pwc-archive/papers-with-abstracts huggingface dataset:
Optimal, near-optimal, and robust epidemic control
Authors: Dylan H. Morris, Fernando W. Rossine, Joshua B. Plotkin, Simon A. Levin
Date: 2020-04-05 00:00:00

In the absence of drugs and vaccines, policymakers use non-pharmaceutical interventions such as social distancing to decrease rates of disease-causing contact, with the aim of reducing or delaying the epidemic peak. These measures carry social and economic costs, so societies may be unable to maintain them for more than a short period of time. Intervention policy design often relies on numerical simulations of epidemic models, but comparing policies and assessing their robustness demands clear principles that apply across strategies. Here we derive the theoretically optimal strategy for using a time-limited intervention to reduce the peak prevalence of a novel disease in the classic Susceptible-Infectious-Recovered epidemic model. We show that broad classes of easier-to-implement strategies can perform nearly as well as the theoretically optimal strategy. But neither the optimal strategy nor any of these near-optimal strategies is robust to implementation error: small errors in timing the intervention produce large increases in peak prevalence. Our results reveal fundamental principles of non-pharmaceutical disease control and expose their potential fragility. For robust control, an intervention must be strong, early, and ideally sustained.
Simulation, visualization and analysis tools for pattern recognition assessment with spiking neuronal networks
Authors: Sergio E. Galindo, Pablo Toharia, Oscar D. Robles, Eduardo Ros, Luis Pastor, Jesús A. Garrido
Date: 2020-03-13 00:00:00
Tasks: Evolutionary Algorithms

Computational modeling is becoming a widely used methodology in modern neuroscience. However, as the complexity of the phenomena under study increases, the analysis of the results emerging from the simulations concomitantly becomes more challenging. In particular, the configuration and validation of brain circuits involving learning often require the processing of large amounts of action potentials and their comparison to the stimulation being presented to the input of the system. In this study we present a systematic work-flow for the configuration of spiking-neuronal-network-based learning systems including evolutionary algorithms for information transmission optimization, advanced visualization tools for the validation of the best suitable configuration and customized scripts for final quantitative evaluation of the learning capabilities. By integrating both grouped action potential information and stimulation-related events, the proposed visualization framework provides qualitatively assessment of the evolution of the learning process in the simulation under study. The proposed work-flow has been used to study how receptive fields emerge in a network of inhibitory interneurons with excitatory and inhibitory spike-timing dependent plasticity when it is exposed to repetitive and partially overlapped stimulation patterns. According to our results, the output population reliably detected the presence of the stimulation patterns, even when the fan-in ratio of the interneurons was considerably restricted.
The Anatomy of Mitos Web Search Engine
Authors: Panagiotis Papadakos, Giorgos Vasiliadis, Yannis Theoharis, Nikos Armenatzoglou, Stella Kopidaki, Yannis Marketakis, Manos Daskalakis, Kostas Karamaroudis, Giorgos Linardakis, Giannis Makrydakis, Vangelis Papathanasiou, Lefteris Sardis, Petros Tsialiamanis, Georgia Troullinou, Kostas Vandikas, Dimitris Velegrakis, Yannis Tzitzikas
Date: 2008-03-14 00:00:00
Tasks: Anatomy, Clustering, Information Retrieval, Retrieval

Engineering a Web search engine offering effective and efficient information retrieval is a challenging task. This document presents our experiences from designing and developing a Web search engine offering a wide spectrum of functionalities and we report some interesting experimental results. A rather peculiar design choice of the engine is that its index is based on a DBMS, while some of the distinctive functionalities that are offered include advanced Greek language stemming, real time result clustering, and advanced link analysis techniques (also for spam page detection).
ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System
Authors: Georgios Alexandridis, Georgios Siolas, Andreas Stafylopatis
Date: 2017-06-22 00:00:00
Tasks: Recommendation Systems

Review-based recommender systems have gained noticeable ground in recent years. In addition to the rating scores, those systems are enriched with textual evaluations of items by the users. Neural language processing models, on the other hand, have already found application in recommender systems, mainly as a means of encoding user preference data, with the actual textual description of items serving only as side information. In this paper, a novel approach to incorporating the aforementioned models into the recommendation process is presented. Initially, a neural language processing model and more specifically the paragraph vector model is used to encode textual user reviews of variable length into feature vectors of fixed length. Subsequently this information is fused along with the rating scores in a probabilistic matrix factorization algorithm, based on maximum a-posteriori estimation. The resulting system, ParVecMF, is compared to a ratings' matrix factorization approach on a reference dataset. The obtained preliminary results on a set of two metrics are encouraging and may stimulate further research in this area.
SEIR and Regression Model based COVID-19 outbreak predictions in India
Authors: Gaurav Pandey, Poonam Chaudhary, Rajan Gupta, Saibal Pal
Date: 2020-04-01 00:00:00
Tasks: regression

