pwc-archive/papers-with-abstracts huggingface dataset:
Artificial neural networks for disease trajectory prediction in the context of sepsis
Date: 2020-07-29 00:00:00
Tasks: Trajectory Prediction

The disease trajectory for clinical sepsis, in terms of temporal cytokine and
phenotypic dynamics, can be interpreted as a random dynamical system. The
ability to make accurate predictions about patient state from clinical
measurements has eluded the biomedical community, primarily due to the paucity
of relevant and high-resolution data. We have utilized two distinct neural
network architectures, Long Short-Term Memory and Multi-Layer Perceptron, to
take a time sequence of five measurements of eleven simulated serum cytokine
concentrations as input and to return both the future cytokine trajectories as
well as an aggregate metric representing the patient's state of health. The
neural networks converged within 50 epochs for cytokine trajectory predictions
and health-metric regressions, with the expected amount of error (due to
stochasticity in the simulation). The mapping from a specific cytokine profile
to a state-of-health is not unique, and increased levels of inflammation result
in less accurate predictions. Due to the propagation of machine learning error
combined with computational model stochasticity over time, the network should
be re-grounded in reality daily as predictions can diverge from the true model
trajectory as the system evolves towards a probabilistic basin of attraction.
This work serves as a proof-of-concept for the use of artificial neural
networks to predict disease progression in sepsis. This work is not intended to
replace a trained clinician, rather the goal is to augment intuition with
quantifiable statistical information to help them make the best decisions. We
note that this relies on a valid computational model of the system in question
as there does not exist sufficient data to inform a machine-learning trained,
artificially intelligent, controller.
On the Transcriptomic Signature and General Stress State Associated with Aneuploidy
Authors: Hung-Ji Tsai, Anjali R. Nelliat, Andrei Kucharavy, Mohammad Ikbal Choudhury, Sean X. Sun, Michael C. Schatz, Rong Li
Date: 2020-07-28 00:00:00
Tasks: valid

Whether aneuploid cells with diverse karyotypes have any properties in common has a been a subject of intense interest. A recent study by Terhorst et al. (1) reinvestigated the common aneuploidy gene expression (CAGE), disputing the conclusion of our recent work (2). In this short article, which has been submitted to PNAS as a Letter to the Editor, we explain our major concerns about Terhorst et al. and why we believe that our previous conclusion stands valid.
DNN No-Reference PSTN Speech Quality Prediction
Date: 2020-07-29 00:00:00
Tasks: Prediction

Classic public switched telephone networks (PSTN) are often a black box for
VoIP network providers, as they have no access to performance indicators, such
as delay or packet loss. Only the degraded output speech signal can be used to
monitor the speech quality of these networks. However, the current
state-of-the-art speech quality models are not reliable enough to be used for
live monitoring. One of the reasons for this is that PSTN distortions can be
unique depending on the provider and country, which makes it difficult to train
a model that generalizes well for different PSTN networks. In this paper, we
present a new open-source PSTN speech quality test set with over 1000
crowdsourced real phone calls. Our proposed no-reference model outperforms the
full-reference POLQA and no-reference P.563 on the validation and test set.
Further, we analyzed the influence of file cropping on the perceived speech
quality and the influence of the number of ratings and training size on the
model accuracy.
Money flow network among firms' accounts in a regional bank of Japan
Authors: Yoshi Fujiwara, Hiroyasu Inoue, Takayuki Yamaguchi, Hideaki Aoyama, Takuma Tanaka
Date: 2020-07-29 00:00:00

