pwc-archive/papers-with-abstracts huggingface dataset:
Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept
Authors: Mili Wadhwani, Sakshi Deshmukh, Harsh S. Dhiman
Date: 2022-04-28 00:00:00
Tasks: Fault Detection, Time Series, Time Series Analysis

Wind turbine is a complex machine with its rotating and non-rotating equipment being sensitive to faults. Due to increased wear and tear, the maintenance aspect of a wind turbine is of critical importance. Unexpected failure of wind turbine components can lead to increased O\&M costs which ultimately reduces effective power capture of a wind farm. Fault detection in wind turbines is often supplemented with SCADA data available from wind farm operators in the form of time-series format with a 10-minute sample interval. Moreover, time-series analysis and data representation has become a powerful tool to get a deeper understating of the dynamic processes in complex machinery like wind turbine. Wind turbine SCADA data is usually available in form of a multivariate time-series with variables like gearbox oil temperature, gearbox bearing temperature, nacelle temperature, rotor speed and active power produced. In this preprint, we discuss the concept of a digital twin for time to failure forecasting of the wind turbine gearbox where a predictive module continuously gets updated with real-time SCADA data and generates meaningful insights for the wind farm operator.
The Tree of Blobs of a Species Network: Identifiability under the Coalescent
Authors: Elizabeth S. Allman, Hector Baños, Jonathan D. Mitchell, John A. Rhodes
Date: 2022-05-06 00:00:00

Inference of species networks from genomic data under the Network Multispecies Coalescent Model is currently severely limited by heavy computational demands. It also remains unclear how complicated networks can be for consistent inference to be possible. As a step toward inferring a general species network, this work considers its tree of blobs, in which non-cut edges are contracted to nodes, so only tree-like relationships between the taxa are shown. An identifiability theorem, that most features of the unrooted tree of blobs can be determined from the distribution of gene quartet topologies, is established. This depends upon an analysis of gene quartet concordance factors under the model, together with a new combinatorial inference rule. The arguments for this theoretical result suggest a practical algorithm for tree of blobs inference, to be fully developed in a subsequent work.
Scanning Electron Microscopy and Metabolite Measurement Revealed the Stress Mechanism of PS-COOH Microplastics on Rhodotorula mucilaginosa AN5
Authors: Jiahao Ma, Xiangfei Meng, Zixin Li, Lexian Li, Jiwen Xu, Guangfeng Kan
Date: 2022-05-07 00:00:00

Microplastics in the marine environment have been paid more and more attention by researchers, and the impact of these substances on marine microorganisms can not be ignored. Studies have shown that PS-COOH Microplastics are harmful to marine molluscs, algae and monads. This study explore the effect and mechanism of microplastics (80 nm PS-COOH) on Antarctic marine yeast, Rhodotorula mucilaginosa AN5 by bacterial count, Scanning Electron Microscopy (SEM) and metabolite analysis. The results illustrates that a 50 mg/L concentration of PS-COOH could inhibit 36.15% growth of yeast cells and 10 mg/L inhibit 80.20%. Microplastics stress causes changes in the content of some oxidative stress substances, including reactive oxygen species (ROS) 42.86% , malondialdehyde (MDA) 54.06% content and the activities of antioxidant enzymes such as catalase (CAT) 36.00% , peroxidase (POD) 66.67% and superoxide dismutase (SOD) 25.40%. These results revealed the possible stress effect of microplastic pollution on marine yeast and may affect bottom layer of marine ecosystem.
Pre-frontal cortex guides dimension-reducing transformations in the occipito-ventral pathway for categorization behaviors
Authors: Y. Duan, J. Zhan, J. Gross, R. A. A. Ince, P. G. Schyns
Date: 2022-05-09 00:00:00
Tasks: feature selection

To interpret our surroundings, the brain uses a visual categorization process. Current theories and models suggest that this process comprises a hierarchy of different computations that transforms complex, high-dimensional inputs into lower-dimensional representations (i.e. manifolds) in support of multiple categorization behaviors. Here, we tested this hypothesis by analyzing these transformations reflected in dynamic MEG source activity while individual participants actively categorized the same stimuli according to different tasks: face expression, face gender, pedestrian gender, vehicle type. Results reveal three transformation stages guided by pre-frontal cortex. At Stage 1 (high-dimensional, 50-120ms), occipital sources represent both task-relevant and task-irrelevant stimulus features; task-relevant features advance into higher ventral/dorsal regions whereas task-irrelevant features halt at the occipital-temporal junction. At Stage 2 (121-150ms), stimulus feature representations reduce to lower-dimensional manifolds, which then transform into the task-relevant features underlying categorization behavior over Stage 3 (161-350ms).
A neural network model for timing control with reinforcement
Authors: Jing Wang, Yousuf El-Jayyousi, Ilker Ozden
Date: 2022-05-09 00:00:00

