User-specified Criteria Research Articles

Exploration of Chemical Space through Automated Reasoning.

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts. Comgen accepts a variety of user-specified criteria, converts these into an abstract form, and utilises a powerful automated reasoning algorithm to identify compositions that satisfy these user requirements, or prove that the requirements cannot be simultaneously satisfied. In contrast to machine learning techniques, explicitly reasoning about domain knowledge, rather than making inferences from data, ensures that Comgen's outputs are fully interpretable and provably correct. Users interact with Comgen through a high-level Python interface. We illustrate use of the tool with several case studies focused on the search for new ionic conductors. Further, we demonstrate the integration of Comgen into an end-to-end automated workflow to propose and evaluate candidate compositions quantitatively, prior to experimental investigation.

A Cross-Platform Movie Filtering and Recommendation System Using Big Data Analytics

One of the best forms of entertainment today is online multimedia with movies and various other entertainment sources at the tip of our fingers. However, finding a movie from the numerous options available in a movie database to suit a user's taste is a non-trivial task. Most recommendation systems do not consider the user's limitations, such as whether the user has the means to watch the movie suggested by the system. This paper proposes a system to provide recommendations that are platform specific. Two sources of movies have been considered, viz., Netflix and Amazon Prime, and other data sets can be included as well. In addition to providing recommendations, the proposed system helps filter movies based on user-specified criteria. It applies big data analytics for faster execution of both movie recommendation and data filtering and can work with large and distributed movie databases.

Decisive skyline queries for truly balancing multiple criteria

Skyline queries have emerged as an increasingly popular tool for identifying a set of interesting objects that balance different user-specified criteria. Although in several applications the user aims to detect data objects that have values as good as possible in all specified criteria, skyline queries fail to identify only those objects. Instead, objects whose values are good in a subset of the given criteria are also included in the skyline set, even though they may take arbitrarily bad values in the remaining criteria. To alleviate this shortcoming, we study the decisive subspaces that express the semantics of skyline points and determine skyline membership. We propose a novel alternative query, called decisive skyline query, which retrieves a set of points that balance all specified criteria. We study two variants of the proposed query, the strict variant, which retrieves only the subset of skyline points that have the full data space as decisive subspace, and the relaxed variant, which imposes the decisive semantics in a more flexible way. Furthermore, we present pruning properties that accelerate the process of finding the decisive skyline set. Capitalizing on these pruning properties, we propose a novel efficient algorithm for computing decisive skyline points. Our experimental study, which employs both synthetic and real data sets for various experimental setups, demonstrates the efficiency and effectiveness of our algorithm, and shows that the newly proposed query is more intuitive and informative for the user.

Linguistic Explanations of Black Box Deep Learning Detectors on Simulated Aerial Drone Imagery.

Deep learning has become increasingly common in aerial imagery analysis. As its use continues to grow, it is crucial that we understand and can explain its behavior. One eXplainable AI (XAI) approach is to generate linguistic summarizations of data and/or models. However, the number of summaries can increase significantly with the number of data attributes, posing a challenge. Herein, we proposed a hierarchical approach for generating and evaluating linguistic statements of black box deep learning models. Our approach scores and ranks statements according to user-specified criteria. A systematic process was outlined for the evaluation of an object detector on a low altitude aerial drone. A deep learning model trained on real imagery was evaluated on a photorealistic simulated dataset with known ground truth across different contexts. The effectiveness and versatility of our approach was demonstrated by showing tailored linguistic summaries for different user types. Ultimately, this process is an efficient human-centric way of identifying successes, shortcomings, and biases in data and deep learning models.

POInTbrowse: orthology prediction and synteny exploration for paleopolyploid genomes

We describe POInTbrowse, a web portal that gives access to the orthology inferences made for polyploid genomes with POInT, the Polyploidy Orthology Inference Tool. Ancient, or paleo-, polyploidy events are widely distributed across the eukaryotic phylogeny, and the combination of duplicated and lost duplicated genes that these polyploidies produce can confound the identification of orthologous genes between genomes. POInT uses conserved synteny and phylogenetic models to infer orthologous genes between genomes with a shared polyploidy. It also gives confidence estimates for those orthology inferences. POInTbrowse gives both graphical and query-based access to these inferences from 12 different polyploidy events, allowing users to visualize genomic regions produced by polyploidies and perform batch queries for each polyploidy event, downloading genes trees and coding sequences for orthologous genes meeting user-specified criteria. POInTbrowse and the associated data are online at https://wgd.statgen.ncsu.edu.