COVID-19 pandemic has become a major threat to the country. Till date, well tested medication or antidote is not available to cure this disease. According to WHO reports, COVID-19 is a severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Analysis of this disease requires major attention by the Government to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. SEIR model and Regression model have been used for predictions based on the data collected from John Hopkins University repository in the time period of 30th January 2020 to 30th March 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0 which is the spread of the disease was calculated to be 2.02. Expected cases may rise between 5000-6000 in the next two weeks of time. This study will help the Government and doctors in preparing their plans for the next two weeks. Based on the predictions for short-term interval, these models can be tuned for forecasting in long-term intervals.
Orchestral: a lightweight framework for parallel simulations of cell-cell communication
Authors: Adrien Coulier, Andreas Hellander
Date: 2018-06-28 00:00:00
Tasks: Distributed Computing

We develop a modeling and simulation framework capable of massively parallel simulation of multicellular systems with spatially resolved stochastic kinetics in individual cells. By the use of operator-splitting we decouple the simulation of reaction-diffusion kinetics inside the cells from the simulation of molecular cell-cell interactions occurring on the boundaries between cells. This decoupling leverages the inherent scale separation in the underlying model to enable highly horizontally scalable parallel simulation, suitable for simulation on heterogeneous, distributed computing infrastructures such as public and private clouds. Thanks to its modular structure, our frameworks makes it possible to couple just any existing single-cell simulation software together with any cell signaling simulator. We exemplify the flexibility and scalability of the framework by using the popular single-cell simulation software eGFRD to construct and simulate a multicellular model of Notch-Delta signaling over OpenStack cloud infrastructure provided by the SNIC Science Cloud.
Mathematical Modeling of Epidemic Diseases; A Case Study of the COVID-19 Coronavirus
Authors: Reza Sameni
Date: 2020-03-25 00:00:00

The outbreak of the Coronavirus COVID-19 has taken the lives of several thousands worldwide and locked-out many countries and regions, with yet unpredictable global consequences. In this research we study the epidemic patterns of this virus, from a mathematical modeling perspective. The study is based on an extensions of the well-known susceptible-infected-recovered (SIR) family of compartmental models. It is shown how social measures such as distancing, regional lockdowns, quarantine and global public health vigilance, influence the model parameters, which can eventually change the mortality rates and active contaminated cases over time, in the real world. As with all mathematical models, the predictive ability of the model is limited by the accuracy of the available data and to the so-called level of abstraction used for modeling the problem. In order to provide the broader audience of researchers a better understanding of spreading patterns of epidemic diseases, a short introduction on biological systems modeling is also presented and the Matlab source codes for the simulations are provided online.
Control of ecological outcomes through deliberate parameter changes in a model of the gut microbiome
Authors: Zipeng Wang, Eric W. Jones, Joshua M. Mueller, Jean M. Carlson
Date: 2019-12-07 00:00:00
Tasks: Dimensionality Reduction

The generalized Lotka-Volterra (gLV) equations are a mathematical proxy for ecological dynamics. We focus on a gLV model of the gut microbiome, in which the evolution of the gut microbial state is determined in part by pairwise inter-species interaction parameters that encode environmentally-mediated resource competition between microbes. We develop an in silico method that controls the steady-state outcome of the system by adjusting these interaction parameters. This approach is confined to a bistable region of the gLV model. The two steady states of interest are idealized as either a "healthy" or "diseased" steady state of the gut microbiome. In this method, a dimensionality reduction technique called steady-state reduction (SSR) is first used to generate a two-dimensional (2D) gLV model that approximates the high-dimensional dynamics on the 2D subspace spanned by the two steady states. Then a bifurcation analysis of the 2D model analytically determines parameter modifications that drive a disease-prone initial condition to the healthy steady state. This parameter modification of the reduced 2D model guides parameter modifications of the original high-dimensional model, resulting in a change of steady-state outcome in the high-dimensional model. This control method, called SPARC (SSR-guided parameter change), bypasses the computational challenge of directly determining parameter modifications in the original high-dimensional system. SPARC could guide the development of indirect bacteriotherapies, which seek to change microbial compositions by deliberately modifying gut environmental variables such as gut acidity or macronutrient availability.
Testing Many Restrictions Under Heteroskedasticity
Authors: Stanislav Anatolyev, Mikkel Sølvsten
Date: 2020-03-16 00:00:00
Tasks: valid

