pwc-archive/papers-with-abstracts huggingface dataset:
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB
Authors: Maytus Piriyajitakonkij, Patchanon Warin, Payongkit Lakhan, Pitsharponrn Leelaarporn, Theerasarn Pianpanit, Nakorn Kumchaiseemak, Supasorn Suwajanakorn, Nattee Niparnan, Subhas Chandra Mukhopadhyay, Theerawit Wilaiprasitporn
Date: 2020-05-02 00:00:00
Tasks: Activity Recognition, Data Augmentation, Human Activity Recognition, MULTI-VIEW LEARNING, Time Series, Time Series Analysis

Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. The data recorded from 38 volunteers displayed that SPN with a mean accuracy of $73.7 \pm 0.8 \%$ significantly outperformed the mean accuracy of $59.9 \pm 0.7 \%$ obtained from deep convolution neural network (DCNN) in recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications.
Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease
Authors: Alok Joshi, Da-Hui Wang, Steven Watterson, Paula L. McClean, Chandan K. Behera, Trevor Sharp, KongFatt Wong-Lin
Date: 2020-05-05 00:00:00
Tasks: HTR

Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (A$\beta$) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of A$\beta$ and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets.
Low Complexity Iterative Rake Decision Feedback Equalizer for Zero-Padded OTFS systems
Authors: Tharaj Thaj, Emanuele Viterbo
Date: 2020-05-02 00:00:00

This paper presents a linear complexity iterative rake detector for the recently proposed orthogonal time frequency space (OTFS) modulation scheme. The basic idea is to extract and coherently combine the received multipath components of the transmitted symbols in the delay-Doppler grid using maximal ratio combining (MRC) to improve the SNR of the combined signal. We reformulate the OTFS input-output relation in simple vector form by placing guard null symbols or zero padding (ZP) in the delay-Doppler grid and exploiting the resulting circulant property of the blocks of the channel matrix. Using this vector input-output relation we propose a low complexity iterative decision feedback equalizer (DFE) based on MRC. The performance and complexity of the proposed detector favorably compares with the state of the art message passing detector. An alternative time domain MRC based detector is also proposed for even faster detection. We further propose a Gauss-Seidel based over-relaxation parameter in the rake detector to improve the performance and the convergence speed of the iterative detection. We also show how the MRC detector can be combined with outer error-correcting codes to operate as a turbo DFE scheme to further improve the error performance. All results are compared with a baseline orthogonal frequency division multiplexing (OFDM) scheme employing a single tap minimum mean square error (MMSE) equalizer.
How to manage the post pandemic opening? A Pontryagin Maximum Principle approach
Date: 2020-06-24 00:00:00

The COVID-19 pandemic has completely disrupted the operation of our
societies. Its elusive transmission process, characterized by an unusually long
incubation period, as well as a high contagion capacity, has forced many
countries to take quarantine and social isolation measures that conspire
against the performance of national economies. This situation confronts
decision makers in different countries with the alternative of reopening the
economies, thus facing the unpredictable cost of a rebound of the infection.
This work tries to offer an initial theoretical framework to handle this
alternative.
Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City
Date: 2020-05-15 00:00:00

Epidemiological models contain a set of parameters that must be adjusted
based on available observations. Once a model has been calibrated, it can be
used as a forecasting tool to make predictions and to evaluate contingency
plans. It is customary to employ only point estimators for such predictions.
However, some models may fit the same data reasonably well for a broad range of
parameter values, and this flexibility means that predictions stemming from
such models will vary widely, depending on the particular parameter values
employed within the range that give a good fit. When data are poor or
incomplete, model uncertainty widens further. A way to circumvent this problem
is to use Bayesian statistics to incorporate observations and use the full
range of parameter estimates contained in the posterior distribution to adjust
for uncertainties in model predictions. Specifically, given the epidemiological
model and a probability distribution for observations, we use the posterior
distribution of model parameters to generate all possible epidemiological
curves via the posterior predictive distribution. From the envelope of all
curves one can extract the worst-case scenario and study the impact of
implementing contingency plans according to this assessment. We apply this
approach to the potential evolution of COVID-19 in Mexico City and assess
whether contingency plans are being successful and whether the epidemiological
curve has flattened.
Analysis and Prediction of COVID-19 Pandemic in Pakistan using Time-dependent SIR Model
Date: 2020-05-10 00:00:00