CRIB: A Novel Method for Device-Based Physical Behavior Analysis

Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette (https://github.com/paulhibbing/PBpatterns/blob/main/vignettes/CRIB.pdf) is available to assist users with implementing CRIB in R.

Astro-COLIBRI—The COincidence LIBrary for Real-time Inquiry for Multimessenger Astrophysics

Abstract Astro-COLIBRI is a novel tool that evaluates alerts of transient observations in real time, filters them by user-specified criteria, and puts them into their multiwavelength and multimessenger context. Through fast generation of an overview of persistent sources as well as transient events in the relevant phase space, Astro-COLIBRI contributes to an enhanced discovery potential of both serendipitous and follow-up observations of the transient sky. The software’s architecture comprises a Representational State Transfer Application Programming Interface, both a static and a real-time database, a cloud-based alert system, as well as a website and apps for iOS and Android as clients for users. The latter provide a graphical representation with a summary of the relevant data to allow for the fast identification of interesting phenomena along with an assessment of observing conditions at a large selection of observatories around the world.

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP

Abstract. This paper complements a series of now four publications that document the release of the Earth System Model Evaluation Tool (ESMValTool) v2.0. It describes new diagnostics on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) which are participating in the Coupled Model Intercomparison Project (CMIP). The second release of this tool aims to support the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). Furthermore, datasets from other models and observations can be analysed. The diagnostics for the hydrological cycle include several precipitation and drought indices, as well as hydroclimatic intensity and indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). The latter are also used for identification of extreme events, for impact assessment, and to project and characterize the risks and impacts of climate change for natural and socio-economic systems. Further impact assessment diagnostics are included to compute daily temperature ranges and capacity factors for wind and solar energy generation. Regional scales can be analysed with new diagnostics implemented for selected regions and stochastic downscaling. ESMValTool v2.0 also includes diagnostics to analyse large multi-model ensembles including grouping and selecting ensemble members by user-specified criteria. Here, we present examples for their capabilities based on the well-established CMIP Phase 5 (CMIP5) dataset.

AT 2020iko: A WZ Sge-type Dwarf Nova Candidate with an Anomalous Precursor Event

Abstract The ongoing Zwicky Transient Facility (ZTF) survey is generating a massive alert rate from a variety of optical transients and variable stars, which are being filtered down to subsets meeting user-specified criteria by broker systems such as the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES). In a beta implementation of the algorithm of Soraisam et al. on ANTARES, we flagged AT 2020iko from the ZTF real-time alert stream as an anomalous source. This source is located close to a red extended Sloan Digital Sky Survey source. In the first few epochs of detection, it exhibited a V-shaped brightness profile, preceded by nondetections both in ZTF and in the All-Sky Automated Survey for Supernovae extending to 2014. Its full light curve shows a precursor event, followed by a main superoutburst and at least two rebrightenings. A low-resolution spectrum of this source points to a dwarf nova (DN) nature. Although some of the features of AT 2020iko indicate an SU UMa-type DN, its large amplitude, presence of rebrightenings, and inferred supercycle period of ≥6 yr are in favor of AT 2020iko being a new WZ Sge-type DN candidate, a subset of rare DNe consisting of extreme mass-ratio (<0.1) binaries with an orbital period around the period minimum. The precusor event of AT 2020iko brightened by 6.5 mag, while its decay spanned 3–5 mag. We speculate this superoutburst is associated with a less expanded accretion disk than in typical superoutbursts in WZ Sge systems, with the large depth of the precursor decay implying an extremely small mass ratio. To the best of our knowledge, such a precursor event has not been recorded for any DN. This result serves to demonstrate the efficacy of our real-time anomaly search algorithm.

An adaptive IoT platform on budgeted 3G data plans

Abstract In this paper, we design and implement an Internet-of-Things (IoT) based platform for developing cities using environmental sensing as a driving application. Since ubiquitous and free WiFi access is not available in most developing cities, IoT deployments must leverage 3G cellular connections that are metered and expensive. In order to best utilize the limited 3G data plan, we propose two adaptation strategies to drive sensing and data collection. The first technique is an infrastructure-level adaptation approach where we adjust sensing intervals of periodic sensors so that the data volume remains bounded within the allocated data budget. The second approach is at the information-level where application-specific analytics are deployed on-board devices. This use case focuses on multimedia sensors that process captured raw media data to lower volume semantic data that is communicated. We implement the two adaptation strategies on the EnviroSCALE (Environmental Sensing and Community Alert Network) platform, which is an inexpensive Raspberry Pi based environmental sensing system that employs air quality sensors and periodically publishes sensor data over a 3G connection with a limited data plan. We outline our deployment experience of EnviroSCALE in Dhaka city, the capital of Bangladesh, particularly in the direction of infrastructure-level adaptation. For information-level adaptation, our testbed experiment results demonstrate the practicality of adaptive sensing and triggering rich sensing analytics via user-specified criteria over budgeted 3G connections.

