pwc-archive/papers-with-abstracts huggingface dataset:
Semi-Blind and l1 Robust System Identification for Anemia Management
Date: 2020-08-20 00:00:00
Tasks: Blood Cell Count, Management

Chronic diseases such as cancer, diabetes, heart diseases, chronic kidney
disease (CKD) require a drug management system that ensures a stable and robust
output of the patient's condition in response to drug dosage. In the case of
CKD, the patients suffer from the deficiency of red blood cell count and
external human recombinant erythropoietin (EPO) is required to maintain healthy
levels of hemoglobin (Hb). Anemia is a common comorbidity in patients with CKD.
For an efficient and robust anemia management system for CKD patients instead
of traditional population-based approaches, individualized patient-specific
approaches are needed. Hence, individualized system (patient) models for
patient-specific drug-dose responses are required. In this research, system
identification for CKD is performed for individual patients. For
control-oriented system identification, two robust identification techniques
are applied: (1) l1 robust identification considering zero initial conditions
and (2) semi-blind robust system identification considering non-zero initial
conditions. The EPO data of patients are used as the input and Hb data is used
as the output of the system. For this study, individualized patient models are
developed by using patient-specific data. The ARX one-step-ahead prediction
technique is used for model validation at real patient data. The performance of
these two techniques is compared by calculating minimum means square error
(MMSE). By comparison, we show that the semi-blind robust identification
technique gives better results as compared to l1 robust identification.
Positionality-Weighted Aggregation Methods for Cumulative Voting
Authors: Takeshi Kato, Yasuhiro Asa, Misa Owa
Date: 2020-08-20 00:00:00

Respecting minority opinions is vital in solving social problems. However, minority opinions are often ignored in general majority rules. To build consensus on pluralistic values and make social choices that consider minority opinions, we propose aggregation methods that give weighting to the minority's positionality on cardinal cumulative voting. Based on quadratic and linear voting, we formulated three weighted aggregation methods that differ in the ratio of votes to cumulative points and the weighting of the minority to all members, and assuming that the distributions of votes follow normal distributions, we calculated the frequency distributions of the aggregation results. We found that minority opinions are more likely to be reflected proportionately to the average of the distribution in two of the above three methods. This implies that Sen and Gotoh's idea of considering the social position of unfortunate people on ordinal ranking in the welfare economics, was illustrated by weighting the minority's positionality on cardinal voting. In addition, it is possible to visualize the number and positionality of the minority from the analysis of the aggregation results. These results will be useful to promote mutual understanding between the majority and minority by interactively visualizing the contents of the proposed aggregation methods in the consensus-building process. With the further development of information technology, the consensus building based on big data will be necessary. We recommend the use of our proposed aggregation methods to make social choices for pluralistic values such as social, environmental, and economic.
Joint Visual and Wireless Signal Feature based Approach for High-Precision Indoor Localization
Authors: Yu Wang, Guangbing Zhou, Chenlu Xiang, Shunqing Zhang, Shugong Xu
Date: 2020-08-20 00:00:00
Tasks: Image-Based Localization, Indoor Localization

The existing localization systems for indoor applications basically rely on wireless signal. With the massive deployment of low-cost cameras, the visual image based localization become attractive as well. However, in the existing literature, the hybrid visual and wireless approaches simply combine the above schemes in a straight forward manner, and fail to explore the interactions between them. In this paper, we propose a joint visual and wireless signal feature based approach for high-precision indoor localization system. In this joint scheme, WiFi signals are utilized to compute the coarse area with likelihood probability and visual images are used to fine-tune the localization result. Based on the numerical results, we show that the proposed scheme can achieve 0.62m localization accuracy with near real-time running time.
Distributed Stochastic Optimization With Unbounded Subgradients Over Randomly Time-Varying Networks
Authors: Tao Li, Keli Fu, Yan Chen, Xiaozheng Fu, Alexander L. Fradkov
Date: 2020-08-20 00:00:00
Tasks: Stochastic Optimization