The current outbreak is known as Coronavirus Disease or COVID-19 caused by
the virus SAR-COV-2 which continues to wreak havoc across the globe. The World
Health Organization (WHO) has declared the outbreak a Public Health Emergency
of International Concern. In Pakistan, the spread of the virus is on the rise
with the number of infected people and causalities rapidly increasing. In the
absence of proper vaccination and treatment, to reduce the number of infections
and casualties, the only option so far is to educate people regarding
preventive measures and to enforce countrywide lock-down. Any strategy about
the preventive measures needs to be based upon detailed analysis of the
COVID-19 outbreak and accurate scientific predictions. In this paper, we
conduct mathematical and numerical analysis to come up with reliable and
accurate predictions of the outbreak in Pakistan. The time-dependent
Susceptible-Infected-Recovered (SIR) model is used to fit the data and provide
future predictions. The turning point of the peak of the pandemic is defined as
the day when the transmission rate becomes less than the recovering rate. We
have predicted that the outbreak will reach its maximum peak occurring from
late May to 9 June with unrecovered number of Infectives in the range
20000-47000 and the cumulative number of infected cases in the range of
57500-153100. The number of Infectives will remain at the lower end in the
lock-down scenario but can rapidly double or triple if the spread of the
epidemic is not curtailed and localized. The uncertainty on single day
projection in our analysis after April 15 is found to be within 5\%.
A stochastic epidemic model of COVID-19 disease
Date: 2020-05-13 00:00:00
Tasks: model

To model the evolution of diseases with extended latency periods and the
presence of asymptomatic patients like COVID-19, we define a simple discrete
time stochastic SIR-type epidemic model. We include both latent periods as well
as the presence of quarantine areas, to capture the evolutionary dynamics of
such diseases.
Optimal Tuning of a Class of Reset Controllers using Higher-Order Describing Function Analysis: Application in Precision Motion Systems
Date: 2020-05-28 00:00:00

Currently, the demand for a better alternative to linear PID controllers
increases due to the rising expectations of the high-tech industry. This causes
many researchers to explore non-linear controllers like reset controllers. In
literature, many reset architectures have been proposed to overcome the
inherent linear controller limitations. However, an appropriate tuning method
for these reset controllers has not been proposed so far. In this paper, an
optimal tuning method for a class of reset controllers including a novel
low-pass filter, is proposed using the recently developed frequency domain
method which is applicable to analyze closed-loop performances of these
controllers. In order to show the effectiveness of this approach, the
performance of the optimally tuned reset controller is compared with another
reset controller which is poorly tuned using the DF. Both controllers are also
compared with a PID to showcase the effectiveness of the proposed method. The
results re-assure that the DF method is not reliable for tuning reset
controllers, and it is possible that a reset controller showing better
performance according to a pure DF analysis has worse performances than a
linear controller in practice. Between the two reset controllers compared, the
proposed approach not only ensures optimal performance of the system, but also
results in reduced overall control output. Indeed, this tuning method has the
potential for enabling wide-scale application of reset controllers in industry.
Modality for Scenario Analysis and Maximum Likelihood Allocation
Authors: Takaaki Koike, Marius Hofert
Date: 2020-05-06 00:00:00

We study the variability of a risk from the statistical viewpoint of multimodality of the conditional loss distribution given that the aggregate loss equals an exogenously provided capital. This conditional distribution serves as a building block for calculating risk allocations such as the Euler capital allocation of Value-at-Risk. A superlevel set of this conditional distribution can be interpreted as a set of severe and plausible stress scenarios the given capital is supposed to cover. We show that various distributional properties of this conditional distribution, such as modality, dependence and tail behavior, are inherited from those of the underlying joint loss distribution. Among these properties, we find that modality of the conditional distribution is an important feature in risk assessment related to the variety of risky scenarios likely to occur in a stressed situation. Under unimodality, we introduce a novel risk allocation method called maximum likelihood allocation (MLA), defined as the mode of the conditional distribution given the total capital. Under multimodality, a single vector of allocations can be less sound. To overcome this issue, we investigate the so-called multimodalty adjustment to increase the soundness of risk allocations. Properties of the conditional distribution, MLA and multimodality adjustment are demonstrated in numerical experiments. In particular, we observe that negative dependence among losses typically leads to multimodality, and thus a higher multimodality adjustment can be required.
Percolation on the gene regulatory network
Authors: Giuseppe Torrisi, Reimer Kühn, Alessia Annibale
Date: 2020-05-06 00:00:00

