pwc-archive/papers-with-abstracts huggingface dataset:
Optical Wireless Satellite Networks versus Optical Fiber Terrestrial Networks: The Latency Perspective
Authors: Aizaz U. Chaudhry, Halim Yanikomeroglu
Date: 2021-06-14 00:00:00

Formed by using laser inter-satellite links (LISLs) among satellites in upcoming low Earth orbit and very low Earth orbit satellite constellations, optical wireless satellite networks (OWSNs), also known as free-space optical satellite networks, can provide a better alternative to existing optical fiber terrestrial networks (OFTNs) for long-distance inter-continental data communications. The LISLs operate at the speed of light in vacuum in space, which gives OWSNs a crucial advantage over OFTNs in terms of latency. In this paper, we employ the satellite constellation for Phase I of Starlink and LISLs between satellites to simulate an OWSN. Then, we compare the network latency of this OWSN and the OFTN under three different scenarios for long-distance inter-continental data communications. The results show that the OWSN performs better than the OFTN in all scenarios. It is observed that the longer the length of the inter-continental connection between the source and the destination, the better the latency improvement offered by the OWSN compared to OFTN.
Qualitative analysis of a mathematical model for Xylella fastidiosa epidemics
Authors: Edoardo Beretta, Vincenzo Capasso, Simone Scacchi, Matteo Brunetti, Matteo Montagna
Date: 2021-06-15 00:00:00
Tasks: Management

In Southern Italy, since 2013, there has been an ongoing Olive Quick Decline Syndrome (OQDS) outbreak, due to the bacterium Xylella fastidiosa. In a couple of previous papers, the authors have proposed a mathematical approach for identifying possible control strategies for eliminating or at least reduce the economic impact of such event. The main players involved in OQDS are represented by the insect vector, Philaenus spumarius, its host plants (olive trees and weeds) and the bacterium, X. fastidiosa. A basic mathematical model has been expressed in terms of a system of ordinary differential equations; a preliminary analysis already provided interesting results about possible control strategies within an integrated pest management framework, not requiring the removal of the productive resource represented by the olive trees. The same conjectures have been later confirmed by analyzing the impact of possible spatial heterogeneities on controlling a X. fastidiosa epidemic. These encouraging facts have stimulated a more detailed and rigorous mathematical analysis of the same system, as presented in this paper. A clear picture of the possible steady states (equilibria) and their stability properties has been outlined, within a variety of different parameter scenarios, for the original spatially homogeneous ecosystem. The results obtained here confirm, in a mathematically rigorous way, what had been conjectured in the previous papers, i.e. that the removal of a suitable amount of weed biomass (reservoir of the juvenile stages of the insect vector of X. fastidiosa from olive orchards and surrounding areas is the most acceptable strategy to control the spread of the OQDS. In addition, as expected, the adoption of more resistant olive tree cultivars has been shown to be a good strategy, though less cost-effective, in controlling the pathogen.
Stochastic fluctuations in protein interaction networks are nearly Poissonian
Authors: Jaroslav Albert
Date: 2021-06-15 00:00:00

Gene regulatory networks are comprised of biochemical reactions, which are inherently stochastic. Each reaction channel contributes to this stochasticity in different measure. In this paper we study the stochastic dynamics of protein interaction networks (PIN) that are made up of monomers and dimers. The network is defined by the dimers, which are formed by hybridizing two monomers. The size of a PIN was defined as the number of monomers that interacts with at least one other monomer (including itself). We generated 4200 random PIN of sizes between 2 and 8 (600 per size) and simulated via the Gillespie algorithm the stochastic evolution of copy numbers of all monomers and dimers until they reached a steady state. The simulations revealed that the Fano factors of both monomers and dimers in all networks and for all time points were close to one, either from below or above. Only 10% of Fano factors for monomers were above 1.3 and 10% of Fano factors for dimers were above 1.17, with 5.54 and 3.47 as the maximum value recorded for monomers and dimers, respectively. These findings suggests that PIN in real biological setting contribute to the overall stochastic noise that is close to Poisson. Our results also show a correlation between stochastic noise, network size and network connectivity: for monomers, the Fano factors tend towards 1 from above, while the Fano factors for dimers tend towards 1 from below. For monomers, this tendency is amplified with increased network connectivity.
Noradrenergic neuromodulation of nonlinear bursting neurons controls critical dynamics
Authors: Brandon R. Munn, Eli J. Müller, James M. Shine
Date: 2021-06-15 00:00:00

