pwc-archive/papers-with-abstracts huggingface dataset:
Data-driven yaw misalignment correction for utility-scale wind turbines
Authors: Linyue Gao, Jiarong Hong
Date: 2021-09-18 00:00:00

In recent years, wind turbine yaw misalignment that tends to degrade the turbine power production and impact the blade fatigue loads raises more attention along with the rapid development of large-scale wind turbines. The state-of-the-art correction methods require additional instruments such as LiDAR to provide the ground truths and are not suitable for long-term operation and large-scale implementation due to the high costs. In the present study, we propose a framework that enables the effective and efficient detection and correction of static and dynamic yaw errors by using only turbine SCADA data, suitable for a low-cost regular inspection for large-scale wind farms in onshore, coastal, and offshore sites. This framework includes a short-period data collection of the turbine operating under multiple static yaw errors, a data mining correction for the static yaw error, and ultra-short-term dynamic yaw error forecasts with machine learning algorithms. Three regression algorithms, i.e., linear, support vector machine, and random forest, and a hybrid model based on the average prediction of the three, have been tested for dynamic yaw error prediction and compared using the field measurement data from a 2.5 MW turbine. For the data collected in the present study, the hybrid method shows the best performance and can reduce total yaw error by up to 85% (on average of 71%) compared to the cases without static and dynamic yaw error corrections. In addition, we have tested the transferability of the proposed method in the application of detecting other static and dynamic yaw errors.
First Demonstration of the Korean eLoran Accuracy in a Narrow Waterway Using Improved ASF Maps
Authors: Woohyun Kim, Pyo-Woong Son, Sul Gee Park, Sang Hyun Park, Jiwon Seo
Date: 2021-09-18 00:00:00
Tasks: Survey

The vulnerabilities of global navigation satellite systems (GNSSs) to radio frequency jamming and spoofing have attracted significant research attention. In particular, the large-scale jamming incidents that occurred in South Korea substantiate the practical importance of implementing a complementary navigation system. This letter briefly summarizes the efforts of South Korea to deploy an enhanced long-range navigation (eLoran) system, which is a terrestrial low-frequency radio navigation system that can complement GNSSs. After four years of research and development, the Korean eLoran testbed system has been recently deployed and is operational since June 1, 2021. Although its initial performance at sea is satisfactory, navigation through a narrow waterway is still challenging because a complete survey of the additional secondary factor (ASF), which is the largest source of error for eLoran, is practically difficult in a narrow waterway. This letter proposes an alternative way to survey the ASF in a narrow waterway and improve the ASF map generation methods. Moreover, the performance of the proposed approach was validated experimentally.
Physical Layer Anonymous Precoding: The Path to Privacy-Preserving Communications
Authors: Zhongxiang Wei, Christos Masouros, H. Vincent Poor, Athina P. Petropulu, Lajos Hanzo
Date: 2021-09-18 00:00:00
Tasks: Cloud Computing, Privacy Preserving

Next-generation systems aim to increase both the speed and responsiveness of wireless communications, while supporting compelling applications such as edge and cloud computing, remote-Health, vehicle-to-infrastructure communications, etc. As these applications are expected to carry confidential personal data, ensuring user privacy becomes a critical issue. In contrast to traditional security and privacy designs that aim to prevent confidential information from being eavesdropped upon by adversaries, or learned by unauthorized parties, in this paper we consider designs that mask the users' identities during communication, hence resulting in anonymous communications. In particular, we examine the recent interest in physical layer (PHY) anonymous solutions. This line of research departs from conventional higher layer anonymous authentication, encryption and routing protocols, and judiciously manipulates the signaling pattern of transmitted signals in order to mask the senders' PHY characteristics. We first discuss the concept of anonymity at the PHY, and illustrate a strategy that is able to unmask the sender's identity by analyzing his or her PHY information only, i.e., signalling patterns and the inherent fading characteristics. Subsequently, we overview the emerging area of anonymous precoding to preserve the sender's anonymity, while ensuring high receiver-side signal-to-interference-plus-noise ratio (SINR) for communication. This family of anonymous precoding designs represents a new approach to providing anonymity at the PHY, introducing a new dimension for privacy-preserving techniques.
A PDE Model for Protocell Evolution and the Origin of Chromosomes via Multilevel Selection
Authors: Daniel B. Cooney, Fernando W. Rossine, Dylan H. Morris, Simon A. Levin
Date: 2021-09-20 00:00:00

