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56 results for “competition model”
CNF Encoded Isomorphic and Optimized Miters from Hardware Model Checking Competition 2012 Models
<p>These miter benchmarks are used in a paper on congruence closure in 2024.</p> <p>The miter AIGs in the [`aig`](aig) directory are the same as those used to<br>generate CNFs of miter benchmarks submitted to the SAT Challenge in 2012.<br>Some of them have regularly been used in the SAT Competitions since then.</p> <p>The AIGs are separated into two sets:</p> <p>- [`aig/iso`](aig/iso) miters of isomorphic circuits<br>- [`aig/opt`](aig/opt) miters of optimized versus original circuit</p> <p>The first set checks equivalence of one circuit with itself in the<br>[`aig/iso`](aig/iso) (isomorphic) sub-directory and second set contains<br>equivalence checking problems (aka miters) comparing the original circuit<br>with an optimized version in the [`aig/opt`](aig/opt) (optimized)<br>sub-directory. The optimized versions of the circuits were obtained in 2012<br>by the `dc2` script for [ABC](https://people.eecs.berkeley.edu/~alanmi/abc).<br>We did not re-run ABC with a newer version in order to make sure we have as<br>base-line in our experiments the same CNFs, as they are well-known, i.e., <br>they have been used in the SAT competitions for several years.</p> <p>Note that the original AIGER models have state elements (called "latches"<br>in AIGER terminology), but both the optimization with ABC as well as our<br>equivalence checking is purely combinational, by treating latches as<br>additional pseudo inputs, and next-state functions as output-functions. The<br>miters were accordingly generated (by the 2011 version) of `aigmiter` using<br>the `-c` option. For the same reason as for ABC explained above, we did not<br>rerun `aigmiter` either.</p> <p>We then encoded the two sets of AIGs into CNF in two different ways.<br>The first encoding just uses a simple Tseitin encoding, without<br>Plaisted-Greenbaum optimization, i.e., using the `--no-pg` option, as in 2012.<br>Even though we use a new version of `aigtocnf` in `src/aiger/aigtocnf.c` we<br>made sure that identical CNFs are generated for this first encoding variant<br>by checking that the MD5 sum of the (actually compressed) CNF files match.<br>This required to disable a newly introduced cone-of-influence (COI)<br>optimization with `--no-coi` which skips unreachable gates even when the<br>Plaisted-Greenbaum optimization is disabled.</p> <p>The second variant of the encoding detects XOR and ITE gates by pattern<br>matching and produces more concise CNFs by skipping internal AND gates<br>of detected XOR and ITE gates producing a direct encoding instead.</p> <p>This gives the following sets of CNFs;</p> <p>- [`cnf/ands/iso`](cnf/ands/iso) isomorphic circuits with ANDs only<br>- [`cnf/ands/opt`](cnf/ands/opt) optimized versus original with ANDs only<br>- [`cnf/xits/iso`](cnf/xits/isor) isomorphic circuits with ANDs, XORs, ITEs<br>- [`cnf/xits/opt`](cnf/xits/opt) optimized versus original with ANDs, XORs, ITEs</p> <p>All the CNFs in these directories have the same original file name of the<br>AIGER model to simplify run-time comparison. In `cnf/all` we further<br>provide links with qualified names to distinguish them.</p>
Model Counting Competition 2024: Submitted Solvers
<p>The dataset contains the submissions that have been evaluated in the Model Counting Competition 2024 on the tracks:</p> <ul> <li>Track 1 (Model Counting)</li> <li>Track 2 (Weighted Model Counting)</li> <li>Track 2b (Weighted Model Counting Bonus Track)</li> <li>Track 3 (Projected Model Counting)</li> <li>Track 4 (Projected Weighted Model Counting)</li> </ul> <p><br>Details will be made public in an upcoming report.</p> <p>We also refer to the competition website: https://mccompetition.org/</p>
Model Counting Competition 2024: Competition Instances
<div> </div> <div> <p>Instances for the Model Counting Competition 2024</p> <ul> <li>Track 1 (Model Counting)</li> <li>Track 2 (Weighted Model Counting)</li> <li>Track 2b (Weighted Model Counting - bonus track)</li> <li>Track 3 (Projected Model Counting)</li> <li>Track 4 (Projected Weighted Model Counting)</li> </ul> <p><br>The even instances (trackX_000.cnf, trackX_002.cnf, ...) were made public for all participants during the testing phase of the solvers, whereas the private instances (trackX_001.cnf, trackX_003.cnf, ...) were used for the final evaluation and disclosed after the submission. </p> <p>For details, we refer to <a href="https://mccompetition.org/past_iterations">https://mccompetition.org/past_iterations</a>.</p> <p>Instances originate from various publicly available data sets and submissions made after a call for benchmarks. The full instances from which we selected are available on Zenodo under </p> <ul> <li>Model Counting Competition 2020: Full Instance Set</li> <li>Model Counting Competition 2021: Full Instance Set</li> <li>Model Counting Competition 2022: Full Instance Set.</li> <li>Model Counting Competition 2023: Full Instance Set.</li> <li>Model Counting Competition 2024: Full Instance Set.</li> </ul> <p>Details will be made public in the upcoming report.</p> </div>
Model Counting Competition 2024: Full Instance Set