In order to remain adaptable to a dynamic environment, neural activity must be simultaneously both sensitive and stable. To solve this problem, the brain has been hypothesised to sit near a critical boundary. Yet, precisely how criticality and these opposing information processing modes are implemented in the brain remains elusive. A potential solution to this problem involves modulating intrinsically nonlinear neurons within the cerebral cortex with neuromodulatory neurotransmitters such as noradrenaline, a highly-conserved chemical released from the pontine locus coeruleus. Here we confirm that neuronal spiking in mice is poised close to the critical point of a branching process and that time-varying signatures of criticality fluctuate with neuromodulatory tone, as assessed by dynamic alterations in pupil diameter. We explore these results theoretically by creating a dual-compartment model of non-linear pyramidal neurons - capable of both regular spike and bursting modes - that replicates our main empirical findings of slightly subcritical dynamics. We then probe our model at a resolution impossible in vivo to demonstrate that noradrenaline differentially alters spiking- and bursting-criticality to facilitate sensitive and stable dynamics following an inverted-U profile that peaks at intermediate noradrenergic tone. Finally, we demonstrate that this intermediate noradrenergic regime displays burst avalanches with power-law size and duration distributions and scaling relationship belonging to the universality class of self-organized criticality. Our results confirm that the noradrenergic ascending arousal system acts as a control parameter for emergent critical dynamics in the brain. This methodology could be extended to explore other neuromodulators as control parameters of the brain.
Interval Timing: Modeling the break-run-break pattern using start/stop threshold-less drift-diffusion model
Authors: Jason Zwicker, Francois Rivest
Date: 2021-06-14 00:00:00

Animal interval timing is often studied through the peak interval (PI) procedure. In this procedure, the animal is rewarded for the first response after a fixed delay from the stimulus onset, but on some trials, the stimulus remains and no reward is given. The common methods and models to analyse the response pattern describe it as break-run-break, a period of low rate response followed by rapid responding, followed by a low rate of response. The study of the pattern has found correlations between start, stop, and duration of the run period that hold across species and experiment. It is commonly assumed that in order to achieve the statistics with a pacemaker accumulator model it is necessary to have start and stop thresholds. In this paper we will develop a new model that varies response rate in relation to the likelihood of event occurrence, as opposed to a threshold, for changing the response rate. The new model reproduced the start and stop statistics that have been observed in 14 different PI experiments from 3 different papers. The developed model is also compared to the Time-adaptive Drift-diffusion Model (TDDM), the latest accumulator model subsuming the scalar expectancy theory (SET), on all 14 data-sets. The results show that it is unnecessary to have explicit start and stop thresholds or an internal equivalent to break-run-break states to reproduce the individual trials statistics and population behaviour and get the same break-run-break analysis results. The new model also produces more realistic individual trials compared to TDDM.
CoV-Spectrum: Analysis of globally shared SARS-CoV-2 data to Identify and Characterize New Variants
Authors: Chaoran Chen, Sarah Nadeau, Michael Yared, Philippe Voinov, Ning Xie, Cornelius Roemer, Tanja Stadler
Date: 2021-06-15 00:00:00