Motivated by distributed statistical learning over uncertain communication networks, we study distributed stochastic optimization by networked nodes to cooperatively minimize a sum of convex cost functions. The network is modeled by a sequence of time-varying random digraphs with each node representing a local optimizer and each edge representing a communication link. We consider the distributed subgradient optimization algorithm with noisy measurements of local cost functions' subgradients, additive and multiplicative noises among information exchanging between each pair of nodes. By stochastic Lyapunov method, convex analysis, algebraic graph theory and martingale convergence theory, we prove that if the local subgradient functions grow linearly and the sequence of digraphs is conditionally balanced and uniformly conditionally jointly connected, then proper algorithm step sizes can be designed so that all nodes' states converge to the global optimal solution almost surely.
Assessing the Quality-of-Experience of Adaptive Bitrate Video Streaming
Date: 2020-08-20 00:00:00

The diversity of video delivery pipeline poses a grand challenge to the
evaluation of adaptive bitrate (ABR) streaming algorithms and objective
quality-of-experience (QoE) models. Here we introduce so-far the largest
subject-rated database of its kind, namely WaterlooSQoE-IV, consisting of 1350
adaptive streaming videos created from diverse source contents, video encoders,
network traces, ABR algorithms, and viewing devices. We collect human opinions
for each video with a series of carefully designed subjective experiments.
Subsequent data analysis and testing/comparison of ABR algorithms and QoE
models using the database lead to a series of novel observations and
interesting findings, in terms of the effectiveness of subjective experiment
methodologies, the interactions between user experience and source content,
viewing device and encoder type, the heterogeneities in the bias and preference
of user experiences, the behaviors of ABR algorithms, and the performance of
objective QoE models. Most importantly, our results suggest that a better
objective QoE model, or a better understanding of human perceptual experience
and behaviour, is the most dominating factor in improving the performance of
ABR algorithms, as opposed to advanced optimization frameworks, machine
learning strategies or bandwidth predictors, where a majority of ABR research
has been focused on in the past decade. On the other hand, our performance
evaluation of 11 QoE models shows only a moderate correlation between
state-of-the-art QoE models and subjective ratings, implying rooms for
improvement in both QoE modeling and ABR algorithms. The database is made
publicly available at: \url{https://ece.uwaterloo.ca/~zduanmu/waterloosqoe4/}.
Using Multi-Resolution Feature Maps with Convolutional Neural Networks for Anti-Spoofing in ASV
Date: 2020-08-20 00:00:00
Tasks: Speaker Verification

This paper presents a simple but effective method that uses multi-resolution
feature maps with convolutional neural networks (CNNs) for anti-spoofing in
automatic speaker verification (ASV). The central idea is to alleviate the
problem that the feature maps commonly used in anti-spoofing networks are
insufficient for building discriminative representations of audio segments, as
they are often extracted by a single-length sliding window. Resulting
trade-offs between time and frequency resolutions restrict the information in
single spectrograms. The proposed method improves both frequency resolution and
time resolution by stacking multiple spectrograms that are extracted using
different window lengths. These are fed into a convolutional neural network in
the form of multiple channels, making it possible to extract more information
from input signals while only marginally increasing computational costs. The
efficiency of the proposed method has been conformed on the ASVspoof 2019
database. We show that the use of the proposed multiresolution inputs
consistently outperforms that of score fusion across different CNN
architectures. Moreover, computational cost remains small.
Hemodynamic Bigger Hydrostatic Pressure Instead of Lower Shear Stress Aggravates Atherosclerosis
Authors: Xinggang Wang, Junbo Ge
Date: 2020-08-20 00:00:00