In this study, we investigate the flow of money among bank accounts possessed by firms in a region by employing an exhaustive list of all the bank transfers in a regional bank in Japan, to clarify how the network of money flow is related to the economic activities of the firms. The network statistics and structures are examined and shown to be similar to those of a nationwide production network. Specifically, the bowtie analysis indicates what we refer to as a "walnut" structure with core and upstream/downstream components. To quantify the location of an individual account in the network, we used the Hodge decomposition method and found that the Hodge potential of the account has a significant correlation to its position in the bowtie structure as well as to its net flow of incoming and outgoing money and links, namely the net demand/supply of individual accounts. In addition, we used non-negative matrix factorization to identify important factors underlying the entire flow of money; it can be interpreted that these factors are associated with regional economic activities.One factor has a feature whereby the remittance source is localized to the largest city in the region, while the destination is scattered. The other factors correspond to the economic activities specific to different local places.This study serves as a basis for further investigation on the relationship between money flow and economic activities of firms.
Downlink Single-Snapshot Localization and Mapping with a Single-Antenna Receiver
Authors: Alessio Fascista, Angelo Coluccia, Henk Wymeersch, Gonzalo Seco-Granados
Date: 2020-07-29 00:00:00

5G mmWave MIMO systems enable accurate estimation of the user position and mapping of the radio environment using a single snapshot when both the base station (BS) and user are equipped with large antenna arrays. However, massive arrays are initially expected only at the BS side, likely leaving users with one or very few antennas. In this paper, we propose a novel method for single-snapshot localization and mapping in the more challenging case of a user equipped with a single-antenna receiver. The joint maximum likelihood (ML) estimation problem is formulated and its solution formally derived. To avoid the burden of a full-dimensional search over the space of the unknown parameters, we present a novel practical approach that exploits the sparsity of mmWave channels to compute an approximate joint ML estimate. A thorough analysis, including the derivation of the Cram\'er-Rao lower bounds, reveals that accurate localization and mapping can be achieved also in a MISO setup even when the direct line-of-sight path between the BS and the user is severely attenuated.
Digital biomarkers and artificial intelligence for mass diagnosis of atrial fibrillation in a population sample at risk of sleep disordered breathing
Authors: Armand Chocron, Roi Efraim, Franck Mandel, Michael Rueschman, Niclas Palmius, Thomas Penzel, Meyer Elbaz, Joachim A. Behar
Date: 2020-07-29 00:00:00
Tasks: Rhythm, Time Series Analysis

Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with a five-fold increase in stroke risk. Many individuals with AF go undetected. These individuals are often asymptomatic. There are ongoing debates on whether mass screening for AF is to be recommended. However, there is incentive in performing screening for specific at risk groups such as individuals suspected of sleep-disordered breathing where an important association between AF and obstructive sleep apnea (OSA) has been demonstrated. We introduce a new methodology leveraging digital biomarkers and recent advances in artificial intelligence (AI) for the purpose of mass AF diagnosis. We demonstrate the value of such methodology in a large population sample at risk of sleep disordered breathing. Four databases, totaling n=3,088 patients and p=26,913 hours of ECG raw data were used. Three of the databases (n=125, p=2,513) were used for training a machine learning model in recognizing AF events from beat-to-beat interval time series. The visit 1 of the sleep heart health study database (SHHS1, n=2,963, p=24,400) consists of overnight polysomnographic (PSG) recordings, and was considered as the test set. In SHHS1, expert inspection identified a total of 70 patients with a prominent AF rhythm. Model prediction on the SHHS1 showed an overall Se=0.97,Sp=0.99,NPV=0.99,PPV=0.67 in classifying individuals with or without prominent AF. PPV was non-inferior (p=0.03) for individuals with an apnea-hypopnea index (AHI) > 15 versus AHI < 15. Over 22% of correctly identified prominent AF rhythm cases were not documented as AF in the SHHS1. Individuals with prominent AF can be automatically diagnosed from an overnight single channel ECG recording, with an accuracy unaffected by the presence of OSA. AF detection from overnight ECG recording revealed a large proportion of undiagnosed AF and may enhance the phenotyping of OSA.
Editorial: Understanding Cryptocurrencies
Authors: Wolfgang Karl Härdle, Campbell R. Harvey, Raphael C. G. Reule
Date: 2020-07-29 00:00:00