How do humans and animals perform trial-and-error learning when the space of possibilities is infinite? In a previous study, we used an interval timing production task and discovered an updating strategy in which the agent adjusted the behavioral and neuronal noise for exploration. In the experiment, human subjects proactively generated a series of timed motor outputs. We found that the sequential motor timing varied at two temporal scales: long-term correlation around the target interval due to memory drifts and short-term adjustments of timing variability according to feedback. We have previously described these features of timing variability with an augmented Gaussian process, termed reward sensitive Gaussian process (RSGP). Here we provide a mechanistic model and simulate the process by borrowing the architecture of recurrent neural networks. While recurrent connection provided the long-term serial correlation in motor timing, to facilitate reward-driven short-term variations, we introduced reward-dependent variability in the network connectivity, inspired by the stochastic nature of synaptic transmission in the brain. Our model was able to recursively generate an output sequence incorporating the internal variability and external reinforcement in a Bayesian framework. We show that the model can learn the key features of human behavior. Unlike other neural network models that search for unique network connectivity for the best match between the model prediction and observation, this model can estimate the uncertainty associated with each outcome and thus did a better job in teasing apart adjustable task-relevant variability from unexplained variability. The proposed artificial neural network model parallels the mechanisms of information processing in neural systems and can extend the framework of brain-inspired reinforcement learning in continuous state control.
Microfluidic cell engineering on high-density microelectrode arrays for assessing structure-function relationships in living neuronal networks
Authors: Yuya Sato, Hideaki Yamamoto, Hideyuki Kato, Takashi Tanii, Shigeo Sato, Ayumi Hirano-Iwata
Date: 2022-05-09 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Neuronal networks in dissociated culture combined with cell engineering technology offer a pivotal platform to constructively explore the relationship between structure and function in living neuronal networks. Here, we fabricated defined neuronal networks possessing a modular architecture on high-density microelectrode arrays (HD-MEAs), a state-of-the-art electrophysiological tool for recording neural activity with high spatial and temporal resolutions. We first established a surface coating protocol using a cell-permissive hydrogel to stably attach polydimethylsiloxane microfluidic film on the HD-MEA. We then recorded the spontaneous neural activity of the engineered neuronal network, which revealed an important portrait of the engineered neuronal network--modular architecture enhances functional complexity by reducing the excessive neural correlation between spatially segregated modules. The results of this study highlight the impact of HD-MEA recordings combined with cell engineering technologies as a novel tool in neuroscience to constructively assess the structure-function relationships in neuronal networks.
Pattern dynamics and stochasticity of the brain rhythms
Authors: Clarissa Hoffman, Jingheng Cheng, Daoyun Ji, Y. Dabaghian
Date: 2022-05-06 00:00:00

Our current understanding of brain rhythms is based on quantifying their instantaneous or time-averaged characteristics. What remains unexplored, is the actual structure of the waves -- their shapes and patterns over finite timescales. To address this, we used two independent approaches to link wave forms to their physiological functions: the first is based on quantifying their consistency with the underlying mean behavior, and the second assesses "orderliness" of the waves' features. The corresponding measures capture the wave's characteristic and abnormal behaviors, such as atypical periodicity or excessive clustering, and demonstrate coupling between the patterns' dynamics and the animal's location, speed and acceleration. Specifically, we studied patterns of $\theta$ and $\gamma$ waves, and Sharp Wave Ripples, and observed speed-modulated changes of the wave's cadence, an antiphase relationship between orderliness and acceleration, as well as spatial selectiveness of patterns. Furthermore, we found an interdependence between orderliness and regularity: larger deviations from steady oscillatory behavior tend to accompany disarrayed temporal cluttering of peaks and troughs. Taken together, our results offer a complementary -- mesoscale -- perspective on brain wave structure, dynamics, and functionality.
Single-species population models with age structure and psychological effect in a polluted environment
Authors: Jiawei Wang, Ruiyang Zhou, Fengying Wei
Date: 2022-05-08 00:00:00