Summary: The CoV-Spectrum website supports the identification of new SARS-CoV-2 variants of concern and the tracking of known variants. Its flexible amino acid and nucleotide mutation search allows querying of variants before they are designated by a lineage nomenclature system. The platform brings together SARS-CoV-2 data from different sources and applies analyses. Results include the proportion of different variants over time, their demographic and geographic distributions, common mutations, hospitalization and mortality probabilities, estimates for transmission fitness advantage and insights obtained from wastewater samples. Availability and Implementation: CoV-Spectrum is available at https://cov-spectrum.ethz.ch. The code is released under the GPL-3.0 license at https://github.com/cevo-public/cov-spectrum-website
The Effect of Client Appraisal on the Efficiency of Micro Finance Bank
Authors: Esther Yusuf Enoch, Usman Abubakar Arabo, Abubakar Mahmud Digil
Date: 2021-06-14 00:00:00
Tasks: Descriptive, Management

One of the major problems confronting financial institutions most especially microfinance institutions is the increasing incidence of loan defaults and consequence loan losses which manifested in their financial performance with huge uncollectible loans and advances. This study assessed the effects of credit management on financial performance on microfinance institutions in Adamawa State, Nigeria. Specifically, we examine the effect of client appraisal on the efficiency of microfinance banks in Adamawa State. The methodology employed in this study is the survey method in which both primary and secondary sources were used in the collection of data. A multi-stage sampling method was adopted in selecting a sample of 21 respondents from a total population of 52 credit officers. Questionnaires were used in the due collection of data from the respondents. Descriptive statistics (simple percentage) and inferential statistics (regression analysis) were used to analyze the data collected and in testing the hypotheses. The study showed that client appraisal has a positive effect on efficiency and productivity.
Tag Disentangled Generative Adversarial Networks for Object ImageRe-rendering
Authors: Chaoyue Wang, Chaohui Wang, Chang Xu, DaCheng Tao
Date: 2017-02-19 00:00:00
Proceeding: International Joint Conference on Artificial Intelligence 2017 2
Tasks: Object, TAG

In  this  paper,  we  propose  a  principled  Tag  Dis-entangled  Generative  Adversarial  Networks  (TD-GAN) for re-rendering new images for the object of interest from a single image of it by specifying multiple scene properties (such as viewpoint, illumination, expression,etc.).  The whole framework consists of a  disentangling network,  a  generative network,  a tag mapping net,  and a discriminative network, which are trained jointly based on a given set of images that are complete/partially tagged(i.e., supervised/semi-supervised setting). Given an input image, the disentangling network extracts dis-entangled and interpretable representations, which are then used to generate images by the generative network.  In order to boost the quality of disentangled representations,  the tag mapping net is integrated to explore the consistency between the image and its tags.   Furthermore,  the discriminative network is introduced to implement the adversarial training strategy for generating more realistic images.  Experiments on two challenging datasets demonstrate the state-of-the-art performance of the proposed framework in the problem of interest.
Exploring the Wasserstein metric for survival analysis
Authors: Margaux Luck*, Tristan Sylvain*, Joseph Paul Cohen, Heloise Cardinal, Andrea Lodi, Yoshua Bengio
Date: 2021-03-01 00:00:00
Proceeding: Proceedings of Machine Learning Research 1:1–13 2021 3
Tasks: Survival Analysis

Survival analysis is a type of semi-supervised task where the target output (the survival time) is often
right-censored. Utilizing this information is a challenge because it is not obvious how to correctly
incorporate these censored examples into a model. We study how three categories of loss functions
can take advantage of this information: partial likelihood methods, rank methods, and our own
classification method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier (KM)
estimate of the probability density to impute the labels of censored examples. The proposed method
predicts the probability distribution of an event, letting us compute survival curves and expected times
of survival that are easier to interpret than the rank. We also demonstrate that this approach directly
optimizes the expected C-index which is the most common evaluation metric for survival models.
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-Supervision
Authors: Fangyu Li, N. Dinesh Reddy, Xudong Chen and Srinivasa G. Narasimhan
Date: 2021-07-16 00:00:00
Proceeding: IEEE Intelligent Vehicles Symposium 2021 7
Tasks: 3D Reconstruction, Anomaly Detection, Autonomous Vehicles