The evolution of complex cellular life involved two major transitions: the encapsulation of self-replicating genetic entities into cellular units and the aggregation of individual genes into a collectively replicating genome. In this paper, we formulate a minimal model of the evolution of proto-chromosomes within protocells. We model a simple protocell composed of two types of genes: a "fast gene" with an advantage for gene-level self-replication and a "slow gene" that replicates more slowly at the gene level, but which confers an advantage for protocell-level reproduction. Protocell-level replication capacity depends on cellular composition of fast and slow genes. We use a partial differential equation to describe how the composition of genes within protocells evolves over time under within-cell and between-cell competition. We find that the gene-level advantage of fast replicators casts a long shadow on the multilevel dynamics of protocell evolution: no level of between-protocell competition can produce coexistence of the fast and slow replicators when the two genes are equally needed for protocell-level reproduction. By introducing a "dimer replicator" consisting of a linked pair of the slow and fast genes, we show analytically that coexistence between the two genes can be promoted in pairwise multilevel competition between fast and dimer replicators, and provide numerical evidence for coexistence in trimorphic competition between fast, slow, and dimer replicators. Our results suggest that dimerization, or the formation of a simple chromosome-like dimer replicator, can help to overcome the shadow of lower-level selection and work in concert with deterministic multilevel selection to allow for the coexistence of two genes that are complementary at the protocell-level but compete at the level of individual gene-level replication.
On the valuation of multiple reset options: integral equation approach
Authors: Nazym Azimbayev, Yerkin Kitapbayev
Date: 2021-09-20 00:00:00

In this paper, we study a pricing problem of the multiple reset put option, which allows the holder to reset several times a current strike price to obtain an at-the-money European put option. We formulate the pricing problem as a multiple optimal stopping problem, then reduce it to a sequence of single optimal stopping problems and study the associated free-boundary problems. We solve this sequence of problems by induction in the number of remaining reset rights and exploit probabilistic arguments such as local time-space calculus on curves. As a result, we characterize each optimal reset boundary as the unique solution to a nonlinear integral equation and derive the reset premium representations for the option prices. We propose that the multiple reset options can be used as cryptocurrency derivatives and an attractive alternative to standard European options due to the extreme volatility of underlying cryptocurrencies.
Surface activity of cancer cells: the fusion of two cell aggregates
Authors: Ivana Pajic-Lijakovic, Milan Milivojevic
Date: 2021-09-20 00:00:00
Tasks: Vocal Bursts Valence Prediction

Although a good comprehension of how cancer cells collectively migrate by following molecular rules which influence the state of cell-cell adhesion contacts has been generated, the impact of collective migration on cellular rearrangement from subcellular to supracellular level remains less understood. Thus, considering collective cell migration (CCM) of cancer mesenchymal cells on one side and healthy epithelial cells on the other during the fusion of two cell aggregates could result in a powerful tool in order to address the contribution of structural changes at subcellular level which influence the cellular rearrangements and help to understand this important, but still controversial topic. While healthy epithelial cells undergo volumetric cell rearrangement driven by the tissue surface tension, which results in a collision of opposite directed velocity front near the contact point between two cell aggregates, mesenchymal cells follow quite different scenario. These cells are capable of reducing the surface tension and undergo surface cell rearrangement. The main goal of this contribution is to discuss the origin of surface activity of cancer cells by accounting for the crosstalk between cell-cell and cell-ECM adhesion contacts influenced by the cell contractility.
Motility switching and front-back synchronisation in polarized cells
Authors: Gissell Estrada-Rodriguez, Benoit Perthame
Date: 2021-09-18 00:00:00