Cryptocurrency refers to a type of digital asset that uses distributed ledger, or blockchain, technology to enable a secure transaction. Although the technology is widely misunderstood, many central banks are considering launching their own national cryptocurrency. In contrast to most data in financial economics, detailed data on the history of every transaction in the cryptocurrency complex are freely available. Furthermore, empirically-oriented research is only now beginning, presenting an extraordinary research opportunity for academia. We provide some insights into the mechanics of cryptocurrencies, describing summary statistics and focusing on potential future research avenues in financial economics.
A 128-point Multi-Path SC FFT Architecture
Authors: Shun-Che Hsu, Shen-Jui Huang, Sau-Gee Chen, Shin-Che Lin, Mario Garrido
Date: 2020-07-29 00:00:00

This paper presents a new radix-2^k multi-path FFT architecture, named MSC FFT, which is based on a single-path radix-2 serial commutator (SC) FFT architecture. The proposed multi-path architecture has a very high hardware utilization that results in a small chip area, while providing high throughput. In addition, the adoption of radix-2^k FFT algorithms allows for simplifying the rotators even further. It is achieved by optimizing the structure of the processing element (PE). The implemented architecture is a 128-point 4-parallel multi-path SC FFT using 90 nm process. Its area and power consumption at 250 MHz are only 0.167 mm2 and 14.81 mW, respectively. Compared with existing works, the proposed design reduces significantly the chip rea and the power consumption, while providing high throughput.
Intelligent Reflecting Surface based Passive Information Transmission: A Symbol-Level Precoding Approach
Authors: Rang Liu, Ming Li, Qian Liu, A. Lee Swindlehurst, Qingqing Wu
Date: 2020-07-29 00:00:00

Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion. While the application of IRS as a passive reflector to enhance the performance of wireless communications has been widely investigated in the literature, using IRS as a passive transmitter recently is emerging as a new concept and attracting steadily growing interest. In this paper, we propose two novel IRS-based passive information transmission systems using advanced symbol-level precoding. One is a standalone passive information transmission system, where the IRS operates as a passive transmitter serving multiple receivers by adjusting its elements to reflect unmodulated carrier signals. The other is a joint passive reflection and information transmission system, where the IRS not only enhances transmissions for multiple primary information receivers (PIRs) by passive reflection, but also simultaneously delivers additional information to a secondary information receiver (SIR) by embedding its information into the primary signals at the symbol level. Two typical optimization problems, i.e., power minimization and quality-of-service (QoS) balancing, are investigated for the proposed IRS-based passive information transmission systems. Simulation results demonstrate the feasibility of IRS-based passive information transmission and the effectiveness of our proposed algorithms, as compared to other benchmark schemes.
Energy-based Modelling of the Feedback Control of Biomolecular Systems with Cyclic Flow Modulation
Authors: Peter J Gawthrop
Date: 2020-07-29 00:00:00

Energy-based modelling brings engineering insight to the understanding of biomolecular systems. It is shown how well-established control engineering concepts, such as loop-gain, arise from energy feedback loops and are therefore amenable to control engineering insight. In particular, a novel method is introduced to allow the transfer function based approach of classical linear control to be utilised in the analysis of feedback systems modelled by network thermodynamics and thus amalgamate energy-based modelling with control systems analysis. The approach is illustrated using a class of metabolic cycles with activation and inhibition leading the concept of Cyclic Flow Modulation.
Who Manipulates Data During Pandemics? Evidence from Newcomb-Benford Law
Date: 2020-07-29 00:00:00

We use the Newcomb-Benford law to test if countries have manipulated reported data during the COVID-19 pandemic. We find that democratic countries, countries with the higher gross domestic product (GDP) per capita, higher healthcare expenditures, and better universal healthcare coverage are less likely to deviate from the Newcomb-Benford law. The relationship holds for the cumulative number of reported deaths and total cases but is more pronounced for the death toll. The findings are robust for second-digit tests, for a sub-sample of countries with regional data, and in relation to the previous swine flu (H1N1) 2009-2010 pandemic. The paper further highlights the importance of independent surveillance data verification projects.
Rethinking Maximum Flow Problem and Beamforming Design through Brain-inspired Geometric Lens
Authors: Ahmed S. Ibrahim
Date: 2020-07-29 00:00:00