Reconstructing 4D vehicular activity (3D space
and time) from cameras is useful for autonomous vehicles,
commuters and local authorities to plan for smarter and safer
cities. Traffic is inherently repetitious over long periods, yet
current deep learning-based 3D reconstruction methods have
not considered such repetitions and have difficulty generalizing
to new intersection-installed cameras. We present a novel
approach exploiting longitudinal (long-term) repetitious motion
as self-supervision to reconstruct 3D vehicular activity from a
video captured by a single fixed camera. Starting from offthe-shelf 2D keypoint detections, our algorithm optimizes 3D
vehicle shapes and poses, and then clusters their trajectories in
3D space. The 2D keypoints and trajectory clusters accumulated
over long-term are later used to improve the 2D and 3D
keypoints via self-supervision without any human annotation.
Our method improves reconstruction accuracy over state of
the art on scenes with a significant visual difference from the
keypoint detector’s training data, and has many applications
including velocity estimation, anomaly detection and vehicle
counting. We demonstrate results on traffic videos captured
at multiple city intersections, collected using our smartphones,
YouTube, and other public datasets.
Quantitative Approaches to Comparing Communication Patterns on Twitter
Authors: Axel Bruns, Stefan Stieglitz
Date: 2012-12-06 00:00:00
Proceeding: Journal of Technology in Human Services 2012 12

To date, the available literature mainly discusses Twitter activity patterns in the context of individual case studies, while comparative research on a large number of communicative events and their dynamics and patterns is missing. By conducting a comparative study of more than 40 different cases (covering topics such as elections, natural disasters, corporate crises, and televised events) we identify a number of distinct types of discussion that can be observed on Twitter. Drawing on a range of communicative metrics, we show that thematic and contextual factors influence the usage of different communicative tools available to Twitter users, such as original tweets, @replies, retweets, and URLs. Based on this first analysis of the overall metrics of Twitter discussions, we also demonstrate stable patterns in the use of Twitter in the context of major topics and events.
What Counterfactuals Can Be Tested
Authors: Ilya Shpitser, Judea Pearl
Date: 2012-06-20 00:00:00
Tasks: counterfactual

Counterfactual statements, e.g., "my headache would be gone had I taken an aspirin" are central to scientific discourse, and are formally interpreted as statements derived from "alternative worlds". However, since they invoke hypothetical states of affairs, often incompatible with what is actually known or observed, testing counterfactuals is fraught with conceptual and practical difficulties. In this paper, we provide a complete characterization of "testable counterfactuals," namely, counterfactual statements whose probabilities can be inferred from physical experiments. We provide complete procedures for discerning whether a given counterfactual is testable and, if so, expressing its probability in terms of experimental data.
Causes and Explanations: A Structural-Model Approach --- Part 1: Causes
Authors: Joseph Y. Halpern, Judea Pearl
Date: 2013-01-10 00:00:00

We propose a new definition of actual causes, using structural equations to model counterfactuals.We show that the definitions yield a plausible and elegant account ofcausation that handles well examples which have caused problems forother definitions and resolves major difficulties in the traditionalaccount. In a companion paper, we show how the definition of causality can beused to give an elegant definition of (causal) explanation.
Automatic Extraction and Sign Determination of Respiratory Signal in Real-time Cardiac Magnetic Resonance imaging
Authors: Chong Chen, Yingmin Liu, Orlando P. Simonetti, Rizwan Ahmad
Date: 2020-02-08 00:00:00