The combination of protrusions and retractions in the movement of polarized cells leads to understand the effect of possible synchronisation between the two ends of the cells. This synchronisation, in turn, could lead to different dynamics such as normal and fractional diffusion. Departing from a stochastic single cell trajectory, where a memory effect induces persistent movement, we derive a kinetic-renewal system at the mesoscopic scale. We investigate various scenarios with different levels of complexity, where the two ends of the cell move either independently or with partial or full synchronisation. We study the relevant macroscopic limits where we obtain diffusion, drift-diffusion or fractional diffusion, depending on the initial system. This article clarifies the form of relevant macroscopic equations that describe the possible effects of synchronised movement in cells, and sheds light on the switching between normal and fractional diffusion
Nipah virus vector sequences in COVID-19 patient samples sequenced by the Wuhan Institute of Virology
Authors: Steven C. Quay, Daoyu Zhang, Adrian Jones, Yuri Deigin
Date: 2021-09-19 00:00:00
Tasks: Virology

We report the detection of Nipah virus in an infectious clone format, a BSL4-level pathogen and CDC-designated Bioterrorism Agent, in raw RNA-Seq sequencing reads deposited by the Wuhan Institute of Virology (WIV) produced from five December 2019 patients infected with SARS-CoV-2. Research involving Nipah infectious clones has never been reported to have occured at the WIV. These patient samples have been previously reported to contain reads from several other viruses: Influenza A, Spodoptera frugiperda rhabdovirus and Nipah. Previous authors have interpreted the presence of these virus sequences as indicative of co-infections of the patients in question by these pathogens or laboratory contamination. However, our analysis shows that NiV genes are encapsulated in synthetic vectors, which we infer was for assembly of a NiV infectious clone. In particular, we document the finding of internal N, P-V-W-C and L protein coding sequences as well as coverage of the G and F genes. Furthermore, the format of Hepatitis D virus ribozyme and T7 terminator downstream of the 5-prime end of the NiV sequence is consistent with truncation required at the end of the genome for a full length infectious clone. This indicates that research at WIV was being conducted on an assembled NiV infectious clone. Contamination of patient sequencing reads by an infectious NiV clone of the highly pathogenic Bangladesh strain could indicate a significant breach of BSL-4 protocols. We call on WIV to explain the purpose of this research on infectious clones of Nipah Virus, the full chronology of this work, and to explain how and at what stage of sample preparation this contamination occurred.
Functional switching among dynamic neuronal hub-nodes in the brain induces transition of cognitive states
Authors: Jasleen Gund, Yashaswee Mishra, R. K. Brojen Singh, B. N. Mallick
Date: 2021-09-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The cognitive states have broadly been divided into waking, rapid eye movement sleep and non-REMS. Although the mechanism of state transition is unknown, it has been proposed that functional activation/deactivation among different brain regions leads to such transition. As analysis of electroencephalogram allows us exploring properties and association among brain regions, we exploited it to address our query. We have recorded the frontal and occipital cortical EEG from surgically prepared chronic freely moving, normally behaving rats and classified their vigilance states and vigilance state-transitions. The complexity analysis carried out by computing multifractal spectrum width categorized VST as highly non-linear and complex than their participating vigilance states. The EEG signals were decomposed into frequency ranges as that of classical human Delta, Theta, Alpha, Beta and Gamma oscillations. The dynamic network attributes of these oscillations has shown compelling topological correlation between the frontal and occipital regions of the brain during conscious states. The topological trends of the underlying hierarchical network organization have been characterized by the percentage of the hub and non-hub nodes, that further determines the global and local connectivity trends. This has revealed interesting insights of functional disconnection during NREMS due to decrease in their number of hub-nodes as compared to waking state. There is also switching behaviour in the ratio of hub/non-hub nodes between frontal and occipital region during NREMS-Wake and Wake-NREMS transitions. Our findings provide support as proof-of-principle of functional regional inactivation or activation of hub-nodes as the gradual switching mechanism towards transitioning of cognitive states in a graded manner, wake to sleep or vice versa; the detailed neuro-physio-chemical mechanism needs further study.
Mass vaccination in a roaring pandemic
Authors: Giuseppe Gaeta
Date: 2021-09-19 00:00:00

