pwc-archive/papers-with-abstracts huggingface dataset:
Simple Thermal Noise Estimation of Switched Capacitor Circuits Based on OTAs -- Part II: SC Filters
Date: 2019-08-21 00:00:00
Tasks: Noise Estimation

In Part I of this paper, we have shown how to calculate the thermal noise
voltage variances in switched-capacitor (SC) circuits using operational
transconductance amplifiers (OTAs) with capacitive feedback by using the
extended Bode theorem. The method allows a precise estimation of the thermal
noise voltage variances by simple circuit inspection without the calculation of
any transfer functions nor integrals. While Part I focuses on SC amplifiers and
track&hold circuits, Part II shows how to use the extended Bode theorem for SC
filters. It validates the method on the basic integrator and then on a
first-order low-pass filter by comparing the analytical results to transient
noise simulations showing an excellent match.
Statistical characterization of scattering delay in synthetic aperture radar imaging
Date: 2019-09-22 00:00:00

Distinguishing between the instantaneous and delayed scatterers in synthetic
aperture radar (SAR) images is important for target identification and
characterization. To perform this task, one can use the autocorrelation
analysis of coordinate-delay images. However, due to the range-delay ambiguity
the difference in the correlation properties between the instantaneous and
delayed targets may be small. Moreover, the reliability of discrimination is
affected by speckle, which is ubiquitous in SAR images, and requires
statistical treatment.
  Previously, we have developed a maximum likelihood based approach for
discriminating between the instantaneous and delayed targets in SAR images. To
test it, we employed simple statistical models. They allowed us to simulate
ensembles of images that depend on various parameters, including aperture width
and target contrast.
  In the current paper, we enhance our previously developed methodology by
establishing confidence levels for the discrimination between the instantaneous
and delayed scatterers. Our procedure takes into account the difference in
thresholds for different target contrasts without making any assumptions about
the statistics of those contrasts.
Forecasting e-scooter substitution of direct and access trips by mode and distance
Authors: Mina Lee, Joseph Y. J. Chow, Gyugeun Yoon, Brian Yueshuai He
Date: 2019-08-21 00:00:00

An e-scooter trip model is estimated from four U.S. cities: Portland, Austin, Chicago and New York City. A log-log regression model is estimated for e-scooter trips based on user age, population, land area, and the number of scooters. The model predicts 75K daily e-scooter trips in Manhattan for a deployment of 2000 scooters, which translates to 77 million USD in annual revenue. We propose a novel nonlinear, multifactor model to break down the number of daily trips by the alternative modes of transportation that they would likely substitute based on statistical similarity. The model parameters reveal a relationship with direct trips of bike, walk, carpool, automobile and taxi as well as access/egress trips with public transit in Manhattan. Our model estimates that e-scooters could replace 32% of carpool; 13% of bike; and 7.2% of taxi trips. The distance structure of revenue from access/egress trips is found to differ from that of other substituted trips.
Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork
Authors: Qi Li, Tijana Milenković
Date: 2019-08-21 00:00:00
Tasks: Data Integration, Human Aging

Background. Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes' protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. Results. In a systematic and comprehensive evaluation, we find that in many of the evaluation tests: (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. Conclusion. These results justify the need for our framework.
Optimal step-size of least mean absolute fourth algorithm in low SNR
Date: 2019-08-22 00:00:00

There is a need to improve the capability of the adaptive filtering algorithm
against Gaussian or multiple types of non-Gaussian noises, time-varying system,
and systems with low SNR. In this paper, we propose an optimized least mean
absolute fourth (OPLMF) algorithm, especially for a time-varying unknown system
with low signal-noise-rate (SNR). The optimal step-size of OPLMF is obtained by
minimizing the mean-square deviation (MSD) at any given moment in time. In
addition, the mean convergence and steady-state error of OPLMF are derived.
Also the theoretical computational complexity of OPLMF is analyzed.
Furthermore, the simulation experiment results of system identification are
used to illustrate the principle and efficiency of the OPLMF algorithm. The
performance of the algorithm is analyzed mathematically and validated
experimentally. Simulation results demonstrate that the proposed OPLMF is
superior to the normalized LMF (NLMF) and variable step-size of LMF using
quotient form (VSSLMFQ) algorithms.
Dynamic Weight Importance Sampling for Low Cost Spatiotemporal Sensing
Date: 2019-08-22 00:00:00

A simple and low cost dynamic weight importance sampling (DWIS)
implementation is presented and discussed for spatiotemporal sensing of unknown
correlated signals in sensor field. The spatial signal is compressed into its
contour lines and a partitioned subset of sensors that their observations are
in a given margin of the contour levels, is used for importance sampling. The
selected sensor population is changed dynamically to maintain the low cost and
acceptable spatial signal estimation from limited observations. The estimation
performance, cost and convergence of the proposed approach is evaluated for
spatial and temporal monitoring, using three different contour level definition
schemes. The results show that using DWIS and modeling the spatial signal with
contour lines is low cost. In this study the presence of noise in sensor
observations is ignored. The number of participant sensors is taken as modeling
cost.
Implementing result-based agri-environmental payments by means of modelling
Date: 2019-08-22 00:00:00
Tasks: Management

From a theoretical point of view, result-based agri-environmental payments
are clearly preferable to action-based payments. However, they suffer from two
major practical disadvantages: costs of measuring the results and payment
uncertainty for the participating farmers. In this paper, we propose an
alternative design to overcome these two disadvantages by means of modelling
(instead of measuring) the results. We describe the concept of model-informed
result-based agri-environmental payments (MIRBAP), including a hypothetical
example of payments for the protection and enhancement of soil functions. We
offer a comprehensive discussion of the relative advantages and disadvantages
of MIRBAP, showing that it not only unites most of the advantages of
result-based and action-based schemes, but also adds two new advantages: the
potential to address trade-offs among multiple policy objectives and management
for long-term environmental effects. We argue that MIRBAP would be a valuable
addition to the agri-environmental policy toolbox and a reflection of recent
advancements in agri-environmental modelling.
Robustly segmenting quadriceps muscles of ultra-endurance athletes with weakly supervised U-Net
Date: 2019-08-22 00:00:00
Tasks: Segmentation

In this study, segmentation of quadriceps muscle heads of ultra-endurance
athletes was done using a multi-atlas segmentation and corrective leaning
framework where the registration based multi-atlas segmentation step was
replaced with weakly supervised U-Net. For the case with remarkably different
morphology, our method produced improved accuracy, while reduced significantly
the computation time.
Modelling physical limits of migration by a kinetic model with non-local sensing
Authors: Nadia Loy, Luigi Preziosi
Date: 2019-08-22 00:00:00
Tasks: Position

Migrating cells choose their preferential direction of motion in response to different signals and stimuli sensed by spanning their external environment. However, the presence of dense fibrous regions, lack of proper substrate, and cell overcrowding may hamper cells from moving in certain directions or even from sensing beyond regions that practically act like physical barriers. We extend the non-local kinetic model proposed by Loy and Preziosi (2019) to include situations in which the sensing radius is not constant, but depends on position, sensing direction and time as cells' behavior might be determined on the basis of information collected before reaching physically limiting configurations. We analyze how the actual possible sensing of the environment influences the dynamics by recovering the appropriate macroscopic limits and by integrating numerically the kinetic transport equation.
Sequential Rib Labeling and Segmentation in Chest X-Ray using Mask R-CNN
Date: 2019-08-22 00:00:00
Tasks: Segmentation

Mask R-CNN is a state-of-the-art network architecture for the detection and
segmentation of object instances in the computer vision domain. In this
contribution, it is used to localize, label and segment individual ribs in
anterior-posterior chest X-ray images. For this purpose, several extensions
have been made to the original architecture, in order to address the specific
challenges of this application. This includes the use of rib specific networks,
facilitating dedicated anchor boxes sampled from a training set, as well as a
sequential processing of all ribs. Here, the segmentation result of the upper
neighbor rib is used as additional input to the network. This approach is the
first addressing both rib segmentation and anatomical labeling in chest
radiographs. The results are comparable or even better than existing methods
aiming only at segmentation.
Quantitative portfolio selection: using density forecasting to find consistent portfolios
Authors: N. Meade, J. E. Beasley, C. J. Adcock
Date: 2019-08-22 00:00:00

In the knowledge that the ex-post performance of Markowitz efficient portfolios is inferior to that implied ex-ante, we make two contributions to the portfolio selection literature. Firstly, we propose a methodology to identify the region of risk-expected return space where ex-post performance matches ex-ante estimates. Secondly, we extend ex-post efficient set mathematics to overcome the biases in the estimation of the ex-ante efficient frontier. A density forecasting approach is used to measure the accuracy of ex-ante estimates using the Berkowitz statistic, we develop this statistic to increase its sensitivity to changes in the data generating process. The area of risk-expected return space where the density forecasts are accurate, where ex-post performance matches ex-ante estimates, is termed the consistency region. Under the 'laboratory' conditions of a simulated multivariate normal data set, we compute the consistency region and the estimated ex-post frontier. Over different sample sizes used for estimation, the behaviour of the consistency region is shown to be both intuitively reasonable and to enclose the estimated ex-post frontier. Using actual data from the constituents of the US Dow Jones 30 index, we show that the size of the consistency region is time dependent and, in volatile conditions, may disappear. Using our development of the Berkowitz statistic, we demonstrate the superior performance of an investment strategy based on consistent rather than efficient portfolios.
Coexistence under hierarchical resource exploitation: the role of R*-preemption tradeoff
Authors: Man Qi, Niv DeMalach, Tao Sun, Hailin Zhang
Date: 2019-08-22 00:00:00

Resource competition theory predicts coexistence and exclusion patterns based on species R*s, the minimum resource values required for a species to persist. A central assumption of the theory is that all species have equal access to resources. However, many systems are characterized by preemption exploitation, where some species deplete resources before their competitors can access them (e.g., asymmetric light competition, contest competition among animals). We hypothesize that coexistence under preemption requires an R*-preemption tradeoff, i.e., the species with the priority access should have a higher R* (lower efficiency). Thus, we developed an extension of resource competition theory to investigate partial and total preemption (in the latter, the preemptor is unaffected by species with lower preemption rank). We found that an R*-preemption tradeoff is a necessary condition for coexistence in all models. Moreover, under total preemption, the tradeoff alone is sufficient for coexistence. In contrast, under partial preemption, more conditions are needed, which restricts the parameter space of coexistence. Finally, we discussed the implications of our finding for seemingly distinct tradeoffs, which we view as special cases of R*-preemption tradeoff. These tradeoffs include the digger-grazer, the competition-colonization, and tradeoffs related to light competition between trees and understories.
WiFi Motion Detection: A Study into Efficacy and Classification
Date: 2019-08-20 00:00:00
Tasks: Anomaly Detection, Classification, Motion Detection

WiFi and security pose both an issue and act as a growing presence in
everyday life. Today's motions detection implementations are severely lacking
in the areas of secrecy, scope, and cost. To combat this problem, we aim to
develop a motion detection system that utilizes WiFi Channel State Information
(CSI), which describes how a wireless signal propagates from the transmitter to
the receiver. The goal of this study is to develop a real-time motion detection
and classification system that is discreet, cost-effective, and easily
implementable. The system would only require an Ubuntu laptop with an Intel
Ultimate N WiFi Link 5300 and a standard router. The system will be developed
in two parts: (1) a robust system to track CSI variations in real-time, and (2)
an algorithm to classify the motion. The system used to track CSI variance in
real-time was completed in August 2018. Initial results show that introduction
of motion to a previously motionless area is detected with high confidence. We
present the development of (1) anomaly detection, utilizing the moving average
filter implemented in the initial program and/or unsupervised machine learning,
and (2) supervised machine learning algorithms to classify a set of simple
motions using a proposed feature extraction methods. Lastly, classification
methods such as Decision Tree, Naive Bayes, and Long Short-Term Memory can be
used to classify basic actions regardless of speed, location, or orientation.
A mathematical model describing the localization and spread of influenza A virus infection within the human respiratory tract
Authors: Christian Quirouette, Nada P. Younis, Micaela B. Reddy, Catherine A. A. Beauchemin
Date: 2019-08-22 00:00:00

Within the human respiratory tract (HRT), viruses diffuse through the periciliary fluid (PCF) bathing the epithelium, and travel upwards via advection towards the nose and mouth, as the mucus escalator entrains the PCF. While many mathematical models (MMs) to date have described the course of influenza A virus (IAV) infections in vivo, none have considered the impact of both diffusion and advection on the kinetics and localization of the infection. The MM herein represents the HRT as a one-dimensional track extending from the nose down to a depth of 30 cm, wherein stationary cells interact with the concentration of IAV which move along within the PCF. When IAV advection and diffusion are both considered, the former is found to dominate infection kinetics, and a 10-fold increase in the virus production rate is required to counter its effects. The MM predicts that advection prevents infection from disseminating below the depth at which virus first deposits. Because virus is entrained upwards, the upper HRT sees the most virus, whereas the lower HRT sees far less. As such, infection peaks and resolves faster in the upper than in the lower HRT, making it appear as though infection progresses from the upper towards the lower HRT. When the spatial MM is expanded to include cellular regeneration and an immune response, it can capture the time course of infection with a seasonal and an avian IAV strain by shifting parameters in a manner consistent with what is expected to differ between these two types of infection. The impact of antiviral therapy with neuraminidase inhibitors was also investigated. This new MM offers a convenient and unique platform from which to study the localization and spread of respiratory viral infections within the HRT.
A phylogenetic approach disentangles interlocus gene conversion tract length and initiation rate
Date: 2019-08-22 00:00:00

Interlocus gene conversion (IGC) homogenizes paralogs. Little is known
regarding the mutation events that cause IGC and even less is known about the
IGC mutations that experience fixation. To disentangle the rates of fixed IGC
mutations from the tract lengths of these fixed mutations, we employ a
composite likelihood procedure. We characterize the procedure with simulations.
We apply the procedure to duplicated primate introns and to protein-coding
paralogs from both yeast and primates. Our estimates from protein-coding data
concerning the mean length of fixed IGC tracts were unexpectedly low and are
associated with high degrees of uncertainty. In contrast, our estimates from
the primate intron data had lengths in the general range expected from IGC
mutation studies. While it is challenging to separate the rate at which fixed
IGC mutations initiate from the average number of nucleotide positions that
these IGC events affect, all of our analyses indicate that IGC is responsible
for a substantial proportion of evolutionary change in duplicated regions. Our
results suggest that IGC should be considered whenever the evolution of
multigene families is examined.
A Novel Synchronous Reference Frame Frequency-Locked Loop
Date: 2019-09-26 00:00:00

This letter proposes a new design of frequency-locked loop (FLL) which is
based on synchronous (dq) reference frame instead of stationary
({\alpha}\b{eta}) reference frame. First, a synchronous reference frame FLL
(briefly called SRF-FLL0) equivalent to the conventional FLL is proposed. Then
the SRF-FLL0 is improved by utilizing the phase error to acquire a better
performance. The small-signal modeling and parameter tuning of the improved
synchronous reference frame FLL (SRF-FLL) are presented. Finally, the
theoretical analysis and experiment results verify the superiority and
effectiveness of proposed SRF-FLL.
A nonlinear optimisation model for constructing minimal drawdown portfolios
Authors: C. A. Valle, J. E. Beasley
Date: 2019-08-23 00:00:00

In this paper we consider the problem of minimising drawdown in a portfolio of financial assets. Here drawdown represents the relative opportunity cost of the single best missed trading opportunity over a specified time period. We formulate the problem (minimising average drawdown, maximum drawdown, or a weighted combination of the two) as a nonlinear program and show how it can be partially linearised by replacing one of the nonlinear constraints by equivalent linear constraints. Computational results are presented (generated using the nonlinear solver SCIP) for three test instances drawn from the EURO STOXX 50, the FTSE 100 and the S&P 500 with daily price data over the period 2010-2016. We present results for long-only drawdown portfolios as well as results for portfolios with both long and short positions. These indicate that (on average) our minimal drawdown portfolios dominate the market indices in terms of return, Sharpe ratio, maximum drawdown and average drawdown over the (approximately 1800 trading day) out-of-sample period.
On Energy Harvesting of Hybrid TDMA-NOMA Systems
Date: 2019-08-23 00:00:00

In this paper, we investigate energy harvesting capabilities of
non-orthogonal multiple access (NOMA) scheme integrated with the conventional
time division multiple access (TDMA) scheme, which is referred to as hybrid
TDMA-NOMA system. In a such hybrid scheme, users are divided into a number of
groups, with the total time allocated for transmission is shared between these
groups through multiple time slots. In particular, a time slot is assigned to
serve each group, whereas the users in the corresponding group are served based
on power-domain NOMA technique. Furthermore, simultaneous wireless power and
information transfer technique is utilized to simultaneously harvest energy and
decode information at each user. Therefore, each user splits the received
signal into two parts, namely, energy harvesting part and information decoding
part. In particular, we jointly determine the power allocation and power
splitting ratios for all users to minimize the transmit power under minimum
rate and minimum energy harvesting requirements at each user. Furthermore, this
joint design is a non-convex problem in nature. Hence, we employ successive
interference cancellation to overcome these non-convexity issues and determine
the design parameters (i.e., the power allocations and the power splitting
ratios). In simulation results, we demonstrate the performance of the proposed
hybrid TDMA-NOMA design and show that it outperforms the conventional TDMA
scheme in terms of transmit power consumption.
Heterogeneous Earnings Effects of the Job Corps by Gender Earnings: A Translated Quantile Approach
Date: 2019-08-23 00:00:00

Several studies of the Job Corps tend to nd more positive earnings effects
for males than for females. This effect heterogeneity favouring males contrasts
with the results of the majority of other training programmes' evaluations.
Applying the translated quantile approach of Bitler, Hoynes, and Domina (2014),
I investigate a potential mechanism behind the surprising findings for the Job
Corps. My results provide suggestive evidence that the effect of heterogeneity
by gender operates through existing gender earnings inequality rather than Job
Corps trainability differences.
Reconfigurable Intelligent Surfaces vs. Relaying: Differences, Similarities, and Performance Comparison
Date: 2020-02-21 00:00:00

Reconfigurable intelligent surfaces (RISs) have the potential of realizing
the emerging concept of smart radio environments by leveraging the unique
properties of meta-surfaces. In this article, we discuss the potential
applications of RISs in wireless networks that operate at high-frequency bands,
e.g., millimeter wave (30-100 GHz) and sub-millimeter wave (greater than 100
GHz) frequencies. When used in wireless networks, RISs may operate in a manner
similar to relays. This paper elaborates on the key differences and
similarities between RISs that are configured to operate as anomalous
reflectors and relays. In particular, we illustrate numerical results that
highlight the spectral efficiency gains of RISs when their size is sufficiently
large as compared with the wavelength of the radio waves. In addition, we
discuss key open issues that need to be addressed for unlocking the potential
benefits of RISs.