<p>The dataset contains all instances that the organizers of the competition received or collected during the preparation phase of the Model Counting Competition 2024. The dataset includes short benchmark descriptions (00_description.{pdf,txt}) by the submitters/collectors (00_authors.txt).</p> <p>For more details, we refer to the upcoming report.</p> <p>Contributors are listed in the dataset.</p> <p>[Version 2: We accidentally included Track2-4 instances instead of Track1 instances in Track1. We fixed this. Now, <span>mc2024-track1-mc_collected.tar</span> correctly contains the Track1 instances.]</p>
Plant competition models data
<p>Dataset from the density and distance experiments. </p>
CNF Encoded Isomorphic and Optimized Miters from Hardware Model Checking Competition 2020 Models
<p>From the Hardware Model Checking Competition 2020 we have collected 324<br>sequential model checking problems and for each generated an isomorphic and<br>an optimized miter. The isomorphic miters just compares two identical<br>copies while for the optimized miter one copy went through optimization<br>with ABC using the `dc2` command.</p> <p>The CNFs are generated with a new version of `aigtocnf` which detects<br>XOR and ITE gates in the AIGER circuit and if detected uses a more compact<br>encoding (4 clauses clauses instead of 9) for each detected gate.</p>
Dataset for Improved differential expression analysis of miRNA-seq data by modeling competition to be counted
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Data from: A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions
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Data from: Does trait-based joint species distribution modelling reveal the signature of competition in stream macroinvertebrate communities?
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Data from: Incorporating interspecific competition into species-distribution mapping by upward scaling of small-scale model projections to the landscape
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Dataset associated to "Unraveling the relative role of light and water competition between lianas and trees in tropical forests: A vegetation model analysis"
<p>This dataset includes all the codes necessary to reproduce the simulations presented in the paper "Unraveling the relative role of light and water competition between lianas and trees in tropical forests: A vegetation model analysis" published in Journal of Ecology. More precisely, it includes the version of the codes used to generate the results for the vegetation model (ED2), the analysis platform (PEcAn), as well as the meterological drivers (Drivers) and the initial conditions (IC) of the model and the priors and posterior parameter distributions.</p>
Data from: Sperm competition games: a general model for pre-copulatory male-male competition
Reproductive males face a trade-off between expenditure on pre-copulatory male-male competition – increasing the number of females that they secure as mates – and sperm competition – increasing their fertilisation success with those females. Previous sperm allocation models have focused on scramble competition in which males compete by searching for mates and the number of matings rises linearly with pre-copulatory expenditure. However, recent studies have emphasised contest competition involving pre-copulatory expenditure on armaments, where winning contests may be highly dependent on marginal increases in relative armament level. Here we develop a general model of sperm allocation that allows us to examine the effect of all forms of pre-copulatory competition on sperm allocation patterns. The model predicts that sperm allocation decreases if either the 'mate-competition loading', a, or the number of males competing for each mating, M, increases. Other predictions remain unchanged from previous models: (i) expenditure per ejaculate should increase and then decrease, and (ii) total post-copulatory expenditure should increase, as the level of sperm competition increases. A negative correlation between a and M is biologically plausible, and may buffer deviations from the previous models. There is some support for our predictions from comparative analyses across dung beetle species and frog populations.
Data from: Competition both drives and impedes diversification in a model adaptive radiation
Competitors are known to be important in governing the outcome of evolutionary diversification during an adaptive radiation, but the precise mechanisms by which they exert their effects remain elusive. Using the model adaptive radiation of Pseudomonas fluorescens, we show experimentally that the effect of competition on diversification of a focal lineage depends on both the strength of competition and the ability of the competitors to diversify. We provide evidence that the extent of diversification in the absence of interspecific competitors depends on the strength of resource competition. We also show that the presence of competitors can actually increase diversity by increasing interspecific resource competition. Competitors that themselves are able to diversify prevent diversification of the focal lineage by removing otherwise available ecological opportunities. These results suggest that the progress of an adaptive radiation depends ultimately on the strength of resource competition, an effect that can be exaggerated or impeded by the presence of competitors.
Data from: Modelling competition and dispersal in a statistical phylogeographic framework
Competition between organisms influences the processes governing the colonization of new habitats. As a consequence, species or populations arriving first at a suitable location may prevent secondary colonization. While adaptation to environmental variables (e.g., temperature, altitude, etc.) is essential, the presence or absence of certain species at a particular location often depends on whether or not competing species co-occur. For example, competition is thought to play an important role in structuring mammalian communities assembly. It can also explain spatial patterns of low genetic diversity following rapid colonization events or the "progression rule" displayed by phylogenies of species found on archipelagos. Despite the potential of competition to maintain populations in isolation, past quantitative analyses have largely ignored it because of the difficulty in designing adequate methods for assessing its impact. We present here a new model that integrates competition and dispersal into a Bayesian phylogeographic framework. Extensive simulations and analysis of real data show that our approach clearly outperforms the traditional Mantel test for detecting correlation between genetic and geographic distances. But most importantly, we demonstrate that competition can be detected with high sensitivity and specificity from the phylogenetic analysis of genetic variation in space.
Data from: Dynamic occupancy modeling reveals a hierarchy of competition among fishers, grey foxes, and ringtails
1. Determining how species coexist is critical for understanding functional diversity, niche partitioning and interspecific interactions. Identifying the direct and indirect interactions among sympatric carnivores that enable their coexistence are particularly important to elucidate because they are integral for maintaining ecosystem function. 2. We studied the effects of removing 9 fishers (Pekania pennanti) on their population dynamics and used this perturbation to elucidate the interspecific interactions among fishers, grey foxes (Urocyon cinereoargenteus), and ringtails (Bassariscus astutus). Grey foxes (family: Canidae) are likely to compete with fishers due to their similar body sizes and dietary overlap, and ringtails (family: Procyonidae), like fishers, are semi-arboreal species of conservation concern. We used spatial capture-recapture to investigate fisher population numbers and dynamic occupancy models that incorporated interspecific interactions to investigate the effects members of these species had on the colonization and persistence of each other's site occupancy. 3. The fisher population showed no change in density for up to three years following the removals of fishers for translocations. In contrast, fisher site occupancy decreased in the years immediately following the translocations. During this same time period, site occupancy by grey foxes increased and remained elevated through the end of the study. 4. We found a complicated hierarchy among fishers, foxes, and ringtails. Fishers affected grey fox site persistence negatively but had a positive effect on their colonization. Foxes had a positive effect on ringtail site colonization. Thus, fishers were the dominant small carnivore where present and negatively affected foxes directly and ringtails indirectly. 5. Coexistence among the small carnivores we studied appears to reflect dynamic spatial partitioning. Conservation and management efforts should investigate how intraguild interactions may influence the recolonization of carnivores to previously occupied landscapes.
Improving accuracy of breeding values by incorporating genomic information in spatial-competition mixed models
<p>Supplementary information of a <em>Eucalyptus grandis</em> population, genomic and pedigree data including identity information of trees, family, and provenance of the paper entitled: <strong>Improving accuracy of breeding values by incorporating genomic information in spatial-competition mixed models</strong>.</p> <p> </p>
Fig. 6 in Outcome of within-host competition demonstrates that parasite virulence doesn't equal success in a myxozoan model system
Fig. 6. Log fold change in immunogloblulin expression relative to controls on day 14 in intestine samples. Parasite copy numbers measured in intestine samples are overlaid on IgM intestine plot (parasite copy number data are the same for the IgT plot). Letters denote treatments that differed (Tukey's HSD tests, α = 0.05).
Fig. 5 in Outcome of within-host competition demonstrates that parasite virulence doesn't equal success in a myxozoan model system
Fig. 5. Log-foldchange in cytokine expression relative to controls at day-14 in spleen and intestine samples. Parasite copy numbers measured in gill tissues is overlaid on IFN-gamma spleen plot and parasite copy numbers measured in intestine samples are overlaid on IFN-gamma intestine plot, but parasite copy number data are the same for, and apply to, all cytokine plots underneath. Letters denote treatments that differed (Tukey's HDS tests, α = 0.05).
in vitro analysis of a competitive inhibition model for T7 RNA polymerase biosensors
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Data from: Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models
Animals and humans have a tendency to repeat recent choices, a phenomenon known as choice hysteresis. The mechanism for this choice bias remains unclear. Using an established, biophysically informed model of a competitive attractor network for decision making, we found that decaying tail activity from the previous trial caused choice hysteresis, especially during difficult trials, and accurately predicted human perceptual choices. In the model, choice variability could be directionally altered through amplification or dampening of post-trial activity decay through simulated depolarizing or hyperpolarizing network stimulation. An analogous intervention using transcranial direct current stimulation (tDCS) over left dorsolateral prefrontal cortex (dlPFC) yielded a close match between model predictions and experimental results: net soma depolarizing currents increased choice hysteresis, while hyperpolarizing currents suppressed it. Residual activity in competitive attractor networks within dlPFC may thus give rise to biases in perceptual choices, which can be directionally controlled through non-invasive brain stimulation.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.