pwc-archive/papers-with-abstracts huggingface dataset:
Strategy-proof and Envy-free Mechanisms for House Allocation
Authors: Priyanka Shende, Manish Purohit
Date: 2020-10-30 00:00:00
Tasks: Fairness

We consider the problem of allocating indivisible objects to agents when agents have strict preferences over objects. There are inherent trade-offs between competing notions of efficiency, fairness and incentives in assignment mechanisms. It is, therefore, natural to consider mechanisms that satisfy two of these three properties in their strongest notions, while trying to improve on the third dimension. In this paper, we are motivated by the following question: Is there a strategy-proof and envy-free random assignment mechanism more efficient than equal division? Our contributions in this paper are twofold. First, we further explore the incompatibility between efficiency and envy-freeness in the class of strategy-proof mechanisms. We define a new notion of efficiency that is weaker than ex-post efficiency and prove that any strategy-proof and envy-free mechanism must sacrifice efficiency even in this very weak sense. Next, we introduce a new family of mechanisms called Pairwise Exchange mechanisms and make the surprising observation that strategy-proofness is equivalent to envy-freeness within this class. We characterize the set of all neutral and strategy-proof (and hence, also envy-free) mechanisms in this family and show that they admit a very simple linear representation.
Molecular Communications in Viral Infections Research: Modelling, Experimental Data and Future Directions
Authors: Michael Taynnan Barros, Mladen Veletić, Masamitsu Kanada, Massimiliano Pierobon, Seppo Vainio, Ilangko Balasingham, Sasitharan Balasubramaniam
Date: 2020-10-30 00:00:00

Hundreds of millions of people worldwide are affected by viral infections each year, and yet, several of them neither have vaccines nor effective treatment during and post-infection. This challenge has been highlighted by the COVID-19 pandemic, showing how viruses can quickly spread and how they can impact society as a whole. Novel techniques that bring in different disciplines must emerge to provide forward-looking strategies to combat viral infections, as well as possible future pandemics. In the past decade, an interdisciplinary area involving bioengineering, nanotechnology and information and communication technology (ICT) has been developing, known as Molecular Communications. This new emerging area uses elements of classical communication systems and maps it to molecular signalling and communication found inside and outside the body, where the aim is to develop new tools that can serve future medicine. In this paper, we provide an extensive and detailed discussion on how Molecular Communications can be integrated into the research on viral infectious diseases modelling, and how possible treatment and vaccines can be developed considering molecules as information carriers. We provide a literature review on the existing models of Molecular Communications for viral infection (in-body and out-body), a deep analysis on their effects on the host and subsequent communication process for other systems within the body (e.g., immune response), sources of experimental data on known viral infections and how it can be used by the Molecular Communications community, as well as open issues and future directions. Since the development of therapeutics/vaccines needs an interdisciplinary approach centred around ICT, we are confident that Molecular Communications can play a central role here by providing a detail characterisation and manipulation of the propagation of molecules in different media.
EUKulele: Taxonomic annotation of the unsung eukaryotic microbes
Date: 2020-10-30 00:00:00
Tasks: Diversity

The assessment of microbial species biodiversity is essential in ecology and
evolutionary biology (Reaka-Kudla et al. 1996), but especially challenging for
communities of microorganisms found in the environment (Das et al. 2006,
Hillebrand et al. 2018). Beyond providing a census of organisms in the ocean,
assessing marine microbial biodiversity can reveal how microbes respond to
environmental change (Salazar et al. 2017), clarify the ecological roles of
community members (Hehemann et al. 2016), and lead to biotechnology discoveries
(Das et al. 2006). Computational approaches to characterize taxonomic diversity
and phylogeny based on the quality of available data for environmental sequence
datasets is fundamental for advancing our understanding of the role of these
organisms in the environment. Even more pressing is the need for comprehensive
and consistent methods to assign taxonomy to environmentally-relevant microbial
eukaryotes. Here, we present EUKulele, an open-source software tool designed to
assign taxonomy to microeukaryotes detected in meta-omic samples, and
complement analysis approaches in other domains by accommodating assembly
output and providing concrete metrics reporting the taxonomic completeness of
each sample.
An Uncertainty Estimation Framework for Risk Assessment in Deep Learning-based Atrial Fibrillation Classification
Authors: James Belen, Sajad Mousavi, Alireza Shamsoshoara, Fatemeh Afghah
Date: 2020-10-30 00:00:00
Tasks: General Classification

Atrial Fibrillation (AF) is among one of the most common types of heart arrhythmia afflicting more than 3 million people in the U.S. alone. AF is estimated to be the cause of death of 1 in 4 individuals. Recent advancements in Artificial Intelligence (AI) algorithms have led to the capability of reliably detecting AF from ECG signals. While these algorithms can accurately detect AF with high precision, the discrete and deterministic classifications mean that these networks are likely to erroneously classify the given ECG signal. This paper proposes a variational autoencoder classifier network that provides an uncertainty estimation of the network's output in addition to reliable classification accuracy. This framework can increase physicians' trust in using AI-based AF detection algorithms by providing them with a confidence score which reflects how uncertain the algorithm is about a case and recommending them to put more attention to the cases with a lower confidence score. The uncertainty is estimated by conducting multiple passes of the input through the network to build a distribution; the mean of the standard deviations is reported as the network's uncertainty. Our proposed network obtains 97.64% accuracy in addition to reporting the uncertainty
Understanding The Role of Magnetic and Magneto-Quasistatic Fields in Human Body Communication
Authors: Mayukh Nath, Alfred Krister Ulvog, Scott Weigand, Shreyas Sen
Date: 2020-10-30 00:00:00

With the advent of wearable technologies, Human Body Communication (HBC) has emerged as a physically secure and power-efficient alternative to the otherwise ubiquitous Wireless Body Area Network (WBAN). Whereas the most investigated nodes of HBC have been Electric and Electro-quasistatic (EQS) Capacitive and Galvanic, recently Magnetic HBC (M-HBC) has been proposed as a viable alternative. Previous works have investigated M-HBC through an application point of view, without developing a fundamental working principle for the same. In this paper, for the first time, a ground up analysis has been performed to study the possible effects and contributions of the human body channel in M-HBC over a broad frequency range (1kHz to 10 GHz), by detailed electromagnetic simulations and supporting experiments. The results show that while M-HBC can be successfully operated as a body area network, the human body itself plays a minimal or negligible role in it's functionality. For frequencies less than about 30 MHz, in the domain of operation of Magneto-quasistatic (MQS) HBC, the human body is transparent to the quasistatic magnetic field. Conversely for higher frequencies, the conductive nature of human tissues end up attenuating Magnetic HBC fields due to Eddy currents induced in body tissues, eliminating the possibility of the body to support efficient waveguide modes. With this better understanding at hand, different modes of operations of MQS HBC have been outlined for both high impedance capacitive and 50 Ohm termination cases, and their performances have been compared with EQS HBC for similar sized devices, over varying distance between TX and RX. The resulting report presents the first fundamental understanding towards M-HBC operation and its contrast with EQS HBC, aiding HBC device designers to make educated design decisions, depending on mode of applications.
From data to reduced-order models via moment matching
Authors: Azka Muji Burohman, Bart Besselink, Jacquelien M. A. Scherpen, M. Kanat Camlibel
Date: 2020-10-31 00:00:00

A new method for data-driven interpolatory model reduction is presented in this paper. Using the so-called data informativity perspective, we define a framework that enables the computation of moments at given (possibly complex) interpolation points based on time-domain input-output data only, without explicitly identifying the high-order system. Instead, by characterizing the set of all systems explaining the data, necessary and sufficient conditions are provided under which all systems in this set share the same moment at a given interpolation point. Moreover, these conditions allow for explicitly computing these moments. Reduced-order models are then derived by employing a variation of the classical rational interpolation method. The condition to enforce moment matching model reduction with prescribed poles is also discussed as a means to obtain stable reduced-order models. An example of an electrical circuit illustrates this framework.
Multimodal Urban Sound Tagging with Spatiotemporal Context
Authors: Jisheng Bai, Jianfeng Chen, Mou Wang
Date: 2020-10-31 00:00:00

Noise pollution significantly affects our daily life and urban development. Urban Sound Tagging (UST) has attracted much attention recently, which aims to analyze and monitor urban noise pollution. One weakness of the previous UST studies is that the spatial and temporal context of sound signals, which contains complementary information about when and where the audio data was recorded, has not been investigated. To address this problem, in this paper, we propose a multimodal UST system that deeply mines the audio and spatiotemporal context together. In order to incorporate characteristics of different acoustic features, two sets of four spectrograms are first extracted as the inputs of residual neural networks. Then, the spatiotemporal context is encoded and combined with acoustic features to explore the efficiency of multimodal learning for discriminating sound signals. Moreover, a data filtering approach is adopted in text processing to further improve the performance of multi-modality. We evaluate the proposed method on the UST challenge (task 5) of DCASE2020. Experimental results demonstrate the effectiveness of the proposed method.
Non-Orthogonal Multiple Access (NOMA) With Multiple Intelligent Reflecting Surfaces
Authors: Yanyu Cheng, Kwok Hung Li, Yuanwei Liu, Kah Chan Teh, George K. Karagiannidis
Date: 2020-10-31 00:00:00
Tasks: Diversity

In this paper, non-orthogonal multiple access (NOMA) networks assisted by multiple intelligent reflecting surfaces (IRSs) with discrete phase shifts are investigated, in which each user device (UD) is served by an IRS to improve the quality of the received signal. Two scenarios are considered according to whether there is a direct link between the base station (BS) and each UD, and the outage performance is analyzed for each of them. Specifically, the asymptotic expressions for the upper and lower bounds of the outage probability in the high signal-to-noise ratio (SNR) regime are derived. Following that, the diversity order is obtained. It is shown that the use of discrete phase shifts does not degrade diversity order. More importantly, simulation results reveal that a 3-bit resolution for discrete phase shifts is sufficient to achieve near-optimal outage performance. Simulation results also imply the superiority of IRSs over full-duplex decode-and-forward relays.
When "Better" is better than "Best"
Authors: Ben Amiet, Andrea Collevecchio, Kais Hamza
Date: 2020-10-31 00:00:00

We consider two-player normal form games where each player has the same finite strategy set. The payoffs of each player are assumed to be i.i.d. random variables with a continuous distribution. We show that, with high probability, the better-response dynamics converge to pure Nash equilibrium whenever there is one, whereas best-response dynamics fails to converge, as it is trapped.
Wireless Power Transfer with Distributed Antennas: System Design, Prototype, and Experiments
Authors: Shanpu Shen, JungHoon Kim, Chaoyun Song, Bruno Clerckx
Date: 2020-10-31 00:00:00
Tasks: Diversity

In this paper, we design and experiment a far-field wireless power transfer (WPT) architecture based on distributed antennas, so-called WPT DAS, that dynamically selects transmit antenna and frequency to increase the output dc power. Uniquely, spatial and frequency diversities are jointly exploited in the proposed WPT DAS with low complexity, low cost, and flexible deployment to combat the wireless fading channel. A numerical experiment is designed to show the benefits using antenna and frequency selections in spatially and frequency selective fading channels for single-user and multi-user cases. Accordingly, the proposed WPT DAS for single-user and two-user cases is prototyped. At the transmitter, we adopt antenna selection to exploit spatial diversity and adopt frequency selection to exploit frequency diversity. A low-complexity over-the-air limited feedback using an IEEE 802.15.4 RF interface is designed for antenna and frequency selections and reporting from the receiver to the transmitter. The proposed WPT DAS prototype is demonstrated in a real indoor environment. The measurements show that WPT DAS can boost the output dc power by up to 30 dB in single-user case and boost the sum of output dc power by up to 21.8 dB in two-user case and broaden the service coverage area in a low cost, low complexity, and flexible manner.
Temporary changes in large-scale memory neural networks after fear learning and extinction in healthy adults
Date: 2020-10-31 00:00:00
Tasks: Functional Connectivity, Hippocampus

The analysis of the functional connectivity of brain sections associated with
fear-conditioned training is one of the methods of study of memory neural
networks. Before, the majority of studies focused on the functional
connectivity of the brain regions that are known for emotional processing such
as amygdala and areas of the ventromedial prefrontal cortex in the resting
state right after the formation of a fear-conditioned reflex. In the present
study authors applied the methods of the theory of graphs to search for changes
in the functional connectivity at the level of the brain in the resting state
in a week dynamics after the extinction of a conditioned reflex with partial
reinforcement. The most significant changes were observed in the functional
connectivity of the left parahippocampal area. In particular, the rostral part
of the left parahippocampal gyrus became the center of a new subnetwork
connected with the rostral part of the left hippocampus in all the sessions and
after the extinction of a conditioned reflex and the lateral part of the left
amygdala right after the extinction and a day after the extinction of a
conditioned reflex. A week after the extinction of a conditioned reflex, the
rostral part of the left parahippocampal gyrus also had more connections with
the areas of the middle frontal gyrus in comparison with the parameters of the
baseline resting state to the stimulus. Besides, these changes remained within
one week from the moment of the extinction of a conditioned reflex, which could
be explained by the chosen paradigm of conditioned reflex with partial
reinforcement that led to a slower extinction than a paradigm with full
reinforcement.
Defect Detection by MIMO Wireless Sensing based on Weighted Low-Rank plus Sparse Recovery
Authors: Udaya S. K. P. Miriya Thanthrige, Ali Kariminezhad, Peter Jung, Aydin Sezgin
Date: 2020-10-31 00:00:00
Tasks: Compressive Sensing, Defect Detection

We present a compressive sensing based defect detection by multiple input multiple output (MIMO) wireless radar. Here, defects are inside a layered material structure, therefore, due to reflections from the surface of the layered material structure the defect detection is challenging. By utilizing a low-rank nature of the reflections of the layered material structure and sparse nature of the defects, we propose a method based on rank minimization and sparse recovery. To improve the accuracy in the recovery of low-rank and sparse components, we propose a non-convex approach based on the iteratively reweighted nuclear norm and iteratively reweighted $\ell_1-$norm algorithm. Our numerical results show that the proposed method is able to demix and recover the signalling responses of the defects and layered structure successfully from substantially reduced number of observations. Further, the proposed approach outperforms the state-of-the-art clutter reduction approaches
Infectious Disease Transmission via Aerosol Propagation from a Molecular Communication Perspective: Shannon Meets Coronavirus
Authors: Max Schurwanz, Peter Adam Hoeher, Sunasheer Bhattacharjee, Martin Damrath, Lukas Stratmann, Falko Dressler
Date: 2020-10-31 00:00:00

Molecular communication is not only able to mimic biological and chemical communication mechanisms, but also provides a theoretical framework for viral infection processes. In this tutorial, aerosol and droplet transmission is modeled as a multiuser scenario with mobile nodes, related to broadcasting and relaying. In contrast to data communication systems, in the application of pathogen-laden aerosol transmission, mutual information between nodes should be minimized. Towards this goal, several countermeasures are reasoned. The findings are supported by experimental results and by an advanced particle simulation tool. This work is inspired by the recent outbreak of the coronavirus (COVID-19) pandemic, but also applicable to other airborne infectious diseases like influenza.
In-The-Wild Interference Characterization and Modelling for Electro-Quasistatic-HBC with Miniaturized Wearables
Authors: Parikha Mehrotra, David Yang, Scott Weigand, Shreyas Sen
Date: 2020-10-31 00:00:00