This paper considers a single-population model with age structure and psychological effects in a polluted environment. We divide the single population into two stages of larval and adult structure. The model uses Logistic input, and the larvae are converted into adult bodies by constant ratio. We only consider adulthood. The role of psychological effects makes the contact between adult and environmental toxins a functional form, while the contact between larvae and environmental toxins is linear. For the deterministic model embodied as a nonlinear time-varying system, we discuss the asymptotic stability of the system by Lyapunov one-time approximation theory, and give a sufficient condition for stability to be established. Considering that the contact rate between biological and environmental toxins in nature is not always constant, we make the contact rate interfere with white noise, and then modify the contact rate into a stochastic process, thus establishing a corresponding random single-population model. According to It\^o formula and Lyapunov in the function method, we first prove the existence of globally unique positive solutions for stochastic models under arbitrary initial conditions, and then give sufficient conditions for weak average long-term survival and random long-term survival for single populations in the expected sense.
Stage structured prey-predator model incorporating mortal peril consequential to inefficiency and habitat complexity in juvenile hunting
Authors: Debasish Bhattacharjee, Tapasvini Roy, Santanu Acharjee, Tarini Kumar Dutta
Date: 2022-05-08 00:00:00

Dynamic exploration for a predator-prey bio-system of two species with ratio-dependent functional response is carried out, where the capability to predate in both the stages of the predator, the juvenile and the matured, is taken into account. But, only the matured predators are inferred to be efficient in killing the prey without any negative repercussions. The mortality risks for the juvenile predators are attributable to the inefficiency rate of juveniles coupled with habitat complexity which is either in the form of anti-predator behavior of the prey taken with the aid of their habitat or in the form of a territorial generalist mesopredator. So as to avoid extinction of either of the species and to preserve the food chain of the ecological system, the results pertaining to the existence and stability of all the equilibrium points of the bio-system along with permanence, transcritical and Hopf bifurcation has been thoroughly studied. Corroboration of the results along with the dependence of the biosystem on some crucial parameters is done through numerical simulation. It is found that juvenile predators' inefficiency relative to the resistance confronted, plays a crucial role to control each species density of the ecosystem, as an intriguing limit cycle between the trivial and axial equilibriums of the proposed system along with the co-existing periodic point, because of some ineffeciency parametric value of the juvenile predator has been witnessed.
New results and open questions for SIR-PH epidemic models with linear birth rate, loss of immunity, vaccination, and disease and vaccination fatalities
Authors: Florin Avram, Rim Adenane, Andrei Halanay
Date: 2022-05-07 00:00:00
Tasks: Epidemiology

Our paper presents three new classes of models: SIR-PH, SIR-PH-FA, and SIR-PH-IA, and states two problems we would like to solve about them. Recall that deterministic mathematical epidemiology has one basic general law, the R0 alternative" of [52, 51], which states that the local stability condition of the disease free equilibrium may be expressed as R0 < 1, where R0 is the famous basic reproduction number, which plays also a major role in the theory of branching processes. The literature suggests that it is impossible to find general laws concerning the endemic points. However, it is quite common that 1. When R0 > 1, there exists a unique fixed endemic point, and 2. the endemic point is locally stable when R0 > 1. One would like to establish these properties for a large class of realistic epidemic models (and we do not include here epidemics without casualties). We have introduced in [7, 5] a "simple", but broad class of "SIR-PH models" with varying population, with the express purpose of establishing for these processes the two properties above. Since that seemed still hard, we have introduced a further class of "SIR-PH-FA" models, which may be interpreted as approximations for the SIR-PH models, and which includes simpler models typically studied in the literature (with constant population, without loss of immunity, etc). The goal of our paper is to draw attention to the two open problems above, for the SIR-PH, SIR-PH-FA, and also for a second, more refined "intermediate approximation" SIR-PH-IA. We illustrate the current status-quo by presenting new results on a generalization of the SAIRS epidemic model of [44, 40].
Bitcoin Returns and Public Attention to COVID-19: Do Timing and Individualism Matter?
Authors: Huaxin Wang-Lu
Date: 2022-05-09 00:00:00

The evolution of the pandemic and people's concern over it have an impact on the Bitcoin market, while the extent of individualism could differentiate investor behaviors in the financial market during the pandemic. This paper examines whether public attention to COVID-19 in individualistic countries versus collectivistic countries Granger causes Bitcoin returns between February 11, 2020 and May 09, 2022. To this end, eight large economies with a individualistic or collectivistic tradition are chosen for analyses. By using rolling and recursive-evolving algorithms, it accounts for the timing of COVID-19 issues that vary by country and circumvents the potential estimation bias that a traditional Granger causality test may suffer due largely to Google's sampling variation for different time frames. In general, collectivistic countries are found to have stronger causal impacts on Bitcoin returns than individualistic countries.
Diamonds and forward variance models
Authors: Peter Friz, Jim Gatheral
Date: 2022-05-07 00:00:00
Tasks: Form