In real-time (RT) cardiac cine imaging, a stack of 2D slices is collected sequentially under free-breathing conditions. A complete heartbeat from each slice is then used for cardiac function quantification. The inter-slice respiratory mismatch can compromise accurate quantification of cardiac function. Methods based on principal components analysis (PCA) have been proposed to extract the respiratory signal from RT cardiac cine, but these methods cannot resolve the inter-slice sign ambiguity of the respiratory signal. In this work, we propose a fully automatic sign correction procedure based on the similarity of neighboring slices and correlation to the center-of-mass curve. The proposed method is evaluated in eleven volunteers, with ten slices per volunteer. The motion in a manually selected region-of-interest (ROI) is used as a reference. The results show that the extracted respiratory signal has a high, positive correlation with the reference in all cases. The qualitative assessment of images also shows that the proposed approach can accurately identify heartbeats, one from each slice, belonging to the same respiratory phase. This approach can improve cardiac function quantification for RT cine without manual intervention.
mSHAP: SHAP Values for Two-Part Models
Authors: Spencer Matthews, Brian Hartman
Date: 2021-06-16 00:00:00
Tasks: Vocal Bursts Valence Prediction

Two-part models are important to and used throughout insurance and actuarial science. Since insurance is required for registering a car, obtaining a mortgage, and participating in certain businesses, it is especially important that the models which price insurance policies are fair and non-discriminatory. Black box models can make it very difficult to know which covariates are influencing the results. SHAP values enable interpretation of various black box models, but little progress has been made in two-part models. In this paper, we propose mSHAP (or multiplicative SHAP), a method for computing SHAP values of two-part models using the SHAP values of the individual models. This method will allow for the predictions of two-part models to be explained at an individual observation level. After developing mSHAP, we perform an in-depth simulation study. Although the kernelSHAP algorithm is also capable of computing approximate SHAP values for a two-part model, a comparison with our method demonstrates that mSHAP is exponentially faster. Ultimately, we apply mSHAP to a two-part ratemaking model for personal auto property damage insurance coverage. Additionally, an R package (mshap) is available to easily implement the method in a wide variety of applications.
Temporal Convolution Networks with Positional Encoding for Evoked Expression Estimation
Authors: VanThong Huynh, Guee-Sang Lee, Hyung-Jeong Yang, Soo-Huyng Kim
Date: 2021-06-16 00:00:00

This paper presents an approach for Evoked Expressions from Videos (EEV) challenge, which aims to predict evoked facial expressions from video. We take advantage of pre-trained models on large-scale datasets in computer vision and audio signals to extract the deep representation of timestamps in the video. A temporal convolution network, rather than an RNN like architecture, is used to explore temporal relationships due to its advantage in memory consumption and parallelism. Furthermore, to address the missing annotations of some timestamps, positional encoding is employed to ensure continuity of input data when discarding these timestamps during training. We achieved state-of-the-art results on the EEV challenge with a Pearson correlation coefficient of 0.05477, the first ranked performance in the EEV 2021 challenge.
Developing a Fidelity Evaluation Approach for Interpretable Machine Learning
Authors: Mythreyi Velmurugan, Chun Ouyang, Catarina Moreira, Renuka Sindhgatta
Date: 2021-06-16 00:00:00
Tasks: BIG-bench Machine Learning, Explainable Artificial Intelligence (XAI), Explanation Fidelity Evaluation, Interpretable Machine Learning

Although modern machine learning and deep learning methods allow for complex and in-depth data analytics, the predictive models generated by these methods are often highly complex, and lack transparency. Explainable AI (XAI) methods are used to improve the interpretability of these complex models, and in doing so improve transparency. However, the inherent fitness of these explainable methods can be hard to evaluate. In particular, methods to evaluate the fidelity of the explanation to the underlying black box require further development, especially for tabular data. In this paper, we (a) propose a three phase approach to developing an evaluation method; (b) adapt an existing evaluation method primarily for image and text data to evaluate models trained on tabular data; and (c) evaluate two popular explainable methods using this evaluation method. Our evaluations suggest that the internal mechanism of the underlying predictive model, the internal mechanism of the explainable method used and model and data complexity all affect explanation fidelity. Given that explanation fidelity is so sensitive to context and tools and data used, we could not clearly identify any specific explainable method as being superior to another.