pwc-archive/papers-with-abstracts huggingface dataset:
Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death counts
Authors: Tobias Wistuba, Andreas Mayr, Christian Staerk
Date: 2021-09-06 00:00:00

The effective reproduction number is a key figure to monitor the course of the COVID-19 pandemic. In this study we consider a retrospective modelling approach for estimating the effective reproduction number based on death counts during the first year of the pandemic in Germany. The proposed Bayesian hierarchical model incorporates splines to estimate reproduction numbers flexibly over time while adjusting for varying effective infection fatality rates. The approach also provides estimates of dark figures regarding undetected infections over time. Results for Germany illustrate that estimated reproduction numbers based on death counts are often similar to classical estimates based on confirmed cases. However, considering death counts proves to be more robust against shifts in testing policies: during the second wave of infections, classical estimation of the reproduction number suggests a flattening/ decreasing trend of infections following the "lockdown light" in November 2020, while our results indicate that true numbers of infections continued to rise until the "second lockdown" in December 2020. This observation is associated with more stringent testing criteria introduced concurrently with the "lockdown light", which is reflected in subsequently increasing dark figures of infections estimated by our model. These findings illustrate that the retrospective viewpoint can provide additional insights regarding the course of the pandemic. In light of progressive vaccinations, shifting the focus from modelling confirmed cases to reported deaths with the possibility to incorporate effective infection fatality rates might be of increasing relevance for the future surveillance of the pandemic.
spatialSim: multi-species spatiotemporal size-structured operating model for management strategy evaluation
Authors: Christopher D. Nottingham, Russell B. Millar
Date: 2021-09-06 00:00:00
Tasks: Management

Spatiotemporal processes have the potential to be one of the most influential factors governing how fisheries targeting sedentary species respond to harvesting. Despite this, management strategy evaluation often fails to account for space or does so at low resolutions due to compute constraints. In this paper, a multi-species spatiotemporal size-structured operating model for sedentary species is presented. The model combines a spatially continuous Gaussian Markov Random Field model of the population dynamics with an areal harvesting model that supports preferential targeting and site selection constraints (e.g., economic constraints). This approach is very compute efficient, which makes it feasible to simulate realistic fisher dynamics and catch data at true spatial scale (e.g., the swept area of a dredge). The New Zealand surfclam fishery was used as a case study to demonstrate the versatility of the operating model and to showcase the simulation of localized depletion, which was manifest in the generation of realistic catch-per-unit-effort data that were uncorrelated with the trends in population abundance. The model is available as part of the open-source R package spatialSim.
Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources
Authors: Jeffrey W. Doser, Wendy Leuenberger, T. Scott Sillett, Michael T. Hallworth, Elise F. Zipkin
Date: 2021-09-04 00:00:00
Tasks: Data Integration, Management

The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends, and effects of environmental variables on entire communities. However, modeling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modeling approaches exist to simultaneously provide species and community level inferences. We present an "integrated community occupancy model" (ICOM) that unites principles of data integration and hierarchical community modeling in a single framework to provide inferences on species-specific and community occurrence dynamics using multiple data sources. We use simulations to compare the ICOM to previously developed hierarchical community occupancy models and single species integrated distribution models. We then apply our model to assess the occurrence and biodiversity dynamics of foliage-gleaning birds in the White Mountain National Forest in the northeastern USA from 2010-2018 using three independent data sources. Simulations reveal that integrating multiple data sources in the ICOM increased precision and accuracy of species and community level inferences compared to single data source models, although benefits of integration were dependent on data source quality (e.g., amount of replication). Compared to single species models, the ICOM yielded more precise species-level estimates. Within our case study, the ICOM had the highest out-of-sample predictive performance compared to single species models and models that used only a subset of the three data sources. The ICOM offers an attractive approach to estimate species and biodiversity dynamics, which is additionally valuable to inform management objectives of both individual species and their broader communities.
Optimized protocol for DNA extraction from ancient skeletal remains using Chelex-100
Authors: Wera M Schmerer
Date: 2021-09-05 00:00:00