The traditional view is that low shear stress of blood flow accelerates atherosclerosis. How does a low shear stress damage the vessel or aggravate atherosclerosis? It is a puzzling question. Furthermore, shear stress of veins is similar to that of Relatively Vulnerable Zones (RVZ) of arteries, while intimal thickening of veins is not obvious as the arteries. There are many places in arterioles with lower shear stress as the decrease of blood velocity, while the thickening of arterioles is not as obvious as that of the medium-sized arteries. These issues make it unconvincing for us to explain atherogenesis with lower shear stress. According to Bernoulli fluid equation (P+1/2\r{ho}v^2+\r{ho}gh=constant), in a closed pipe full of flowing blood, at the same height, the hydrostatic pressure is inversely proportional to the second power of blood velocity. At any point of per unit mass of fluid micro cluster here, the reduction of 1/2\r{ho}v^2 would be converted into P. Therefore, hydrostatic pressure(P) is inversely proportional to v^2 in a very short distance or in the same transection of the artery. When blood micro cluster flows over a very short distance or the same transection of the artery, previous studies did not consider the conversion between 1/2\r{ho}v^2 and P. Therefore, low shear stress aggravates atherosclerosis is an appearance, and the essence is that these areas with smaller blood velocity have much bigger hydrostatic pressure, which aggravates atherosclerosis. This perfectly explains the predisposing sites of atherosclerosis.
Bilirubin lowering effect and safety of a prototype low cost blue light emitting diode (LED) phototherapy device in the treatment of indirect hyperbilirubinemia among healthy term infants in a tertiary government hospital: a pilot study
Authors: Vanessa Marie V. Calabia, Ma. Lucila M. Perez, Gregory L. Tangonan Paul M. Cabacungan, Ivan B. Culaba, Jeremy E. De Guzman
Date: 2020-08-20 00:00:00

Objective: This pilot study was done to evaluate the capability of a prototype low cost blue light emitting diode (LED) phototherapy device in lowering bilirubin levels among healthy term infants diagnosed with indirect hyperbilirubinemia. Methods: Experimental study on term infants diagnosed with indirect hyperbilirubinemia in Ospital ng Makati from May 2016 to November 2016 who underwent phototherapy using the low cost blue LED phototherapy prototype. Results: After 24 hours of phototherapy under the prototype LED phototherapy unit, 16% of the total patients completed treatment as they were already classified in the low risk zone, and another 36% of patients completed treatment after 48 hours. The total bilirubin significantly decreased from baseline bilirubin levels after 24 hours by 16.5% (p = 0.0001). The mean percentage of change of bilirubin reduced after 48 hours of 29.9% was also significant. The proportion of subjects in the high risk zone during baseline to 24th hour went down significantly from 80% to 28% (p = 0.0003), while comparing baseline to 48th hour, the percentage of high risk zone went down from 80% to 9.5% (p = 0.0001). No subjects were reported to have rebound hyperbilirubinemia after discontinuation of phototherapy treatment under the LED prototype. No patient experienced any complication while on phototherapy treatment. Conclusion: The prototype low cost blue light emitting diode (LED) phototherapy was able to lower total serum bilirubin among healthy term infants with indirect hyperbilirubinemia and was safe to use.
State Observation of LTV Systems with Delayed Measurements: A Parameter Estimation-based Approach
Date: 2020-08-18 00:00:00
Tasks: parameter estimation

In this paper we address the problem of state observation of linear
time-varying systems with delayed measurements, which has attracted the
attention of many researchers|see [7] and references therein. We show that,
adopting the parameter estimationbased approach proposed in [3,4], we can
provide a very simple solution to the problem with reduced prior knowledge.
A Review on Coexistence Issues of Broadband Millimeter-Wave Communications
Authors: Ching-Lun Tai, Derek Wu
Date: 2020-08-20 00:00:00