Automatic polygon mesh repair and simplification for three-dimensional human modeling

We present a new approach to repair degenerated and self-intersected mesh of digitized human models, which automatically transforms a raw digitized mesh into a single manifold and closed triangle mesh. The algorithm interactively removes growing neighborhoods of undesired elements and fills resulted surface gaps to yield meshes which subject to user-specified quality criteria. We demonstrate our experimental results by multiple sets of low-quality original models with qualitative measurements compared with a number of existing methods. The results show that the proposed method produces high visual quality meshes with minimal changes of the original mesh appearances.

ScanGEO: parallel mining of high-throughput gene expression data

Current options to mine publicly available gene expression data deposited in NCBI's gene expression omnibus (GEO), such as the GEO web portal and related applications, are optimized to reanalyze a single study, or search for a single gene, and therefore require manual intervention to reanalyze multiple studies for user-specified gene sets. ScanGEO is a simple, user-friendly Shiny web application designed to identify differentially expressed genes across all GEO studies matching user-specified criteria, for a flexible set of genes, visualize results and provide summary statistics and other reports using a single command. The ScanGEO source code is written in R and implemented as a Shiny app that can be freely accessed at http://scangeo.dartmouth.edu/ScanGEO/. For users who would like to run a local instantiation of the app, the R source code is available under a GNU GPLv3 license at https://github.com/StantonLabDartmouth/AppScanGEO. katja.koeppen@dartmouth.edu. Supplementary data are available at Bioinformatics online.

Design and implementation of a mobile system for lung cancer patient follow-up in China and initial report of the ongoing patient registry.

IntroductionManagement of lung cancer remains a challenge. Although clinical and biological patient data are crucial for cancer research, these data may be missing from registries and clinical trials. Biobanks provide a source of high-quality biological material for clinical research; however, linking these samples to the corresponding patient and clinical data is technically challenging. We describe the mobile Lung Cancer Care system (mLCCare), a novel tool which integrates biological and clinical patient data into a single resource.MethodsmLCCare was developed as a mobile device application (app) and an internet website. Data storage is hosted on cloud servers, with the mobile app and website acting as a front-end to the system. mLCCare also facilitates communication with patients to remind them to take their medication and attend follow-up appointments.ResultsBetween January 2014 and October 2015, 5,080 patients with lung cancer have been registered with mLCCare. Data validation ensures all the patient information is of consistently high-quality. Patient cohorts can be constructed via user-specified criteria and data exported for statistical analysis by authorized investigators and collaborators. mLCCare forms the basis of establishing an ongoing lung cancer registry and could form the basis of a high-quality multisite patient registry. Integration of mLCCare with SMS messaging and WeChat functionality facilitates communication between physicians and patients.ConclusionIt is hoped that mLCCare will prove to be a powerful and widely used tool that will enhance both research and clinical practice.

An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty

An indirect simulation-optimization model framework with enhanced computational efficiency and risk-based decision-making capability was developed to determine optimal total maximum daily load (TMDL) allocation under uncertainty. To convert the traditional direct simulation-optimization model into our indirect equivalent model framework, we proposed a two-step strategy: (1) application of interval regression equations derived by a Bayesian recursive regression tree (BRRT v2) algorithm, which approximates the original hydrodynamic and water-quality simulation models and accurately quantifies the inherent nonlinear relationship between nutrient load reductions and the credible interval of algal biomass with a given confidence interval; and (2) incorporation of the calibrated interval regression equations into an uncertain optimization framework, which is further converted to our indirect equivalent framework by the enhanced-interval linear programming (EILP) method and provides approximate-optimal solutions at various risk levels. The proposed strategy was applied to the Swift Creek Reservoir’s nutrient TMDL allocation (Chesterfield County, VA) to identify the minimum nutrient load allocations required from eight sub-watersheds to ensure compliance with user-specified chlorophyll criteria. Our results indicated that the BRRT-EILP model could identify critical sub-watersheds faster than the traditional one and requires lower reduction of nutrient loadings compared to traditional stochastic simulation and trial-and-error (TAE) approaches. This suggests that our proposed framework performs better in optimal TMDL development compared to the traditional simulation-optimization models and provides extreme and non-extreme tradeoff analysis under uncertainty for risk-based decision making.

Optimization of Multiple-Rendezvous Low-Thrust Missions on General-Purpose Graphics Processing Units

A massively parallel method for the identification of optimal sequences of targets in multiple-rendezvous low-thrust missions is presented. Given a list of possible targets, a global search of sequences compatible with the mission requirements is performed. To estimate the feasibility of each transfer, a heuristic model based on Lambert’s transfers is evaluated in parallel for each target, making use of commonly available general-purpose graphics processing units such as the Nvidia Tesla cards. The resulting sequences are ranked by user-specified criteria such as length or fuel consumption. The resulting preliminary sequences are then optimized to a full low-thrust trajectory using classical methods for each leg. The performance of the method is discussed as a function of various parameters of the algorithm. The efficiency of the general-purpose graphics processing unit implementation is demonstrated by comparing it with a traditional CPU-based branch-and-bound method. Finally, the algorithm is used to compute asteroid sequences used in a solution submitted to the seventh edition of the Global Trajectory Optimization Competition.

SEMERGY.net: automatically identifying and optimizing energy-efficient building designs

A central goal of European and national climate and energy programs is to reduce the greenhouse gas emission of buildings. SEMERGY is a web-based optimization environment, which supports users in decision-making regarding energy-efficient building designs. Taking the user-specified criteria investment costs, final energy demand of the building, and environmental impact of used building products into account, the system identifies efficient building designs and retrofit options. The following steps were taken: (i) development of a comprehensive building data model, (ii) creation of an ontology of linked building product data, (iii) development of a rule-based system to automatically identify valid construction alternatives for building components, (iv) development of a multi-objective optimization procedure, and (v) creation of a web-based Graphical User Interface to enable data entry and user interaction. The present contribution provides an overview of the progress made in the above mentioned domains.

Mineral Classification Using Computer-Controlled Scanning Electron Microscopy

Computer-controlled scanning electron microscopy (CC-SEM) is an automated technique combining electron microscopy, image analysis, and X-ray spectroscopy to rapidly acquire morphological and chemical information for thousands of individual particles. Measurements are typically performed on polished cross-sections of material embedded in epoxy mounts. Individual particles are first identified from the epoxy matrix based on user-specified criteria (e.g., pre-set grayscale threshold from a representative Back-Scattered Electron image). Following identification, morphologic parameters (e.g., area, perimeter, aspect ratio, etc.) and an X-ray spectrum (EDS) are recorded for each particle. Multiple fields of view can be analyzed, resulting in many thousands of individual particle analyses collected during a single CC-SEM session. The resulting data can then be mined for useful information like particle size distributions (PSDs), phase proportions, and even bulk chemistry.

Filtergraph: An interactive web application for visualization of astronomy datasets

Abstract Filtergraph is a web application being developed and maintained by the Vanderbilt Initiative in Data-intensive Astrophysics (VIDA) to flexibly and rapidly visualize a large variety of astronomy datasets of various formats and sizes. The user loads a flat-file dataset into Filtergraph which automatically generates an interactive data portal that can be easily shared with others. From this portal, the user can immediately generate scatter plots of up to five dimensions as well as histograms and tables based on the dataset. Key features of the portal include intuitive controls with auto-completed variable names, the ability to filter the data in real time through user-specified criteria, the ability to select data by dragging on the screen, and the ability to perform arithmetic operations on the data in real time. To enable seamless data visualization and exploration, changes are quickly rendered on screen and visualizations can be exported as high quality graphics files. The application is optimized for speed in the context of large datasets: for instance, a plot generated from a stellar database of 3.1 million entries renders in less than 2 s on a standard web server platform. This web application has been created using the Web2py web framework based on the Python programming language. Filtergraph is free to use at http://filtergraph.vanderbilt.edu/ .

Procedural meta-models for architectural design praxis

This article discusses a procedure for the exploration of options in preliminary design. The procedure is based on the application of morphing procedures, which are typical of animation software, to building parametric analyses. The procedure is based on partially overlapping sequences of evaluations targeted on dynamic ad-hoc test-models and is aimed at the creation of data fields for the representation of the performance consequences of competing design sceneries. This representation is necessarily multidimensional and is based on parallel coordinates plots. The implementation of a specific test procedure of the kind described above is also discussed. The procedure has been supported by the use of contemporary analytical and representational systems and tools; namely, ESP-r, Radiance, Ggobi, and an extensible tool for the dynamic morphing of models through user-specified criteria, named OPTS, by the author.

AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery.

AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier maps.