PCR-based analysis of skeletonized human remains is a common aspect in both forensic human identification as well as Ancient DNA research. In this, both areas not merely utilize very similar methodology, but also share the same problems regarding quantity and quality of recovered DNA and presence of inhibitory substances in samples from excavated remains. To enable amplification based analysis of the remains, development of optimized DNA extraction procedures is thus a critical factor in both areas. The study here presents an optimized protocol for DNA extraction from ancient skeletonized remains using Chelex-100, which proved to be effective in yielding amplifiable extracts from sample material excavated after centuries in a soil environment, which consequently have high inhibitor content and overall limited DNA preservation. Success of the optimization strategies utilized is shown in significantly improved amplification outcomes compared to the predecessor method.
Positive Stochastic Collocation for the Collocated Local Volatility Model
Authors: Fabien Le Floc'h, Cornelis W. Oosterlee
Date: 2021-09-06 00:00:00

This paper presents how to apply the stochastic collocation technique to assets that can not move below a boundary. It shows that the polynomial collocation towards a lognormal distribution does not work well. Then, the potentials issues of the related collocated local volatility model (CLV) are explored. Finally, a simple analytical expression for the Dupire local volatility derived from the option prices modelled by stochastic collocation is given.
Semi-analytical pricing of barrier options in the time-dependent $λ$-SABR model
Authors: Andrey Itkin, Dmitry Muravey
Date: 2021-09-05 00:00:00

We extend the approach of Carr, Itkin and Muravey, 2021 for getting semi-analytical prices of barrier options for the time-dependent Heston model with time-dependent barriers by applying it to the so-called $\lambda$-SABR stochastic volatility model. In doing so we modify the general integral transform method (see Itkin, Lipton, Muravey, Generalized integral transforms in mathematical finance, World Scientific, 2021) and deliver solution of this problem in the form of Fourier-Bessel series. The weights of this series solve a linear mixed Volterra-Fredholm equation (LMVF) of the second kind also derived in the paper. Numerical examples illustrate speed and accuracy of our method which are comparable with those of the finite-difference approach at small maturities and outperform them at high maturities even by using a simplistic implementation of the RBF method for solving the LMVF.
Future Photovoltaic Electricity Production Targets and The Link to Consumption per Capita on The Policy Level in MENA Region
Authors: Mostafa Abdelrashied, Dikshita Bhattacharya
Date: 2021-09-05 00:00:00

This paper provides an overview of the status of the electricity market in the region, indicating the nexus between electricity consumption with population growth and GDP. It also analyzes the policy portfolio in different countries, indicating some of the in-action policies' effectiveness and recommended alternatives. World Bank datasets were used for the analysis between 2000 and 2014. We found that the MENA region is at an early stage for renewable energy with a high potential for solar energy, making it attractive for investors. However, the high dependency on oil for consumption and exporting might not provide a prosperous environment for renewable technologies to grow. Therefore, a greater focus on decoupling economic growth from energy consumption will have a long-lasting impact on fiscal revenues for net-oil exporting countries. Moreover, the consequences of the decoupling will allow more renewables penetration in the current energy mix enabling many countries to reach their Paris Agreement goals. For short-term energy policy actions, starting a subsidy reform towards the final repeal of subsidies is a must as these measures relate to all end-use sectors and impact fiscal stability in many countries. With its 1.65GW Benban Solar Park in Aswan, Egypt has shown an example of shifting from subsidizing fossil fuel products to commissioning renewable projects to get closer to its Paris Agreement targets.
TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting
Authors: Zonghan Wu, Da Zheng, Shirui Pan, Quan Gan, Guodong Long, George Karypis
Date: 2021-08-25 00:00:00
Tasks: Attribute, Graph Neural Network

This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-temporal attribute entities with topological structure, the space-time is consecutive and unified while each node's current status is influenced by its neighbors' past states over variant periods of each neighbor. Most spatial-temporal neural networks for traffic forecasting study spatial dependency and temporal correlation separately in processing, gravely impaired the spatial-temporal integrity, and ignore the fact that the neighbors' temporal dependency period for a node can be delayed and dynamic. To model this actual condition, we propose TraverseNet, a novel spatial-temporal graph neural network, viewing space and time as an inseparable whole, to mine spatial-temporal graphs while exploiting the evolving spatial-temporal dependencies for each node via message traverse mechanisms. Experiments with ablation and parameter studies have validated the effectiveness of the proposed TraverseNet, and the detailed implementation can be found from https://github.com/nnzhan/TraverseNet.
A Method for Inferring Polymers Based on Linear Regression and Integer Programming
Authors: Ryota Ido, Shengjuan Cao, Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu
Date: 2021-08-24 00:00:00
Tasks: Form, regression

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In this paper, we design a new method for inferring a polymer based on the framework. For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers. We also use linear regression as a building block of constructing a prediction function in the framework. The results of our computational experiments reveal a set of chemical properties on polymers to which a prediction function constructed with linear regression performs well. We also observe that the proposed method can infer polymers with up to 50 non-hydrogen atoms in a monomer form.
Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems
Authors: Patryk Grelewicz, Thanh Tung Khuat, Jacek Czeczot, Pawel Nowak, Tomasz Klopot, Bogdan Gabrys
Date: 2021-01-08 00:00:00

In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed loop performance. This approach allows for determining the best (but also robust and practically achievable) closed loop performance based on very popular and intuitive closed loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different machine learning based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution setup.
An Internet of Things humanoid robot teleoperated by an open source Android application
Authors: Georgios Angelopoulos, Georgios Theodoros Kalampokis, Minas Dasygenis
Date: 2018-01-18 00:00:00
Proceeding: 17-18 Nov 2018 1
Tasks: Industrial Robots, Translation

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals, or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human interaction. Nowadays the use of IoT in industrial robots is very popular contrary to humanoid robots. There have been a few attempts that combine IoT with humanoid robots but they have limited features. This paper proposes a novel open-source platform Android application for controlling a humanoid robot, with more features than other research projects, in remote areas either behind NAT (Network Address Translation) or without it. The user can view what the robot is seeing through the Android application and can also move it. Additionally, our system has the ability to deliver real-time audio through our application. We envision that our proposed system is not only an attractive solution for many telerobotic applications but also an extensive educational tool in special needs education.
gen2Out: Detecting and Ranking Generalized Anomalies
Authors: Meng-Chieh Lee, Shubhranshu Shekhar, Christos Faloutsos, T. Noah Hutson, Leon Iasemidis
Date: 2021-09-06 00:00:00
Tasks: Anomaly Detection, EEG, Electroencephalogram (EEG)

In a cloud of m-dimensional data points, how would we spot, as well as rank, both single-point- as well as group- anomalies? We are the first to generalize anomaly detection in two dimensions: The first dimension is that we handle both point-anomalies, as well as group-anomalies, under a unified view -- we shall refer to them as generalized anomalies. The second dimension is that gen2Out not only detects, but also ranks, anomalies in suspiciousness order. Detection, and ranking, of anomalies has numerous applications: For example, in EEG recordings of an epileptic patient, an anomaly may indicate a seizure; in computer network traffic data, it may signify a power failure, or a DoS/DDoS attack. We start by setting some reasonable axioms; surprisingly, none of the earlier methods pass all the axioms. Our main contribution is the gen2Out algorithm, that has the following desirable properties: (a) Principled and Sound anomaly scoring that obeys the axioms for detectors, (b) Doubly-general in that it detects, as well as ranks generalized anomaly -- both point- and group-anomalies, (c) Scalable, it is fast and scalable, linear on input size. (d) Effective, experiments on real-world epileptic recordings (200GB) demonstrate effectiveness of gen2Out as confirmed by clinicians. Experiments on 27 real-world benchmark datasets show that gen2Out detects ground truth groups, matches or outperforms point-anomaly baseline algorithms on accuracy, with no competition for group-anomalies and requires about 2 minutes for 1 million data points on a stock machine.
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
Authors: Simon Dahan, Logan Z. J. Williams, Daniel Rueckert, Emma C. Robinson
Date: 2021-09-07 00:00:00
Tasks: Action Recognition, Functional Connectivity, Skeleton Based Action Recognition

The study of functional brain connectivity (FC) is important for understanding the underlying mechanisms of many psychiatric disorders. Many recent analyses adopt graph convolutional networks, to study non-linear interactions between functionally-correlated states. However, although patterns of brain activation are known to be hierarchically organised in both space and time, many methods have failed to extract powerful spatio-temporal features. To overcome those challenges, and improve understanding of long-range functional dynamics, we translate an approach, from the domain of skeleton-based action recognition, designed to model interactions across space and time. We evaluate this approach using the Human Connectome Project (HCP) dataset on sex classification and fluid intelligence prediction. To account for subject topographic variability of functional organisation, we modelled functional connectomes using multi-resolution dual-regressed (subject-specific) ICA nodes. Results show a prediction accuracy of 94.4% for sex classification (an increase of 6.2% compared to other methods), and an improvement of correlation with fluid intelligence of 0.325 vs 0.144, relative to a baseline model that encodes space and time separately. Results suggest that explicit encoding of spatio-temporal dynamics of brain functional activity may improve the precision with which behavioural and cognitive phenotypes may be predicted in the future.
GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation
Authors: Derek Chen, Zhou Yu
Date: 2021-09-07 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Data Augmentation

Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes. Directly training a model with labeled OOS examples yields reasonable performance, but obtaining such data is a resource-intensive process. To tackle this limited-data problem, previous methods focus on better modeling the distribution of in-scope (INS) examples. We introduce GOLD as an orthogonal technique that augments existing data to train better OOS detectors operating in low-data regimes. GOLD generates pseudo-labeled candidates using samples from an auxiliary dataset and keeps only the most beneficial candidates for training through a novel filtering mechanism. In experiments across three target benchmarks, the top GOLD model outperforms all existing methods on all key metrics, achieving relative gains of 52.4%, 48.9% and 50.3% against median baseline performance. We also analyze the unique properties of OOS data to identify key factors for optimally applying our proposed method.
Fruit-CoV: An Efficient Vision-based Framework for Speedy Detection and Diagnosis of SARS-CoV-2 Infections Through Recorded Cough Sounds
Authors: Long H. Nguyen, Nhat Truong Pham, Van Huong Do, Liu Tai Nguyen, Thanh Tin Nguyen, Van Dung Do, Hai Nguyen, Ngoc Duy Nguyen
Date: 2021-09-06 00:00:00

SARS-CoV-2 is colloquially known as COVID-19 that had an initial outbreak in December 2019. The deadly virus has spread across the world, taking part in the global pandemic disease since March 2020. In addition, a recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths over the world. Therefore, it is vital to possess a self-testing service of SARS-CoV-2 at home. In this study, we introduce Fruit-CoV, a two-stage vision framework, which is capable of detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, we convert sounds into Log-Mel Spectrograms and use the EfficientNet-V2 network to extract its visual features in the first stage. In the second stage, we use 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN to aggregate feature representations of the Log-Mel Spectrograms. Finally, we use the combined features to train a binary classifier. In this study, we use a dataset provided by the AICovidVN 115M Challenge, which includes a total of 7371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results show that our proposed model achieves an AUC score of 92.8% and ranks the 1st place on the leaderboard of the AICovidVN Challenge. More importantly, our proposed framework can be integrated into a call center or a VoIP system to speed up detecting SARS-CoV-2 infections through online/recorded cough sounds.
Combining data assimilation and machine learning to estimate parameters of a convective-scale model
Authors: Stefanie Legler, Tijana Janjic
Date: 2021-09-07 00:00:00
Tasks: parameter estimation

Errors in the representation of clouds in convection-permitting numerical weather prediction models can be introduced by different sources. These can be the forcing and boundary conditions, the representation of orography, the accuracy of the numerical schemes determining the evolution of humidity and temperature, but large contributions are due to the parametrization of microphysics and the parametrization of processes in the surface and boundary layers. These schemes typically contain several tunable parameters that are either not physical or only crudely known, leading to model errors. Traditionally, the numerical values of these model parameters are chosen by manual model tuning. More objectively, they can be estimated from observations by the augmented state approach during the data assimilation. Alternatively, in this work, we look at the problem of parameter estimation through an artificial intelligence lens by training two types of artificial neural networks (ANNs) to estimate several parameters of the one-dimensional modified shallow-water model as a function of the observations or analysis of the atmospheric state. Through perfect model experiments, we show that Bayesian neural networks (BNNs) and Bayesian approximations of point estimate neural networks (NNs) are able to estimate model parameters and their relevant statistics. The estimation of parameters combined with data assimilation for the state decreases the initial state errors even when assimilating sparse and noisy observations. The sensitivity to the number of ensemble members, observation coverage, and neural network size is shown. Additionally, we use the method of layer-wise relevance propagation to gain insight into how the ANNs are learning and discover that they naturally select only a few gridpoints that are subject to strong winds and rain to make their predictions of chosen parameters.