at least for certain parameter ranges, prevent the associated loss of stability. Finally, we show that the presence of contact-mediated dormancy enables the host population to attain higher equilibrium sizes - while still being able to avoid a persistent epidemic - than host populations without this trait.
Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data
Authors: Thi Thanh Yen Nguyen, Warith Harchaoui, Lucile Mégret, Cloe Mendoza, Olivier Bouaziz, Christian Neri, Antoine Chambaz
Date: 2021-07-21 00:00:00
Tasks: BIG-bench Machine Learning

We present several algorithms designed to learn a pattern of correspondence between two data sets in situations where it is desirable to match elements that exhibit a relationship belonging to a known parametric model. In the motivating case study, the challenge is to better understand micro-RNA regulation in the striatum of Huntington's disease model mice. The algorithms unfold in two stages. First, an optimal transport plan P and an optimal affine transformation are learned, using the Sinkhorn-Knopp algorithm and a mini-batch gradient descent. Second, P is exploited to derive either several co-clusters or several sets of matched elements. A simulation study illustrates how the algorithms work and perform. The real data application further illustrates their applicability and interest.
Dynamic variability of the phytoplankton electron requirement for carbon fixation in eastern Australian waters
Authors: David J. Hughes, Joseph R Crosswell, Martina A. Doblin, Kevin Oxborough, Peter J. Ralph, Deepa Varkey, David J. Suggett
Date: 2021-07-23 00:00:00

Fast Repetition Rate fluorometry (FRRf) generates high-resolution measures of phytoplankton primary productivity as electron transport rates (ETRs). How ETRs scale to corresponding inorganic carbon (C) uptake rates (the so-called electron requirement for carbon fixation, e,C), inherently describes the extent and effectiveness with which absorbed light energy drives C-fixation. However, it remains unclear whether and how e,C follows predictable patterns for oceanographic datasets spanning physically dynamic, and complex, environmental gradients. We utilise a unique high-throughput approach, coupling ETRs and 14C-incubations to produce a semi-continuous dataset of e,C (n = 80), predominantly from surface waters, along the Australian coast (Brisbane to the Tasman Sea), including the East Australian Current (EAC). Environmental conditions along this transect could be generally grouped into cooler, more nutrient-rich waters dominated by larger size-fractionated Chl-a (>10 um) versus warmer nutrient-poorer waters dominated by smaller size-fractionated Chl-a (< 2 um). Whilst e,C was higher for warmer water samples, environmental conditions alone explained less than 20% variance of e,C, and changes in predominant size-fraction(s) distributions of Chl-a (biomass) failed to explain variance of e,C. Instead, NPQNSV was a better predictor of e,C, explaining 55% of observed variability. NPQNSV is a physiological descriptor that accounts for changes in both long-term driven acclimation in non-radiative decay, and quasi-instantaneous PSII downregulation, and thus may prove a useful predictor of e,C across physically-dynamic regimes, provided the slope describing their relationship is predictable.
IBEX: An open and extensible method for high content multiplex imaging of diverse tissues
Authors: Andrea J. Radtke, Colin J. Chu, Ziv Yaniv, Li Yao, James Marr, Rebecca T. Beuschel, Hiroshi Ichise, Anita Gola, Juraj Kabat, Bradley Lowekamp, Emily Speranza, Joshua Croteau, Nishant Thakur, Danny Jonigk, Jeremy Davis, Jonathan M. Hernandez, Ronald N. Germain
Date: 2021-07-23 00:00:00

High content imaging is needed to catalogue the variety of cellular phenotypes and multi-cellular ecosystems present in metazoan tissues. We recently developed Iterative Bleaching Extends multi-pleXity (IBEX), an iterative immunolabeling and chemical bleaching method that enables multiplexed imaging (>65 parameters) in diverse tissues, including human organs relevant for international consortia efforts. IBEX is compatible with over 250 commercially available antibodies, 16 unique fluorophores, and can be easily adopted to different imaging platforms using slides and non-proprietary imaging chambers. The overall protocol consists of iterative cycles of antibody labelling, imaging, and chemical bleaching that can be completed at relatively low cost in 2-5 days by biologists with basic laboratory skills. To support widespread adoption, we provide extensive details on tissue processing, curated lists of validated antibodies, and tissue-specific panels for multiplex imaging. Furthermore, instructions are included on how to automate the method using competitively priced instruments and reagents. Finally, we present a software solution for image alignment that can be executed by individuals without programming experience using open source software and freeware. In summary, IBEX is an open and versatile method that can be readily implemented by academic laboratories and scaled to achieve high content mapping of diverse tissues in support of a Human Reference Atlas or other such applications.
Deep equal risk pricing of financial derivatives with non-translation invariant risk measures
Authors: Alexandre Carbonneau, Frédéric Godin
Date: 2021-07-23 00:00:00
Tasks: Deep Reinforcement Learning, ERP, Translation

The use of non-translation invariant risk measures within the equal risk pricing (ERP) methodology for the valuation of financial derivatives is investigated. The ability to move beyond the class of convex risk measures considered in several prior studies provides more flexibility within the pricing scheme. In particular, suitable choices for the risk measure embedded in the ERP framework such as the semi-mean-square-error (SMSE) are shown herein to alleviate the price inflation phenomenon observed under Tail Value-at-Risk based ERP as documented for instance in Carbonneau and Godin (2021b). The numerical implementation of non-translation invariant ERP is performed through deep reinforcement learning, where a slight modification is applied to the conventional deep hedging training algorithm (see Buehler et al., 2019) so as to enable obtaining a price through a single training run for the two neural networks associated with the respective long and short hedging strategies. The accuracy of the neural network training procedure is shown in simulation experiments not to be materially impacted by such modification of the training algorithm.
A bridge between Local GAAP and Solvency II frameworks to quantify Capital Requirement for demographic risk
Authors: Gian Paolo Clemente, Francesco Della Corte, Nino Savelli
Date: 2021-07-22 00:00:00

The paper provides a stochastic model useful for assessing the capital requirement for demographic risk. The model extends to the market consistent context classical methodologies developed in a local accounting framework. In particular we provide a unique formulation for different non-participating life insurance contracts and we prove analytically that the valuation of demographic profit can be significantly affected by the financial conditions in the market. A case study has been also developed considering a portfolio of life insurance contracts. Results prove the effectiveness of the model in highlighting main drivers of capital requirement evaluation, also compared to local GAAP framework.
Reference Class Selection in Similarity-Based Forecasting of Sales Growth
Authors: Etienne Theising, Dominik Wied, Daniel Ziggel
Date: 2021-07-23 00:00:00

This paper proposes a general method to handle forecasts exposed to behavioural bias by finding appropriate outside views, in our case corporate sales forecasts of analysts. The idea is to find reference classes, i.e. peer groups, for each analyzed company separately that share similarities to the firm of interest with respect to a specific predictor. The classes are regarded to be optimal if the forecasted sales distributions match the actual distributions as closely as possible. The forecast quality is measured by applying goodness-of-fit tests on the estimated probability integral transformations and by comparing the predicted quantiles. The method is out-of-sample backtested on a data set consisting of 21,808 US firms over the time period 1950 - 2019, which is also descriptively analyzed. It appears that in particular the past operating margins are good predictors for the distribution of future sales. A case study compares the outside view of our distributional forecasts with actual analysts' forecasts and emphasizes the relevance of our approach in practice.
Describing objects by their attributes
Authors: Ali Farhadi; Ian Endres; Derek Hoiem; David Forsyth
Date: 2019-08-18 00:00:00
Proceeding: CVPR 2019 8
Tasks: Attribute, feature selection

We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.