We propose a hypothesis test that allows for many tested restrictions in a heteroskedastic linear regression model. The test compares the conventional F statistic to a critical value that corrects for many restrictions and conditional heteroskedasticity. This correction uses leave-one-out estimation to correctly center the critical value and leave-three-out estimation to appropriately scale it. The large sample properties of the test are established in an asymptotic framework where the number of tested restrictions may be fixed or may grow with the sample size, and can even be proportional to the number of observations. We show that the test is asymptotically valid and has non-trivial asymptotic power against the same local alternatives as the exact F test when the latter is valid. Simulations corroborate these theoretical findings and suggest excellent size control in moderately small samples, even under strong heteroskedasticity.
On Smoothing and Inference for Topic Models
Authors: Arthur Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh
Date: 2012-05-09 00:00:00
Tasks: Topic Models, Variational Inference

Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation, and this variety motivates the need for careful empirical comparisons. In this paper, we highlight the close connections between these approaches. We find that the main differences are attributable to the amount of smoothing applied to the counts. When the hyperparameters are optimized, the differences in performance among the algorithms diminish significantly. The ability of these algorithms to achieve solutions of comparable accuracy gives us the freedom to select computationally efficient approaches. Using the insights gained from this comparative study, we show how accurate topic models can be learned in several seconds on text corpora with thousands of documents.
A Statistical Analysis of the "Internal Linear Combination" Method in Problems of Signal Separation as in CMB Observations
Authors: R. Vio, P. Andreani
Date: 2008-06-03 00:00:00

AIMS: The separation of foreground contamination from cosmic microwave background (CMB) observations is one of the most challenging and important problem of digital signal processing in Cosmology. In literature, various techniques have been presented, but no general consensus about their real performances and properties has been reached. This is due to the characteristics of these techniques that have been studied essentially through numerical simulations based on semi-empirical models of the CMB and the Galactic foregrounds. Such models often have different level of sophistication and/or are based on different physical assumptions (e.g., the number of the Galactic components and the level of the noise). Hence, a reliable comparison is difficult. What actually is missing is a statistical analysis of the properties of the proposed methodologies. Here, we consider the "Internal Linear Combination" method (ILC) which, among the separation techniques, requires the smallest number of "a priori" assumptions. This feature is of particular interest in the context of the CMB polarization measurements at small angular scales where the lack of knowledge of the polarized backgrounds represents a serious limit. METHODS: The statistical characteristics of ILC are examined through an analytical approach and the basic conditions are fixed in a way to work satisfactorily. RESULTS: ILC provides satisfactory results only under rather restrictive conditions. This is a critical fact to take into consideration in planning the future ground-based observations (e.g., with ALMA) where, contrary to the satellite experiments, there is the possibility to have a certain control of the experimental conditions.
semantify.it, a Platform for Creation, Publication and Distribution of Semantic Annotations
Authors: Elias Kärle, Umutcan Şimşek, Dieter Fensel
Date: 2017-06-30 00:00:00

The application of semantic technologies to content on the web is, in many regards, important and urgent. Search engines, chatbots, intelligent personal assistants and other technologies increasingly rely on content published as semantic structured data. Yet, the process of creating this kind of data is still complicated and widely unknown. The semantify.it platform implements an approach to solve three of the most challenging question regarding the publication of structured semantic data, namely: a) what vocabulary to use, b) how to create annotation files and c) how to publish or integrate annotations within a website without programming. This paper presents the idea and the development of the semantify.it platform. It demonstrates that the creation process of semantically annotated data does not have to be hard, shows use cases and pilot users of the created software and presents where the development of this platform or alike projects lead to in the future.
Domain Specific Semantic Validation of Schema.org Annotations
Authors: Umutcan Şimşek, Elias Kärle, Omar Holzknecht, Dieter Fensel
Date: 2017-06-20 00:00:00

Since its unveiling in 2011, schema.org has become the de facto standard for publishing semantically described structured data on the web, typically in the form of web page annotations. The increasing adoption of schema.org facilitates the growth of the web of data, as well as the development of automated agents that operate on this data. Schema.org is a large heterogeneous vocabulary that covers many domains. This is obviously not a bug, but a feature, since schema.org aims to describe almost everything on the web, and the web is huge. However, the heterogeneity of schema.org may cause a side effect, which is the challenge of picking the right classes and properties for an annotation in a certain domain, as well as keeping the annotation semantically consistent. In this work, we introduce our rule based approach and an implementation of it for validating schema.org annotations from two aspects: (a) the completeness of the annotations in terms of a specified domain, (b) the semantic consistency of the values based on pre-defined rules. We demonstrate our approach in the tourism domain.
Machine Readable Web APIs with Schema.org Action Annotations
Authors: Umutcan Şimşek, Elias Kärle, Dieter Fensel
Date: 2018-05-14 00:00:00