Increasing data rate in wireless networks can be accomplished through a two-pronged approach, which are 1) increasing the network flow rate through parallel independent routes and 2) increasing the user's link rate through beamforming codebook adaptation. Mobile relays are utilized to enable achieving these goals given their flexible positioning. First at the network level, we model regularized Laplacian matrices, which are symmetric positive definite (SPD) ones representing relay-dependent network graphs, as points over Riemannian manifolds. Inspired by the geometric classification of different tasks in the brain network, Riemannian metrics, such as Log-Euclidean metric (LEM), are utilized to choose relay positions that result in maximum LEM. Simulation results show that the proposed LEM-based relay positioning algorithm enables parallel routes and achieves maximum network flow rate, as opposed to other metrics (e.g., algebraic connectivity). Second at the link level, we design unique relay-dependent beamforming codebooks aimed to increase data rate over the spatially-correlated fading channels between a given relay and its neighboring users. To do so, we propose a geometric machine learning approach, which utilizes support vector machine (SVM) model to learn an SPD variant of the user's channel over Riemannian manifolds. Consequently, LEM-based Riemannian metric is utilized for classification of different channels, and a matched beamforming codebook is constructed accordingly. Simulation results show that the proposed geometric-based learning model achieves the maximum link rate after a short training period.
Speed-up Heuristic for an On-Demand Ride-Pooling Algorithm
Date: 2020-07-29 00:00:00
Tasks: Autonomous Driving, Management

With ongoing developments in digitalization and advances in the field of
autonomous driving, on-demand ride pooling is a mobility service with the
potential to disrupt the urban mobility market. Nevertheless, to apply this
kind of service successfully efficient algorithms have to be implemented for
effective fleet management to exploit the benefits associated with this
mobility service. Especially real time computation of finding beneficial
assignments is a problem not solved for large problem sizes until today. In
this study, we show the importance of using advanced algorithms by comparing a
fast, but simple insertion heuristic algorithm with a state-of-the-art
multi-step matching algorithm. We test the algorithms in various scenarios
based on private vehicle trip OD-data for Munich, Germany. Results indicate
that in the tested scenarios by using the multi-step algorithm up to 8$\%$
additional requests could be served while also 10$\%$ additional driven
distance could be saved. However, computational time for finding optimal
assignments in the advanced algorithm exceeds real time rather fast as problem
size increases. Therefore, several aspects to reduce the computational time by
decreasing redundant checks of the advanced multi step algorithm are
introduced. Finally, a refined vehicle selection heuristic based on three rules
is presented to furthermore reduce the computational effort. In the tested
scenarios this heuristic can speed up the most cost intensive algorithm step by
a factor of over 8, while keeping the number of served requests almost constant
and maintaining around 70$\%$ of the driven distance saved in the system.
Considering all algorithm steps, an overall speed up of 2.5 could be achieved.
Structural representations of DNA regulatory substrates can enhance sequence-based algorithms by associating functional sequence variants
Authors: Jan Zrimec
Date: 2020-07-29 00:00:00

The nucleotide sequence representation of DNA can be inadequate for resolving protein-DNA binding sites and regulatory substrates, such as those involved in gene expression and horizontal gene transfer. Considering that sequence-like representations are algorithmically very useful, here we fused over 60 currently available DNA physicochemical and conformational variables into compact structural representations that can encode single DNA binding sites to whole regulatory regions. We find that the main structural components reflect key properties of protein-DNA interactions and can be condensed to the amount of information found in a single nucleotide position. The most accurate structural representations compress functional DNA sequence variants by 30% to 50%, as each instance encodes from tens to thousands of sequences. We show that a structural distance function discriminates among groups of DNA substrates more accurately than nucleotide sequence-based metrics. As this opens up a variety of implementation possibilities, we develop and test a distance-based alignment algorithm, demonstrating the potential of using the structural representations to enhance sequence-based algorithms. Due to the bias of most current bioinformatic methods to nucleotide sequence representations, it is possible that considerable performance increases might still be achievable with such solutions.
Healthcare Utilization and Perceived Health Status from Falun Gong Practitioners in Taiwan: A Pilot SF-36 Survey
Authors: Yu-Whuei Hu, Li-Shan Huang, Eric J. Yeh, Mai He
Date: 2020-07-29 00:00:00
Tasks: Survey

Objective: Falun Gong (FLG) is a practice of mind and body focusing on moral character improvement along with meditative exercises. This 2002 pilot study explored perceived health status, medical resource utilization and related factors among Taiwanese FLG practitioners, compared to the general Taiwanese norm estimated by the 2001 National Health Interview Survey (NHIS). Methods: This cross-sectional, observational study was based on a voluntary, paper-based survey conducted from October 2002 to February 2003 using the same Taiwanese SF-36 instrument employed by the NHIS. Primary outcomes included eight SF-36 domain scores and the number of medical visits. One-sample t-tests, one-way ANOVA and multivariate linear regression analyses were performed. Results: The response rate was 75.6% (1,210/1,600). Compared to the norm, the study cohort had significantly higher scores in six of eight SF-36 domains across gender and age (p<0.05). Among those with chronic diseases, 70% to 89% reported their conditions either improved or cured. 74.2%, 79.2%, 83.3%, and 85.6% quitted alcohol drinking, smoking, chewing betel nuts, and gambling. 62.7% reported a reduced number of medical visits (mean=13.53 before; mean=5.87 after). Conclusions: In this subject cohort, practicing FLG led to higher perceived health scores and reduced health resource utilization compared to the norm.
Mesoscopic architecture enhances communication across the Macaque connectome revealing structure-function correspondence in the brain
Date: 2020-07-29 00:00:00

Analyzing the brain in terms of organizational structures at intermediate
scales provides an approach to negotiate the complexity arising from
interactions between its large number of components. Focusing on a wiring
diagram that spans the cortex, basal ganglia and thalamus of the Macaque brain,
we provide a mesoscopic-level description of the topological architecture of
one of the most well-studied mammalian connectomes. The robust modules we
identify each comprise densely inter-connected cortical and sub-cortical areas
that play complementary roles in executing specific cognitive functions. We
find that physical proximity between areas is insufficient to explain the
modular organization, as similar mesoscopic structures can be obtained even
after factoring out the effect of distance constraints on the connectivity. We
observe that the distribution profile of brain areas, classified in terms of
their intra- and inter-modular connectivity, is conserved across the principal
cortical subdivisions, as well as, sub-cortical structures. In particular
provincial hubs, which have significantly higher number of connections with
members of their module, but relatively less well-connected to other modules,
are the only class that exhibits homophily, i.e., a discernible preference to
connect to each other. By considering a process of diffusive propagation we
demonstrate that this architecture, instead of localizing the activity,
facilitates rapid communication across the connectome. By supplementing the
topological information about the Macaque connectome with physical locations,
volumes and functions of the constituent areas and analyzing this augmented
dataset, we reveal a counter-intuitive role played by the modular architecture
of the brain in promoting global interaction.
Antiviral Drug-Membrane Permeability: the Viral Envelope and Cellular Organelles
Authors: Changjiang Liu, Paolo Elvati, Angela Violi
Date: 2020-07-29 00:00:00

To shorten the time required to find effective new drugs, like antivirals, a key parameter to consider is membrane permeability, as a compound intended for an intracellular target with poor permeability will have low efficacy. Here, we present a computational model that considers both drug characteristics and membrane properties for the rapid assessment of drugs permeability through the coronavirus envelope and various cellular membranes. We analyze 79 drugs that are considered as potential candidates for the treatment of SARS-CoV-2 and determine their time of permeation in different organelle membranes grouped by viral baits and mammalian processes. The computational results are correlated with experimental data, present in the literature, on bioavailability of the drugs, showing a negative correlation between fast permeation and most promising drugs. This model represents an important tool capable of evaluating how permeability affects the ability of compounds to reach both intended and unintended intracellular targets in an accurate and rapid way. The method is general and flexible and can be employed for a variety of molecules, from small drugs to nanoparticles, as well to a variety of biological membranes.