pwc-archive/papers-with-abstracts huggingface dataset:
Economics of 100% renewable power systems
Authors: Takuya Hara
Date: 2022-05-30 00:00:00

Studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain poorly understood. Based on a simple but essential model, this study found that the bottleneck formed by the largest mismatch between demand and power generation profiles determines the optimal capacities of generation and storage and their trade-off relationship. Applying microeconomic theory, particularly the duality of quantity and value, this study comprehensively quantified the relationships among the factor cost of technologies, their optimal capacities, and total system cost. Using actual profile data for multiple years/regions in Japan, this study demonstrated that hybrid systems comprising cost-competitive multiple renewable energy sources and different types of storage are critical for the economic feasibility of any profile.
Can I invest in Metaverse? The effect of obtained information and perceived risk on purchase intention by the perspective of the information adoption model
Authors: İbrahim Halil Efendioğlu
Date: 2022-05-30 00:00:00
Tasks: Descriptive

Metaverse is a virtual universe that combines the physical world and the digital world. People can socialize, play games and even shop with their avatars created in this virtual environment. Metaverse, which is growing very fast in terms of investment, is both a profitable and risky area for consumers. In order to enter the Metaverse for investment purposes, it is necessary to do a certain research and gather information. In this direction, the aim of the study is to determine the effect of the quality of the information obtained by the consumers about the metaverse world, the reliability of the information and the perceived risk, on the purchase intention from the point of view of the information adoption model. For the research, data were collected online from 495 consumers who were interested in metaverse investment. AMOS and SPSS package programs were used in the analysis. First, descriptive statistical analyzes were made for the basic structure of the variables. Then the reliability and validity of the model were tested. Finally, the structural equation model was used to test the proposed model. According to the findings, the reliability and quality of the information affect the purchase intention positively and significantly, while the perceived risk affects the purchase intention negatively and significantly.
Asymptotic welfare performance of Boston assignment algorithms
Authors: Geoffrey Pritchard, Mark C. Wilson
Date: 2022-05-30 00:00:00

We make a detailed analysis of three key algorithms (Serial Dictatorship and the naive and adaptive variants of the Boston algorithm) for the housing allocation problem, under the assumption that agent preferences are chosen iid uniformly from linear orders on the items. We compute limiting distributions (with respect to some common utility functions) as $n\to \infty$ of both the utilitarian welfare and the order bias. To do this, we compute limiting distributions of the outcomes for an arbitrary agent whose initial relative position in the tiebreak order is $\theta\in[0,1]$, as a function of $\theta$. The results for the Boston algorithms are all new, and we expect that these fundamental results on the stochastic processes underlying these algorithms will have wider applicability in future. Overall our results show that the differences in utilitarian welfare performance of the three algorithms are fairly small but still important. However, the differences in order bias are much greater. Also, Naive Boston beats Adaptive Boston, which beats Serial Dictatorship, on both utilitarian welfare and order bias.
Conversational Speech Separation: an Evaluation Study for Streaming Applications
Authors: Giovanni Morrone, Samuele Cornell, Enrico Zovato, Alessio Brutti, Stefano Squartini
Date: 2022-05-31 00:00:00
Tasks: Speech Separation

Continuous speech separation (CSS) is a recently proposed framework which aims at separating each speaker from an input mixture signal in a streaming fashion. Hereafter we perform an evaluation study on practical design considerations for a CSS system, addressing important aspects which have been neglected in recent works. In particular, we focus on the trade-off between separation performance, computational requirements and output latency showing how an offline separation algorithm can be used to perform CSS with a desired latency. We carry out an extensive analysis on the choice of CSS processing window size and hop size on sparsely overlapped data. We find out that the best trade-off between computational burden and performance is obtained for a window of 5 s.
PhD Thesis. Computer-Aided Assessment of Tuberculosis with Radiological Imaging: From rule-based methods to Deep Learning
Authors: Pedro M. Gordaliza
Date: 2022-05-31 00:00:00
Tasks: Computed Tomography (CT), Diagnostic

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb.) that produces pulmonary damage due to its airborne nature. This fact facilitates the disease fast-spreading, which, according to the World Health Organization (WHO), in 2021 caused 1.2 million deaths and 9.9 million new cases. Fortunately, X-Ray Computed Tomography (CT) images enable capturing specific manifestations of TB that are undetectable using regular diagnostic tests. However, this procedure is unfeasible to process the thousands of volume images belonging to the different TB animal models and humans required for a suitable (pre-)clinical trial. To achieve suitable results, automatization of different image analysis processes is a must to quantify TB. Thus, in this thesis, we introduce a set of novel methods based on the state of the art Artificial Intelligence (AI) and Computer Vision (CV). Initially, we present an algorithm to assess Pathological Lung Segmentation (PLS). Next, a Gaussian Mixture Model ruled by an Expectation-Maximization (EM) algorithm is employed to automatically. Chapter 3 introduces a model to automate the identification of TB lesions and the characterization of disease progression. Chapter 4 extends the classification of TB lesions. Namely, we introduce a computational model to infer TB manifestations present in each lung lobe of CT scans by employing the associated radiologist reports as ground truth. In Chapter 5, we present a DL model capable of extracting disentangled information from images of different animal models, as well as information of the mechanisms that generate the CT volumes. To sum up, the thesis presents a collection of valuable tools to automate the quantification of pathological lungs. Chapter 6 elaborates on these conclusions.
Analog Compressed Sensing for Sparse Frequency Shift Keying Modulation Schemes
Authors: Kathleen Yang, Diana C. Gonzalez, Yonina C. Eldar, Muriel Medard
Date: 2022-05-31 00:00:00
Tasks: compressed sensing

There is a growing interest in signaling schemes that operate in the wideband regime due to the crowded frequency spectrum. However, a downside of the wideband regime is that obtaining channel state information is costly, and the capacity of previously used modulation schemes such as code division multiple access and orthogonal frequency division multiplexing begins to diverge from the capacity bound without channel state information. Impulsive frequency shift keying and wideband time frequency coding have been shown to perform well in the wideband regime without channel state information, thus avoiding the costs and challenges associated with obtaining channel state information. However, the maximum likelihood receiver is a bank of frequency-selective filters, which is very costly to implement due to the large number of filters. In this work, we aim to simplify the receiver by using an analog compressed sensing receiver with chipping sequences as correlating signals to detect the sparse signals. Our results show that using a compressed sensing receiver allows for the simplification of the analog receiver with the trade off of a slight degradation in recovery performance. For a fixed frequency separation, symbol time, and peak SNR, the performance loss remains the same for a fixed ratio of number of correlating signals to the number of frequencies.
Enabling NLoS LEO Satellite Communications with Reconfigurable Intelligent Surfaces
Authors: Xiaowen Tian, Nuria Gonzalez-Prelcic, Takayuki Shimizu
Date: 2022-05-31 00:00:00

Low Earth Orbit (LEO) satellite communications (SatCom) are considered a promising solution to provide uninterrupted services in cellular networks. Line-of-sight (LoS) links between the LEO satellites and the ground users are, however, easily blocked in urban scenarios. In this paper, we propose to enable LEO SatCom in non-line-of-sight (NLoS) channels, as those corresponding to links to users in urban canyons, with the aid of reconfigurable intelligent surfaces (RISs). First, we derive the near field signal model for the satellite-RIS-user link. Then, we propose two deployments to improve the coverage of a RIS-aided link: down tilting the RIS located on the top of a building, and considering a deployment with RISs located on the top of opposite buildings. Simulation results show the effectiveness of using RISs in LEO SatCom to overcome blockages in urban canyons. Insights about the optimal tilt angle and the coverage extension provided by the deployment of an additional RIS are also provided.
Optimizing the Deployment of Reconfigurable Intelligent Surfaces in MmWave Vehicular Systems
Authors: Xiaowen Tian, Nuria Gonzalez-Prelcic, Robert W. Heath Jr
Date: 2022-05-31 00:00:00
Tasks: 3D geometry

Millimeter wave (MmWave) systems are vulnerable to blockages, which cause signal drop and link outage. One solution is to deploy reconfigurable intelligent surfaces (RISs) to add a strong non-line-of-sight path from the transmitter to receiver. To achieve the best performance, the location of the deployed RIS should be optimized for a given site, considering the distribution of potential users and possible blockers. In this paper, we find the optimal location, height and downtilt of RIS working in a realistic vehicular scenario. Because of the proximity between the RIS and the vehicles, and the large electrical size of the RIS, we consider a 3D geometry including the elevation angle and near-field beamforming. We provide results on RIS configuration in terms of both coverage ratio and area-averaged rate. We find that the optimized RIS improves the average averaged rate fifty percent over the case without a RIS, as well as further improvements in the coverage ratio.
Doppler-Enabled Single-Antenna Localization and Mapping Without Synchronization
Authors: Hui Chen, Fan Jiang, Yu Ge, Hyowon Kim, Henk Wymeersch
Date: 2022-05-30 00:00:00

Radio localization is a key enabler for joint communication and sensing in the fifth/sixth generation (5G/6G) communication systems. With the help of multipath components (MPCs), localization and mapping tasks can be done with a single base station (BS) and single unsynchronized user equipment (UE) if both of them are equipped with an antenna array. However, the antenna array at the UE side increases the hardware and computational cost, preventing localization functionality. In this work, we show that with Doppler estimation and MPCs, localization and mapping tasks can be performed even with a single-antenna mobile UE. Furthermore, we show that the localization and mapping performance will improve and then saturate at a certain level with an increased UE speed. Both theoretical Cram\'er-Rao bound analysis and simulation results show the potential of localization under mobility and the effectiveness of the proposed localization algorithm.
Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective
Authors: Hui Chen, Ahmed Elzanaty, Reza Ghazalian, Musa Furkan Keskin, Riku Jäntti, Henk Wymeersch
Date: 2022-05-30 00:00:00

Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure. {For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation.} However, with increased array size in extra-large MIMO systems and increased bandwidth at upper mmWave bands, the effect of channel spatial non-stationarity (SNS), spherical wave model (SWM), and beam squint effect (BSE) cannot be ignored. In this case, localization performance will be affected when an inaccurate channel model deviating from the true model is adopted. In this work, we employ the MCRB (misspecified Cram\'er-Rao lower bound) to lower bound the localization error using a simplified mismatched model while the observed data is governed by a more complex true model. The simulation results show that among all the model impairments, the SNS has the least contribution, the SWM dominates when the distance is small compared to the array size, and the BSE has a more significant effect when the distance is much larger than the array size.
A novel analysis approach of uniform persistence for a COVID-19 model with quarantine and standard incidence rate
Authors: Songbai Guo, Yuling Xue, Xiliang Li, Zuohuan Zheng
Date: 2022-05-31 00:00:00

A coronavirus disease 2019 (COVID-19) model with quarantine and standard incidence rate is first developed, then a novel analysis approach for finding the ultimate lower bound of COVID-19 infectious individuals is proposed, which means that the COVID-19 pandemic is uniformly persistent if the control reproduction number $\mathcal{R}_{c}>1$. This approach can be applied to other related biomathematical models, and some existing works can be improved by using it. In addition, the COVID-19-free equilibrium $V^0$ is locally asymptotically stable (LAS) if $\mathcal{R}_{c}<1$ and linearly stable if $\mathcal{R}_{c}=1$, respectively; while $V^0$ is unstable if $\mathcal{R}_{c}>1$.
Cone-constrained Monotone Mean-Variance Portfolio Selection Under Diffusion Models
Authors: Yang shen, Bin Zou
Date: 2022-05-31 00:00:00

We consider monotone mean-variance (MMV) portfolio selection problems with a conic convex constraint under diffusion models, and their counterpart problems under mean-variance (MV) preferences. We obtain the precommitted optimal strategies to both problems in closed form and find that they coincide, without and with the presence of the conic constraint. This result generalizes the equivalence between MMV and MV preferences from non-constrained cases to a specific constrained case. A comparison analysis reveals that the orthogonality property under the conic convex set is a key to ensuring the equivalence result.
Associative Learning Mechanism for Drug-Target Interaction Prediction
Authors: Zhiqin Zhu, Zheng Yao, Guanqiu Qi, Neal Mazur, Baisen Cong
Date: 2022-05-24 00:00:00
Tasks: molecular representation, Prediction, Representation Learning, Value prediction

As a necessary process in drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly. Drug-target affinity (DTA), which represents the strength of drug-target interaction (DTI), has played an important role in the DTI prediction task over the past decade. Although deep learning has been applied to DTA-related research, existing solutions ignore fundamental correlations between molecular substructures in molecular representation learning of drug compound molecules/protein targets. Moreover, traditional methods lack the interpretability of the DTA prediction process. This results in missing feature information of intermolecular interactions, thereby affecting prediction performance. Therefore, this paper proposes a DTA prediction method with interactive learning and an autoencoder mechanism. The proposed model enhances the corresponding ability to capture the feature information of a single molecular sequence by the drug/protein molecular representation learning module and supplements the information interaction between molecular sequence pairs by the interactive information learning module. The DTA value prediction module fuses the drug-target pair interaction information to output the predicted value of DTA. Additionally, this paper theoretically proves that the proposed method maximizes evidence lower bound (ELBO) for the joint distribution of the DTA prediction model, which enhances the consistency of the probability distribution between the actual value and the predicted value. The experimental results confirm mutual transformer-drug target affinity (MT-DTA) achieves better performance than other comparative methods.
Network-based community detection of comorbidities and their association with SARS-CoV-2 virus during COVID-19 pathogenesis
Authors: S. Chatterjee, B. S. Sanjeev
Date: 2022-05-31 00:00:00
Tasks: Community Detection

Recent studies emphasized the necessity to identify key (human) biological processes and pathways targeted by the Coronaviridae family of viruses, especially SARS-CoV-2. COVID-19 caused up to 33-55\% death rates in COVID-19 patients with malignant neoplasms and Alzheimer's disease. Given this scenario, we identified biological processes and pathways which are most likely affected by COVID-19. The associations between various diseases and human genes known to interact with viruses from Coronaviridae family were obtained from the IntAct COVID-19 data set annotated with DisGeNET data. We constructed the disease-gene network to identify genes that are involved in various comorbid diseased states. Communities from the disease-gene network through Louvain method were identified and functional enrichment through over-representation analysis methodology was used to discover significant biological processes and pathways shared between COVID-19 and other diseases. The IntAct COVID-19 data set comprised of 828 human genes and 10,473 diseases that together constituted nodes in the disease-gene network. Each of the 70,210 edges connects a human gene with an associated disease. The top 10 genes linked to most number of diseases were VEGFA, BCL2, CTNNB1, ALB, COX2, AGT, HLA-A, HMOX1, FGT2 and COMT. The most vulnerable group of patients thus discovered had comorbid conditions such as carcinomas, malignant neoplasms and Alzheimer's disease. Finally, we identified 37 potentially useful biological processes and pathways for improved therapies.
Multilayer adaptive networks in neuronal processing
Authors: Adrián Hernández, José M. Amigó
Date: 2022-05-30 00:00:00

The connectome is a wiring diagram mapping all the neural connections in the brain. At the cellular level, it provides a map of the neurons and synapses within a part or all of the brain of an organism. In recent years, significant advances have been made in the study of the connectome via network science and graph theory. This analysis is fundamental to understand neurotransmission (fast synaptic transmission) networks. However, neurons use other forms of communication as neuromodulation that, instead of conveying excitation or inhibition, change neuronal and synaptic properties. This additional neuromodulatory layers condition and reconfigure the connectome. In this paper, we propose that multilayer adaptive networks, in which different synaptic and neurochemical layers interact, are the appropriate framework to explain neuronal processing. Then, we describe a simplified multilayer adaptive network model that accounts for these extra-layers of interaction and analyse the emergence of interesting computational capabilities.
On the Role of Spatial Effects in Early Estimates of Disease Infectiousness: A Second Quantization Approach
Authors: Adam Mielke
Date: 2022-05-31 00:00:00
Tasks: Quantization

With the covid-19 pandemic still ongoing and an enormous amount of test data available, the lessons learned over the last two years need to be developed to a point where they can provide understanding for tackling new variants and future diseases. The SIR-model commonly used to model disease spread, predicts exponential initial growth, which helps establish the infectiousness of a disease in the early days of an outbreak. Unfortunately, the exponential growth becomes muddied by spatial, finite-size, and non-equilibrium effects in realistic systems, and robust estimates that may be used in prediction and description are still lacking. I here establish a second quantization framework that allows introduction of arbitrarily complicated spatial behavior, and I show that a simplified version of this model is in good agreement with both the growth of different covid-19 variants in Denmark and analytical results from the theory of branched polymers. Denmark is well-suited for comparison, because the number of tests with variant information in early December 2021 is very high, so the spread of a single variant can be followed. I expect this model to build bridges between the epidemic modeling and solid state communities. The long-term goal of the particular analysis in this paper is to establish priors that allow better early estimates for the infectiousness of a new disease.
Multi-Domain Adaptation in Neural Machine Translation with Dynamic Sampling Strategies
Authors: Minh-Quang Pham, Josep Crego, François Yvon
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Domain Adaptation, Machine Translation, Translation, Unsupervised Domain Adaptation

Building effective Neural Machine Translation models often implies accommodating diverse sets of heterogeneous data so as to optimize performance for the domain(s) of interest. Such multi-source / multi-domain adaptation problems are typically approached through instance selection or reweighting strategies, based on a static assessment of the relevance of training instances with respect to the task at hand. In this paper, we study dynamic data selection strategies that are able to automatically re-evaluate the usefulness of data samples and to evolve a data selection policy in the course of training. Based on the results of multiple experiments, we show that such methods constitute a generic framework to automatically and effectively handle a variety of real-world situations, from multi-source domain adaptation to multi-domain learning and unsupervised domain adaptation.
The use of online translators by students not enrolled in a professional translation program: beyond copying and pasting for a professional use
Authors: Rudy Loock, Sophie Léchauguette, Benjamin Holt
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Management, Translation

In this paper, we discuss a use of machine translation (MT) that has been quite overlooked up to now, namely by students not enrolled in a professional translation program. A number of studies have reported massive use of free online translators (OTs), and it seems important to uncover such users’ abilities and difficulties when using MT output, whether to improve their understanding, writing, or translation skills. We report here a study on students enrolled in a French ‘applied languages program’ (where students study two languages, as well as law, economics, and management). The aim was to uncover how they use OTs, as well as their (in)ability to identify and correct MT errors. Obtained through two online surveys and several tests conducted with students from 2020 to 2022, our results show an unsurprising widespread use of OTs for many different tasks, but also some specific difficulties in identifying MT errors, in particular in relation to target language fluency.