The schema.org initiative led by the four major search engines curates a vocabulary for describing web content. The number of semantic annotations on the web are increasing, mostly due to the industrial incentives provided by those search engines. The annotations are not only consumed by search engines, but also by other automated agents like intelligent personal assistants (IPAs). However, only annotating data is not enough for automated agents to reach their full potential. Web APIs should be also annotated for automating service consumption, so the IPAs can complete tasks like booking a hotel room or buying a ticket for an event on the fly. Although there has been a vast amount of effort in the semantic web services field, the approaches did not gain too much adoption outside of academia, mainly due to lack of concrete incentives and steep learning curves. In this paper, we suggest a lightweight, bottom-up approach based on schema.org actions to annotate Web APIs. We analyse schema.org vocabulary in the scope of lightweight semantic web services literature and propose extensions where necessary. We show that schema.org actions could be a suitable vocabulary for Web API description. We demonstrate our work by annotating existing Web APIs of accommodation service providers. Additionally, we briefly demonstrate how these APIs can be used dynamically, for example, by a dialogue system.
Fast and Accurate Retrieval of Methane Concentration from Imaging Spectrometer Data Using Sparsity Prior
Authors: Markus D. Foote, Philip E. Dennison, Andrew K. Thorpe, David R. Thompson, Siraput Jongaramrungruang, Christian Frankenberg, Sarang C. Joshi
Date: 2020-03-06 00:00:00
Tasks: GPU, Retrieval

The strong radiative forcing by atmospheric methane has stimulated interest in identifying natural and anthropogenic sources of this potent greenhouse gas. Point sources are important targets for quantification, and anthropogenic targets have potential for emissions reduction. Methane point source plume detection and concentration retrieval have been previously demonstrated using data from the Airborne Visible InfraRed Imaging Spectrometer Next Generation (AVIRIS-NG). Current quantitative methods have tradeoffs between computational requirements and retrieval accuracy, creating obstacles for processing real-time data or large datasets from flight campaigns. We present a new computationally efficient algorithm that applies sparsity and an albedo correction to matched filter retrieval of trace gas concentration-pathlength. The new algorithm was tested using AVIRIS-NG data acquired over several point source plumes in Ahmedabad, India. The algorithm was validated using simulated AVIRIS-NG data including synthetic plumes of known methane concentration. Sparsity and albedo correction together reduced the root mean squared error of retrieved methane concentration-pathlength enhancement by 60.7% compared with a previous robust matched filter method. Background noise was reduced by a factor of 2.64. The new algorithm was able to process the entire 300 flightline 2016 AVIRIS-NG India campaign in just over 8 hours on a desktop computer with GPU acceleration.
Real-time UAV Complex Missions Leveraging Self-Adaptive Controller with Elastic Structure
Authors: Mohamad Abdul Hady, Basaran Bahadir Kocer, Harikumar Kandath, Mahardhika Pratama
Date: 2019-07-19 00:00:00

The expectation of unmanned air vehicles (UAVs) pushes the operation environment to narrow spaces, where the systems may fly very close to an object and perform an interaction. This phase brings the variation in UAV dynamics: thrust and drag coefficient of the propellers might change under different proximity. At the same time, UAVs may need to operate under external disturbances to follow time-based trajectories. Under these challenging conditions, a standard controller approach may not handle all missions with a fixed structure, where there may be a need to adjust its parameters for each different case. With these motivations, practical implementation and evaluation of an autonomous controller applied to a quadrotor UAV are proposed in this work. A self-adaptive controller based on a composite control scheme where a combination of sliding mode control (SMC) and evolving neuro-fuzzy control is used. The parameter vector of the neuro-fuzzy controller is updated adaptively based on the sliding surface of the SMC. The autonomous controller possesses a new elastic structure, where the number of fuzzy rules keeps growing or get pruned based on bias and variance balance. The interaction of the UAV is experimentally evaluated in real time considering the ground effect, ceiling effect and flight through a strong fan-generated wind while following time-based trajectories.
Identification of Port-Hamiltonian Systems from Frequency Response Data
Authors: Peter Benner, Pawan Goyal, Paul Van Dooren
Date: 2019-10-31 00:00:00

In this paper, we study the identification problem of a passive system from tangential interpolation data. We present a simple construction approach based on the Mayo-Antoulas generalized realization theory that automatically yields a port-Hamiltonian realization for every strictly passive system with simple spectral zeros. Furthermore, we discuss the construction of a frequency-limited port-Hamiltonian realization. We illustrate the proposed method by means of several examples.
Gray Level Co-Occurrence Matrices: Generalisation and Some New Features
Authors: Bino Sebastian V, A. Unnikrishnan, Kannan Balakrishnan
Date: 2012-05-22 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval, Texture Classification

Gray Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR.