The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

We have generated sets of the problem instances obtained by using different pseudo-random methods to generate the graphs. The order and the size of an instances were generated randomly using function random() within the respective ranges. Each new edge was added in between two yet non-adjacent vertices randomly until the corresponding size was attained.

Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.

Question Paper Solutions of chapter Graphs and Trees of Discrete Mathematics, 4th Semester , Computer Science and Engineering

Code used to simulate a hybrid computing platform (CPUs + GPUs) to test several online algorithms (that discover the task graph as it is unveiled) and to compare them to the classical HEFT offline scheduler.

The size of the Discrete Graphics Chip market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

The size of the Functional Discrete Graphics Card market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

This Excel table contains a detailed example of a graph-theoretic model used in the specification of the physical topology and network of the modeled system.

The size of the High Performance Discrete Graphics Card market was valued at USD 59120 million in 2023 and is projected to reach USD 158220.11 million by 2032, with an expected CAGR of 15.1% during the forecast period.

This repository contains raw data generated for use in Rumpf and Kaul 2021 (referenced below), and includes sets of files for defining transit networks as well as raw data tables generated by the solution algorithm. See the included README for an in-depth explanation of each data set.

The main focus of the study was to develop and test a public transit design model for improving equity of access to social services throughout a city. The main case study was based on the Chicago Transit Authority network, with the goal of making minor alterations to the bus fleet assignments in order to improve equity of access to primary health care facilities. A small-scale artificial network was also generated for use in sensitivity analysis.

The data sets in this repository include network files used by our hybrid tabu search/simulated annealing solution algorithm in order to solve the social access maximization problem (see the GitHub repository referenced below). Also included are the raw data tables from the CTA and artificial network trial sets.

The results of this study indicate that it is indeed possible to significantly increase social service access levels in the least advantaged areas of a community while still guaranteeing that transit service remains near its current level. While improving the access in some areas does require that other areas lose some access, the gains are generally much greater than the losses. Moreover, the losses tend to occur in the areas that already enjoy the greatest levels of access, with the net result being a more even distribution of accessibility levels throughout the city.

This dataset contains selected results of rigorous numerical computations conducted in the framework of the research described in the paper “Topological-numerical analysis of a two-dimensional discrete neuron model” by Paweł Pilarczyk, Justyna Signerska-Rynkowska and Grzegorz Graff. A preprint of this paper is available at https://doi.org/10.48550/arXiv.2209.03443.

Network of 27 papers and 37 citation links related to "Analysis of radiation and corn borer data using discrete Poisson Xrama distribution".

Dataset for the paper Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization, Martin Heistermann, Jethro Warnett, David Bommes, ACM Transactions on Graphics, Volume 42, Issue 4 (2023), DOI 10.1145/3592437

Both archive files (.zip and .tar.zst) have identical contents.

Contents:

• Results of running various algorithmus variants on the "300" dataset
  • Intermediate data (e.g., patch decompositions)
  • Final meshes
  • Logfiles
  • Metadata in json format (e.g., utilized settings, achieved energies, detailed runtimes)
• Meshes used in all figures

This data release includes water-quality data collected at 38 sites in central and eastern Massachusetts from April 2018 through May 2019 by the U.S. Geological Survey to support the implementation of site-dependent aluminum criteria for Massachusetts waters. Samples of effluent and receiving surface waters were collected monthly at four wastewater-treatment facilities (WWTFs) and seven water-treatment facilities (WTFs) (see SWQ_data_and_instantaneous_CMC_CCC_values.txt). The measured properties and constituents include pH, hardness, and filtered (dissolved) organic carbon, which are required inputs to the U.S. Environmental Protection Agency's Aluminum Criteria Calculator version 2.0. Outputs from the Aluminum Criteria Calculator are also provided in that file; these outputs consist of acute (Criterion Maximum Concentration, CMC) and chronic (Criterion Continuous Concentration, CCC) instantaneous water-quality values for total recoverable aluminum, calculated for monthly samples at selected ambient sites near each of the 11 facilities. Quality-control data from blank, replicate, and spike samples are provided (see SWQ_QC_data.txt). In addition to data tables, the data release includes time-series graphs of the discrete water-quality data (see SWQ_plot_discrete_all.zip). For pH, time-series graphs also are provided showing pH from the discrete monthly water-quality samples as well as near-continuous pH measured at one surface-water site at each facility (see SWQ_plot_contin_discrete_pH.zip). The near-continuous pH data, along with all of the discrete water-quality data except the quality-control data, are also available online from the U.S. Geological Survey's National Water Information System (NWIS) database (https://nwis.waterdata.usgs.gov/nwis).

Network of 31 papers and 48 citation links related to "NONLINEAR SAMPLED-DATA OBSERVER DESIGN VIA APPROXIMATE DISCRETE-TIME MODELS AND EMULATION".

The Functional Discrete Graphics Card market has become an integral part of the technology landscape, powering an array of applications from gaming and professional design to artificial intelligence and high-performance computing. These specialized components offer enhanced graphical performance and superior process

The global Discrete Graphics Card market is poised for robust expansion, projected to reach a substantial $15,460 million by 2025, with a Compound Annual Growth Rate (CAGR) of 4.5% expected to drive sustained momentum through 2033. This growth is fueled by a confluence of factors, primarily the escalating demand for high-performance computing in both personal and commercial applications. The gaming sector, a perennial powerhouse, continues to push the boundaries of visual fidelity, necessitating increasingly powerful gaming graphics cards. Simultaneously, the burgeoning fields of artificial intelligence, machine learning, data science, and content creation are creating an unprecedented need for professional graphics cards capable of handling complex computational tasks. These evolving technological landscapes are not merely trends but fundamental shifts in how individuals and industries leverage computing power, directly translating into a sustained and significant demand for discrete graphics solutions. Beyond the core drivers of gaming and professional applications, the market is being shaped by several emerging trends. The increasing adoption of cloud gaming services, while potentially shifting some on-premise hardware demand, also necessitates powerful cloud infrastructure that relies heavily on discrete GPUs. Furthermore, advancements in AI-driven rendering techniques and the metaverse's potential to create immersive virtual experiences are poised to unlock new avenues for graphics card innovation and adoption. However, certain restraints could temper this growth. Supply chain disruptions, as witnessed in recent years, and the rising cost of manufacturing high-end components pose significant challenges. Additionally, the increasing efficiency of integrated graphics solutions, while not a direct replacement for discrete cards in demanding applications, could influence the adoption rates in lower-tier market segments. Navigating these dynamics will be crucial for stakeholders to capitalize on the substantial opportunities within the discrete graphics card market. This report offers a comprehensive analysis of the global Discrete Graphics Card market, spanning from 2019 to 2033. The study period encompasses historical data from 2019-2024, a base year of 2025, and an estimated year also in 2025, followed by a detailed forecast period from 2025-2033. We project the market to reach a significant valuation, with sales volumes expected in the hundreds of millions of units throughout the forecast period.

Using the User Manual included in the research paper, and the Graph Design Example file as a reference, the user enters or saves all the vertices and edges needed to specify the model of the system topography.

In the first quarter of 2022, Nvidia held a ** percent shipment share within the global PC discrete graphics processing unit (dGPU) market, whilst AMD held a share of ** percent. Intel recorded a share of **** percent of the dGPU market in the first quarter of 2022.