We consider a simplified model for gene regulation, where gene expression is regulated by transcription factors (TFs), which are single proteins or protein complexes. Proteins are in turn synthesised from expressed genes, creating a feedback loop of regulation. This leads to a directed bipartite network in which a link from a gene to a TF exists if the gene codes for a protein contributing to the TF, and a link from a TF to a gene exists if the TF regulates the expression of the gene. Both genes and TFs are modelled as binary variables, which indicate, respectively, whether a gene is expressed or not, and a TF is synthesised or not. We consider the scenario where for a TF to be synthesised, all of its contributing genes must be expressed. This results in an ``AND'' gate logic for the dynamics of TFs. By adapting percolation theory to directed bipartite graphs, evolving according to the AND logic dynamics, we are able to determine the necessary conditions, in the network parameter space, under which bipartite networks can support a multiplicity of stable gene expression patterns, under noisy conditions, as required in stable cell types. In particular, the analysis reveals the possibility of a bi-stability region, where the extensive percolating cluster is or is not resilient to perturbations. This is remarkably different from the transition observed in standard percolation theory. Finally, we consider perturbations involving single node removal that mimic gene knockout experiments. Results reveal the strong dependence of the gene knockout cascade on the logic implemented in the underlying network dynamics, highlighting in particular that avalanche sizes cannot be easily related to gene-gene interaction networks.
Detecting Latent Communities in Network Formation Models
Authors: Shujie Ma, Liangjun Su, Yichong Zhang
Date: 2020-05-07 00:00:00
Tasks: Clustering, regression

This paper proposes a logistic undirected network formation model which allows for assortative matching on observed individual characteristics and the presence of edge-wise fixed effects. We model the coefficients of observed characteristics to have a latent community structure and the edge-wise fixed effects to be of low rank. We propose a multi-step estimation procedure involving nuclear norm regularization, sample splitting, iterative logistic regression and spectral clustering to detect the latent communities. We show that the latent communities can be exactly recovered when the expected degree of the network is of order log n or higher, where n is the number of nodes in the network. The finite sample performance of the new estimation and inference methods is illustrated through both simulated and real datasets.
Online Proximal-ADMM For Time-varying Constrained Convex Optimization
Date: 2020-10-10 00:00:00

This paper considers a convex optimization problem with cost and constraints
that evolve over time. The function to be minimized is strongly convex and
possibly non-differentiable, and variables are coupled through linear
constraints. In this setting, the paper proposes an online algorithm based on
the alternating direction method of multipliers (ADMM), to track the optimal
solution trajectory of the time-varying problem; in particular, the proposed
algorithm consists of a primal proximal gradient descent step and an
appropriately perturbed dual ascent step. The paper derives tracking results,
asymptotic bounds, and linear convergence results. The proposed algorithm is
then specialized to a multi-area power grid optimization problem, and our
numerical results verify the desired properties.
On the initiation of spiral waves in excitable media
Authors: S. P. Hastings, M. M Sussman
Date: 2020-05-07 00:00:00

Excitable media are systems which are at rest in the absence of external input but which respond to a sufficiently strong stimulus by sending a wave of "excitation" across the medium. Examples include cardiac and cortical tissue, and in each of these examples rotating spiral waves have been observed and associated with abnormalities. How such waves are initiated is not well understood. In this numerical study of a standard mathematical model of excitable media, we obtain spirals and other oscillatory patterns by a method, simple in design, which had previously been ruled out. We analyze the early stages of this process and show that long term stable oscillatory behavior, including spiral waves, can start with very simple initial conditions, such as two small spots of excitation, and no subsequent input. Thus, there are no refractory cells in the initial condition. For this model, even random spots of stimulation result in periodic rotating patterns relatively often, leading us to suggest that this could happen in living tissue.
On unbalanced data and common shock models in stochastic loss reserving
Authors: Benjamin Avanzi, Gregory Clive Taylor, Phuong Anh Vu, Bernard Wong
Date: 2020-05-07 00:00:00