Mass vaccination produces a reduction in virus circulation, but also evolutive pressure towards the appearance of virus-resistant strains. We discuss the balance between these two effects, in particular when the mass vaccination takes place in the middle of an epidemic period.
Bifurcation analysis of the predator-prey model with the Allee effect in the predator
Authors: Deeptajyoti Sen, Andrew Morozov, S. Ghorai, Malay Banerjee
Date: 2021-09-18 00:00:00

The use of predator-prey models in theoretical ecology has a long history, and the model equations have largely evolved since the original Lotka-Volterra system towards more realistic descriptions of the processes of predation, reproduction and mortality. One important aspect is the recognition of the fact that the growth of a population can be subject to an Allee effect, where the per capita growth rate increases with the population density. Including an Allee effect has been shown to fundamentally change predator-prey dynamics and strongly impact species persistence, but previous studies mostly focused on scenarios of an Allee effect in the prey population. Here we explore a predator-prey model with an ecologically important case of the Allee effect in the predator population where it occurs in the numerical response of predator without affecting its functional response. Biologically, this can result from various scenarios such as a lack of mating partners, sperm limitation and cooperative breeding mechanisms, among others. Unlike previous studies, we consider here a generic mathematical formulation of the Allee effect without specifying a concrete parameterisation of the functional form, and analyse the possible local bifurcations in the system. Further, we explore the global bifurcation structure of the model and its possible dynamical regimes for three different concrete parameterisations of the Allee effect. The model possesses a complex bifurcation structure: there can be multiple coexistence states including two stable limit cycles. Inclusion of the Allee effect in the predator generally has a destabilising effect on the coexistence equilibrium. We also show that regardless of the parametrisation of the Allee effect, enrichment of the environment will eventually result in extinction of the predator population.
A framework for benchmarking uncertainty in deep regression
Authors: Franko Schmähling, Jörg Martin, Clemens Elster
Date: 2021-09-10 00:00:00
Tasks: Benchmarking, regression, Uncertainty Quantification

We propose a framework for the assessment of uncertainty quantification in deep regression. The framework is based on regression problems where the regression function is a linear combination of nonlinear functions. Basically, any level of complexity can be realized through the choice of the nonlinear functions and the dimensionality of their domain. Results of an uncertainty quantification for deep regression are compared against those obtained by a statistical reference method. The reference method utilizes knowledge of the underlying nonlinear functions and is based on a Bayesian linear regression using a reference prior. Reliability of uncertainty quantification is assessed in terms of coverage probabilities, and accuracy through the size of calculated uncertainties. We illustrate the proposed framework by applying it to current approaches for uncertainty quantification in deep regression. The flexibility, together with the availability of a reference solution, makes the framework suitable for defining benchmark sets for uncertainty quantification.
Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray Images
Authors: Pratik Bhowal, Subhankar Sen, Jin Hee Yoon Zong Woo Geem, Ram Sarkar
Date: 2021-09-09 00:00:00
Proceeding: IEEE Journal of Biomedical and Health Informatics 2021 9
Tasks: COVID-19 Diagnosis, Ensemble Learning, Transfer Learning

Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis (CAD) is inevitable. In this work, we have analyzed Chest X-ray (CXR) images for the detection of the coronavirus. The primary agenda of this proposed research study is to leverage the classification performance of the deep learning models using ensemble learning. Many papers have proposed different ensemble learning techniques in this field, some methods using aggregation functions like Weighted Arithmetic Mean (WAM) among others. However, none of these methods take into consideration the decisions that subsets of the classifiers take. In this paper, we have applied Choquet integral for ensemble and propose a novel method for the evaluation of fuzzy measures using Coalition Game Theory, Information Theory, and Lambda fuzzy approximation. Three different sets of Fuzzy Measures are calculated using three different weighting schemes along with information theory and coalition game theory. Using these three sets of fuzzy measures three Choquet Integrals are calculated and their decisions are finally combined. We have created a database by combining several image repositories developed recently. Impressive results on the newly developed dataset and the challenging COVIDx dataset support the efficacy and robustness of the proposed method. To the best of our knowledge, our experimental results outperform many recently proposed methods. Source code available at https://github.com/subhankar01/Covid-Chestxray-lambda-fuzzy
Attention based Multi-Modal New Product Sales Time-series Forecasting
Authors: Vijay Ekambaram, Kushagra Manglik, Sumanta Mukherjee, SURYA SHRAVAN KUMAR SAJJA, Satyam Dwivedi, Vikas Raykar
Date: 2020-08-23 00:00:00
Proceeding: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 8
Tasks: Decoder, New Product Sales Forecasting, Short-observation new product sales forecasting, Time Series, Time Series Analysis, Time Series Forecasting

Trend driven retail industries such as fashion, launch substantial new products every season. In such a scenario, an accurate demand forecast for these newly launched products is vital for efficient downstream supply chain planning like assortment planning and stock allocation. While classical time-series forecasting algorithms can be used for existing products to forecast the sales, new products do not have any historical time-series data to base the forecast on. In this paper, we propose and empirically evaluate several novel attention-based multi-modal encoder-decoder models to forecast the sales for a new product purely based on product images, any available product attributes and also external factors like holidays, events, weather, and discount. We experimentally validate our approaches on a large fashion dataset and report the improvements in achieved accuracy and enhanced model interpretability as compared to existing k-nearest neighbor based baseline approaches.
Multigraph Approach Towards a Scalable, Robust look-alike Audience Extension System
Authors: Ernest Kirubakaran Selvaraj, Tushar Agarwal, Nilamadhaba Mohapatra, Swapnasarit Sahu
Date: 2021-08-14 00:00:00
Proceeding: AdKDD 2021 8

In online advertising, finding the right audience is critical for the success of a campaign. One common way of finding the right audience is to find users with traits similar to the users who have responded positively to the campaign in the past. The small pool of users who have responded positively to the campaign is known as the seed set and the goal here is to reach a bigger audience with traits very similar to that of the seed set. This technique, popularly known as look-alike audience extension, gets increasingly challenging with the scale and high sparsity of data commonly encountered in the advertising domain. In this paper, we present a novel multigraph-based audience extension and scoring system, which works well with high-dimensional sparse data and can be scaled easily to millions of users. Our experimental results on large real-world data demonstrate significant improvement in the performance of our approach over the existing architectures.
Does Vision-and-Language Pretraining Improve Lexical Grounding?
Authors: Tian Yun, Chen Sun, Ellie Pavlick
Date: 2021-09-21 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Linguistic representations derived from text alone have been criticized for their lack of grounding, i.e., connecting words to their meanings in the physical world. Vision-and-Language (VL) models, trained jointly on text and image or video data, have been offered as a response to such criticisms. However, while VL pretraining has shown success on multimodal tasks such as visual question answering, it is not yet known how the internal linguistic representations themselves compare to their text-only counterparts. This paper compares the semantic representations learned via VL vs. text-only pretraining for two recent VL models using a suite of analyses (clustering, probing, and performance on a commonsense question answering task) in a language-only setting. We find that the multimodal models fail to significantly outperform the text-only variants, suggesting that future work is required if multimodal pretraining is to be pursued as a means of improving NLP in general.