With higher frequencies and broader spectrum than conventional frequency bands, the millimeter-wave (mmWave) band is suitable for next-generation wireless networks featuring short-distance high-rate communications. As a newcomer, mmWaves are expected to have the backward compatibility with existing services and collaborate with other technologies in order to enhance system performances. Therefore, the coexistence issues become an essential topic for next-generation wireless communications. In this paper, we systematically review the coexistence issues of broadband mmWave communications and their corresponding solutions proposed in the literature, helping shed light on the insights of the mmWave design. Particularly, the works surveyed in this paper can be classified into four categories: coexistence with microwave communications, coexistence with fixed services, coexistence with non-orthogonal multiple access (NOMA), and other coexistence issues. Results of numerical evaluations inspired by the literature are presented for a deeper analysis. We also point out some challenges and future directions for each category as a roadmap to further investigate the coexistence issues of broadband mmWave communications.
Signal Separation Using a Mathematical Model of Physiological Signals for the Measurement of Heart Pulse Wave Propagation With Array Radar
Authors: Takuya Sakamoto
Date: 2020-08-20 00:00:00

The arterial pulse wave, which propagates along the artery, is an important indicator of various cardiovascular diseases. By measuring the displacement at multiple parts of the human body, pulse wave velocity can be estimated from the pulse transit time. This paper proposes a technique for signal separation using an antenna array, so that pulse wave propagation can be measured in a non-contact manner. The body displacements due to the pulse wave at different body parts are highly correlated, and cannot be accurately separated using techniques that assume independent or uncorrelated signals. The proposed method formulates the signal separation as an optimization problem, based on a mathematical model of the arterial pulse wave. The objective function in the optimization comprises four terms that are derived based on a small-displacement approximation, unimodal impulse response approximation, and a causality condition. The optimization process was implemented using a genetic algorithm. The effectiveness of the proposed method is demonstrated through numerical simulations and experiments.
Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions
Authors: Gioele Zardini, Nicolas Lanzetti, Andrea Censi, Emilio Frazzoli, Marco Pavone
Date: 2020-08-19 00:00:00
Tasks: Autonomous Vehicles, Management

The design of future mobility solutions and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management policies. This requires tools to study such a coupling and co-design mobility systems in terms of different objectives. We present a framework to address such co-design problems, leveraging a mathematical theory of co-design to frame and solve the problem of designing and deploying an intermodal mobility system, whereby autonomous vehicles service travel demands jointly with micromobility solutions and public transit, in terms of fleets sizing, vehicle characteristics, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design as the interconnection of simple components and to tackle it from a systemic perspective. Moreover, it requires general monotonicity assumptions and naturally handles multiple objectives, delivering rational, actionable solutions for policy makers. We showcase our methodology in a case study of Washington D.C., USA. Our work suggests the possibility to create user-friendly optimization tools to systematically assess costs and benefits of interventions, and to inform policy-making in the future.
The Vigilant Eating Rule: A General Approach for Probabilistic Economic Design with Constraints
Authors: Haris Aziz, Florian Brandl
Date: 2020-08-20 00:00:00

We consider the problem of probabilistic allocation of objects under ordinal preferences. We devise an allocation mechanism, called the vigilant eating rule (VER), that applies to nearly arbitrary feasibility constraints. It is constrained ordinally efficient, can be computed efficiently for a large class of constraints, and treats agents equally if they have the same preferences and are subject to the same constraints. When the set of feasible allocations is convex, we also present a characterization of our rule based on ordinal egalitarianism. Our results about VER do not just apply to allocation problems but to all collective choice problems in which agents have ordinal preferences over discrete outcomes. As a case study, we assume objects have priorities for agents and apply VER to sets of probabilistic allocations that are constrained by stability. VER coincides with the (extended) probabilistic serial rule when priorities are flat and the agent proposing deterministic deferred acceptance algorithm when preferences and priorities are strict. While VER always returns a stable and constrained efficient allocation, it fails to be strategyproof, unconstrained efficient, and envy-free. We show, however, that each of these three properties is incompatible with stability and constrained efficiency.
Why we should use Topological Data Analysis in Ageing: towards defining the "Topological shape of ageing"
Authors: Tamàs Fülöp, Mathieu Desroches, Fernando Antônio Nóbrega Santos, Serafim Rodrigues
Date: 2020-08-14 00:00:00
Tasks: Topological Data Analysis