Introducing common shocks is a popular dependence modelling approach, with some recent applications in loss reserving. The main advantage of this approach is the ability to capture structural dependence coming from known relationships. In addition, it helps with the parsimonious construction of correlation matrices of large dimensions. However, complications arise in the presence of "unbalanced data", that is, when (expected) magnitude of observations over a single triangle, or between triangles, can vary substantially. Specifically, if a single common shock is applied to all of these cells, it can contribute insignificantly to the larger values and/or swamp the smaller ones, unless careful adjustments are made. This problem is further complicated in applications involving negative claim amounts. In this paper, we address this problem in the loss reserving context using a common shock Tweedie approach for unbalanced data. We show that the solution not only provides a much better balance of the common shock proportions relative to the unbalanced data, but it is also parsimonious. Finally, the common shock Tweedie model also provides distributional tractability.
Improved supervised prediction of aging-related genes via weighted dynamic network analysis
Authors: Qi Li, Khalique Newaz, Tijana Milenković
Date: 2020-05-07 00:00:00

This study focuses on the task of supervised prediction of aging-related genes from -omics data. Unlike gene expression methods for this task that capture aging-specific information but ignore interactions between genes (i.e., their protein products), or protein-protein interaction (PPI) network methods for this task that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did not improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. This could be because the dynamic subnetwork was inferred using a naive Induced subgraph approach. Instead, we recently inferred a dynamic aging-specific subnetwork using a methodologically more advanced notion of network propagation (NP), which improved upon Induced dynamic aging-specific subnetwork in a different task, that of unsupervised analyses of the aging process. Here, we evaluate whether our existing NP-based dynamic subnetwork will improve upon the dynamic as well as static subnetwork constructed by the Induced approach in the considered task of supervised prediction of aging-related genes. The existing NP-based subnetwork is unweighted, i.e., it gives equal importance to each of the aging-specific PPIs. Because accounting for aging-specific edge weights might be important, we additionally propose a weighted NP-based dynamic aging-specific subnetwork. We demonstrate that a predictive machine learning model trained and tested on the weighted subnetwork yields higher accuracy when predicting aging-related genes than predictive models run on the existing unweighted dynamic or static subnetworks, regardless of whether the existing subnetworks were inferred using NP or the Induced approach.
Boosting spatial resolution by incorporating periodic boundary conditions into single-distance hard-x-ray phase retrieval
Date: 2020-08-30 00:00:00
Tasks: Retrieval, Unity

A simple coherent-imaging method due to Paganin et al. is widely employed for
phase-amplitude reconstruction of samples using a single paraxial x-ray
propagation-based phase-contrast image. The method assumes that the
sample-to-detector distance is sufficiently small for the associated Fresnel
number to be large compared to unity. The algorithm is particularly effective
when employed in a tomographic setting, using a single propagation-based
phase-contrast image for each projection. Here we develop a simple extension of
the method, which improves the reconstructed contrast of very fine sample
features. This provides first-principles motivation for boosting fine spatial
detail associated with high Fourier frequencies, relative to the original
method, and was inspired by several recent works employing empirically-obtained
Fourier filters to a similar end.
Belief-Averaged Relative Utilitarianism
Authors: Florian Brandl
Date: 2020-05-07 00:00:00

We consider social welfare functions when the preferences of individual agents and society maximize subjective expected utility in the tradition of Savage. A system of axioms is introduced whose unique solution is the social welfare function that averages the agents' beliefs and sums up their utility functions, normalized to have the same range. The first distinguishing axiom requires positive association of society's preferences with the agents' preferences for acts about which beliefs agree. The second is a weakening of Arrow's independence of irrelevant alternatives that only applies to non-redundant acts.
Proving prediction prudence
Authors: Dirk Tasche
Date: 2020-05-07 00:00:00
Tasks: Prediction

We study how to perform tests on samples of pairs of observations and predictions in order to assess whether or not the predictions are prudent. Prudence requires that that the mean of the difference of the observation-prediction pairs can be shown to be significantly negative. For safe conclusions, we suggest testing both unweighted (or equally weighted) and weighted means and explicitly taking into account the randomness of individual pairs. The test methods presented are mainly specified as bootstrap and normal approximation algorithms. The tests are general but can be applied in particular in the area of credit risk, both for regulatory and accounting purposes.