In this non-technical introduction to diamond trees and forests, we focus on their application to computation in stochastic volatility models written in forward variance form, rough volatility models in particular.
The AI Index 2022 Annual Report
Authors: Daniel Zhang, Nestor Maslej, Erik Brynjolfsson, John Etchemendy, Terah Lyons, James Manyika, Helen Ngo, Juan Carlos Niebles, Michael Sellitto, Ellie Sakhaee, Yoav Shoham, Jack Clark, Raymond Perrault
Date: 2022-05-02 00:00:00
Tasks: Ethics

Welcome to the fifth edition of the AI Index Report! The latest edition includes data from a broad set of academic, private, and nonprofit organizations as well as more self-collected data and original analysis than any previous editions, including an expanded technical performance chapter, a new survey of robotics researchers around the world, data on global AI legislation records in 25 countries, and a new chapter with an in-depth analysis of technical AI ethics metrics. The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The report aims to be the world's most credible and authoritative source for data and insights about AI.
MIMIC-III, a freely accessible critical care database
Authors: Alistair E.W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, Roger G. Mark
Date: 2016-05-24 00:00:00
Proceeding: Nature 2016 5
Tasks: Blood pressure estimation, Data Integration, Diagnostic, Length-of-Stay prediction, Medical Code Prediction, Mortality Prediction, Multi-Label Classification Of Biomedical Texts, Multi-Label Text Classification, Multivariate Time Series Forecasting

MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.

In recent years there has been a concerted move towards the adoption of digital health record systems in hospitals. In the US, for example, the number of non-federal acute care hospitals with basic digital systems increased from 9.4 to 75.5% over the 7-year period between 2008 and 2014 (ref. 1).

Despite this advance, interoperability of digital systems remains an open issue, leading to challenges in data integration. As a result, the potential that hospital data offers in terms of understanding and improving care is yet to be fully realized. In parallel, the scientific research community is increasingly coming under criticism for the lack of reproducibility of studies2.

Here we report the release of the MIMIC-III database, an update to the widely-used MIMIC-II database (Data Citation 1). MIMIC-III integrates de-identified, comprehensive clinical data of patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts, and makes it widely accessible to researchers internationally under a data use agreement (Fig. 1). The open nature of the data allows clinical studies to be reproduced and improved in ways that would not otherwise be possible.
SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks
Authors: Weigang Lu, Yibing Zhan, Binbin Lin, Ziyu Guan, Liu Liu, Baosheng Yu, Wei Zhao, Yaming Yang, DaCheng Tao
Date: 2021-12-22 00:00:00
Tasks: Link Prediction, Node Classification

Graph Convolutional Networks (GCNs) suffer from performance degradation when models go deeper. However, earlier works only attributed the performance degeneration to over-smoothing. In this paper, we conduct theoretical and experimental analysis to explore the fundamental causes of performance degradation in deep GCNs: over-smoothing and gradient vanishing have a mutually reinforcing effect that causes the performance to deteriorate more quickly in deep GCNs. On the other hand, existing anti-over-smoothing methods all perform full convolutions up to the model depth. They could not well resist the exponential convergence of over-smoothing due to model depth increasing. In this work, we propose a simple yet effective plug-and-play module, Skipnode, to overcome the performance degradation of deep GCNs. It samples graph nodes in each convolutional layer to skip the convolution operation. In this way, both over-smoothing and gradient vanishing can be effectively suppressed since (1) not all nodes'features propagate through full layers and, (2) the gradient can be directly passed back through ``skipped'' nodes. We provide both theoretical analysis and empirical evaluation to demonstrate the efficacy of Skipnode and its superiority over SOTA baselines.
Scalable logo recognition in real-world images
Authors: Stefan Romberg, Lluis Garcia Pueyo, Rainer Lienhart, Roelof van Zwol
Date: 2011-04-17 00:00:00
Proceeding: ACM International Conference on Multimedia Retrieval 2011 4
Tasks: Logo Recognition

In this paper we propose a highly effective and scalable framework for recognizing logos in images. At the core of our approach lays a method for encoding and indexing the relative spatial layout of local features detected in the logo images. Based on the analysis of the local features and the composition of basic spatial structures, such as edges and triangles, we can derive a quantized representation of the regions in the logos and minimize the false positive detections. Furthermore, we propose a cascaded index for scalable multi-class recognition of logos.

For the evaluation of our system, we have constructed and released a logo recognition benchmark which consists of manually labeled logo images, complemented with non-logo images, all posted on Flickr. The dataset consists of a training, validation, and test set with 32 logo-classes. We thoroughly evaluate our system with this benchmark and show that our approach effectively recognizes different logo classes with high precision.