Living systems are subject to the arrow of time; from birth, they undergo complex transformations (self-organization) in a constant battle for survival, but inevitably ageing and disease trap them to death. Can ageing be understood and eventually reversed? What tools can be employed to further our understanding of ageing? The present article is an invitation for biologists and clinicians to consider key conceptual ideas and computational tools (known to mathematicians and physicists), which potentially may help dissect some of the underlying processes of ageing and disease. Specifically, we first discuss how to classify and analyze complex systems, as well as highlight critical theoretical difficulties that make complex systems hard to study. Subsequently, we introduce Topological Data Analysis - a novel Big Data tool - which may help in the study of complex systems since it extracts knowledge from data in a holistic approach via topological considerations. These conceptual ideas and tools are discussed in a rather informal way to pave future discussions and collaborations between mathematicians and biologists studying ageing.
Modelling multi-period carbon markets using singular forward backward SDEs
Authors: Chassagneux Jean-Francois, Chotai Hinesh, Crisan Dan
Date: 2020-08-20 00:00:00

We introduce a model for the evolution of emissions and the price of emissions allowances in a carbon market such as the European Union Emissions Trading System (EU ETS). The model accounts for multiple trading periods, or phases, with multiple times at which compliance can occur. At the end of each trading period, the participating firms must surrender allowances for their emissions made during that period, and additional allowances can be used for compliance in the following periods. We show that the multi-period allowance pricing problem is well-posed for various mechanisms (such as banking, borrowing and withdrawal of allowances) linking the trading periods. The results are based on the analysis of a forward-backward stochastic differential equation with coupled forward and backward components, a discontinuous terminal condition and a forward component that is degenerate. We also introduce an infinite period model, for a carbon market with a sequence of compliance times and with no end date. We show that, under appropriate conditions, the value function for the multi-period pricing problem converges, as the number of periods increases, to a value function for this infinite period model, and that such functions are unique.
Analytic Calibration in Andreasen-Huge SABR Model
Authors: K. E. Feldman
Date: 2020-08-20 00:00:00
Tasks: model

We derive analytic formulae which link $\alpha$, $\nu$ and $\rho$ parameters in Andreasen-Huge style SABR model to the ATM price and option prices at four strikes close to ATM. Based on these formulae we give a characterisation for the SABR parameters in terms of derivatives of the swap rate forward probability density function. We test the analytic result in the application to the interest rate futures option market.
Modeling the effects of prosocial awareness on COVID-19 dynamics: A case study on Colombia
Date: 2020-08-30 00:00:00

The ongoing COVID-19 pandemic has affected most of the countries on Earth. It
has become a pandemic outbreak with more than 24 million confirmed infections
and above 840 thousand deaths worldwide. In this study, we consider a
mathematical model on COVID-19 transmission with the prosocial awareness
effect. The proposed model can have four equilibrium states based on different
parametric conditions. The local and global stability conditions for awareness
free, disease-free equilibrium is studied. Using Lyapunov function theory and
LaSalle Invariance Principle, the disease-free equilibrium is shown globally
asymptotically stable under some parametric constraints. The existence of
unique awareness free, endemic equilibrium and unique endemic equilibrium is
presented. We calibrate our proposed model parameters to fit daily cases and
deaths from Colombia. Sensitivity analysis indicates that the transmission rate
and learning factor related to awareness of susceptibles are very crucial for
reduction in disease related deaths. Finally, we assess the impact of prosocial
awareness during the outbreak and compare this strategy with popular control
measures. Results indicate that prosocial awareness has competitive potential
to flatten the curve.