The emergence of Human Body Communication (HBC) as an alternative to wireless body area networks (WBAN) has led to the development of small sized, energy efficient and more secure wearable and implantable devices forming a network in and around the body. Previous studies claim that though HBC is comparatively more secure than WBAN, nevertheless, the electromagnetic (EM) radiative nature of HBC in >10MHz region makes the information susceptible to eavesdropping. Furthermore, interferences may be picked up by the body due to the human body antenna effect in the 40-400MHz range. Alternatively, electro-quasistatic (EQS) mode of HBC forms an attractive way for covert data transmission in the sub 10MHz region by allowing the signal to be contained within the body. However, there is a gap in the knowledge about the mechanism and sources of interference in this region (crucial in allowing for proper choice of data transmission band). In this paper, the interference coupling modality in the EQS region is explained along with its possible sources. Interferences seen by the wearable in the actual scenario is a non-trivial problem and a suitable measurement EQS HBC setup is designed to recreate it by employing a wearable sized measurement setup having a small ground plane. For the first time, a human biophysical interference pickup model is proposed and interference measurement results using a wearable device are presented up to 250kHz in different environmental settings.
Generalised geometric Brownian motion: Theory and applications to option pricing
Authors: Viktor Stojkoski, Trifce Sandev, Lasko Basnarkov, Ljupco Kocarev, Ralf Metzler
Date: 2020-10-31 00:00:00

Classical option pricing schemes assume that the value of a financial asset follows a geometric Brownian motion (GBM). However, a growing body of studies suggest that a simple GBM trajectory is not an adequate representation for asset dynamics due to irregularities found when comparing its properties with empirical distributions. As a solution, we develop a generalisation of GBM where the introduction of a memory kernel critically determines the behavior of the stochastic process. We find the general expressions for the moments, log-moments, and the expectation of the periodic log returns, and obtain the corresponding probability density functions by using the subordination approach. Particularly, we consider subdiffusive GBM (sGBM), tempered sGBM, a mix of GBM and sGBM, and a mix of sGBMs. We utilise the resulting generalised GBM (gGBM) to examine the empirical performance of a selected group of kernels in the pricing of European call options. Our results indicate that the performance of a kernel ultimately depends on the maturity of the option and its moneyness.
Presence of Women in Economics Academia: Evidence from India
Authors: Ambrish Dongre, Karan Singhal, Upasak Das
Date: 2020-10-31 00:00:00

This paper documents the representation of women in Economics academia in India by analyzing the share of women in faculty positions, and their participation in a prestigious conference held annually. Data from the elite institutions shows that the presence of women as the Economics faculty members remains low. Of the authors of the papers which were in the final schedule of the prestigious research conference, the proportion of women authors is again found to be disproportionately low. Our findings from further analysis indicate that women are not under-represented at the post-graduate level. Further, the proportion of women in doctoral programmes has increased over time, and is now almost proportionate. Tendency of women who earn a doctorate abroad, to not return to India, time needed to complete a doctoral program, and responsibilities towards the family may explain lower presence of women in Economics academia in India.
Causal Inference for Spatial Treatments
Authors: Michael Pollmann
Date: 2020-10-31 00:00:00
Tasks: Causal Inference, counterfactual

Many events and policies (treatments) occur at specific spatial locations, with researchers interested in their effects on nearby units of interest. I approach the spatial treatment setting from an experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between individuals near realized treatment locations and individuals near counterfactual (unrealized) candidate locations, which differs from current empirical practice. I derive design-based standard errors that are straightforward to compute irrespective of spatial correlations in outcomes. Furthermore, I propose machine learning methods to find counterfactual candidate locations using observational data under unconfounded assignment of the treatment to locations. I apply the proposed methods to study the causal effects of grocery stores on foot traffic to nearby businesses during COVID-19 shelter-in-place policies, finding a substantial positive effect at a very short distance, with no effect at larger distances.