「sampling」の検索結果

OpenTelemetry setup, span context propagation, sampling strategies, Jaeger queries

日本語の概要は準備中です。原文の説明を表示しています。

Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.

日本語の概要は準備中です。原文の説明を表示しています。

Use when you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors. This should trigger for requests such as Improve tracing; Apply OpenTelemetry tracing; Add distributed tracing; Refactor tracing instrumentation; Instrument Java services with OpenTelemetry spans. Part of Plinth Toolkit

日本語の概要は準備中です。原文の説明を表示しています。

Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging

日本語の概要は準備中です。原文の説明を表示しています。

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

日本語の概要は準備中です。原文の説明を表示しています。

Constraint-based (COBRA) analysis of genome-scale metabolic models: FBA, FVA, knockouts, flux sampling, production envelopes, gapfilling, media optimization. Use for strain design, essential gene ID, flux analysis. For kinetic modeling use tellurium; for visualization use Escher.

日本語の概要は準備中です。原文の説明を表示しています。

PyTorch Geometric (PyG) for graph neural networks: node/graph classification, link prediction with GCN, GAT, GraphSAGE, GIN. Message passing, mini-batches, heterogeneous graphs, neighbor sampling, explainability. Supports molecules (QM9, MoleculeNet), social/knowledge graphs, 3D point clouds. For non-graph DL use PyTorch; for classical graph algorithms use NetworkX.

日本語の概要は準備中です。原文の説明を表示しています。

Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.

日本語の概要は準備中です。原文の説明を表示しています。

Use DPM-Solver and DPM-Solver++ single-file samplers for diffusion ODE sampling, PyTorch/JAX integration, ScoreSDE/DDPM examples, and Stable Diffusion acceleration.

日本語の概要は準備中です。原文の説明を表示しています。

Guides CUDA-based BigGAN-PyTorch training, checkpoint sampling, dataset preparation, metric preparation, model customization, and optional TensorFlow Hub weight conversion.

日本語の概要は準備中です。原文の説明を表示しています。

Use audio-diffusion-pytorch for PyTorch waveform diffusion generators, text-conditioned audio generation, inpainting, upsampling, vocoding, and diffusion autoencoding.

日本語の概要は準備中です。原文の説明を表示しています。

Router for Alpamayo R1 multimodal driving inference and trajectory sampling.

日本語の概要は準備中です。原文の説明を表示しています。

IDOR / BOLA hunting - two-account methodology, identifier discovery and UUID leak chaining, the trusted-identifier test, GraphQL node and nested-object IDOR, cross-tenant escalation, write and delete operations. Bounded ID sampling, never range sweeps. Wiki-first, FIND schema output. Trigger on IDOR, BOLA, broken access control, object level authorization, cross-tenant, "read another user's data", "swap the id", or any API path or parameter carrying a numeric ID, UUID, or account identifier.

日本語の概要は準備中です。原文の説明を表示しています。

Blender rendering with Cycles and Eevee including sampling, denoising, camera setup, render passes, color management, and render optimization. Use for final output and look validation via MCP.

日本語の概要は準備中です。原文の説明を表示しています。

Create original short videos using the 事故悬停｜主动取样后独自离场 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Medical Geneticist. Use when a task calls for Medical Geneticist judgment. Reasons from pedigree priors, HPO phenotype match, and ACMG/ClinGen variant classification; integrates exome/genome, CMA, RNA splicing, NBS ACT pathways, Tier 3 carrier screening, SF v3.3, and CPIC pharmacogenomics while treating VUS overcall, CPM/NIPT discordance, mtDNA heteroplasmy sampling, and SpliceAI-only splicing claims as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Mammalogist. Use when a task calls for Mammalogist judgment. Reasons from mammalian life history and detectability-limited sampling through ASM MDD taxonomy, Sherman/camera-trap/SCR survey design, occupancy and SECR models, bat acoustic validation, and museum voucher discipline while treating trap heterogeneity, camera autocorrelation, closure violation, and WNS decontamination gaps as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Hydrogeologist. Use when a task calls for Hydrogeologist judgment. Reasons from Darcy's law, mass conservation, aquifer heterogeneity, and coupled biogeochemical transport through Theis and Cooper-Jacob aquifer-test analysis, MODFLOW/MT3DMS modeling with PEST uncertainty, and low-flow geochemical sampling while treating wellbore-storage and skin artifacts, equivalent-porous-medium failure in fractured/karst flow, and non-unique K-S calibration as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Herpetologist. Use when a task calls for Herpetologist judgment. Reasons from detectability-limited sampling and ectotherm phenology through VES, pitfall/drift-fence and cover-board arrays, NAAMP call surveys, Program MARK CJS, unmarked/occuTTD occupancy, and Bd/Bsal MW113 qPCR biosecurity; uses AmphibiaWeb, Reptile Database, SSAR 9th ed./CNAH names, Amphibian Disease Portal, GBIF/CoordinateCleaner, and ARRIVE 2.0 while treating weather bias, pitfall selectivity, and pathogen cross-contamination as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Industrial Engineer. Use when a task calls for Industrial Engineer judgment. Reasons from takt, Little's Law, and ρ-stable queueing (M/M/c, Erlang C) through DMAIC/DMADV, VSM, line balancing, SLP/ALDEP/CRAFT layout, MTM/MOST, work sampling, RNLE/RULA/REBA, ISO 22400 OEE, and Arena/AnyLogic DES V&V; treats simulation warm-up/replication gaps, CRAFT non-contiguity, and OEE-without-takt red herrings as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Entomologist. Use when a task calls for Entomologist judgment. Reasons from tagmata, Comstock-Needham venation, and tarsal formula through trap-guild sampling (Malaise, pitfall, pan, light), host–parasitoid ecology, ICZN vouchers and genitalia keys, BOLD/GBIF/COL/iNaturalist triage, IUCN invertebrate caveats, CITES/COSE permits, Taylor/GLMM on the correct EU, and EIL/ET with IRAC MoA rotation while treating teneral, dimorphic, and cryptic mis-IDs as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Food Microbiologist. Use when a task calls for Food Microbiologist judgment. Reasons from food as a hurdle-governed matrix of water activity, pH, and redox through BAM/ISO reference methods, c/n/m/M sampling plans, PMA-v-qPCR, and ComBase kinetics while treating VBNC and injured cells, post-process contamination, matrix inhibition, and unconfirmed PCR hits as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Conservation Biologist. Use when a task calls for Conservation Biologist judgment. Reasons from IUCN Red List A–E and Green Status recovery metrics, PVA/Ne, occupancy and distance sampling (unmarked, msocc, RMark), prioritizr/Marxan SCP, Conservation Evidence and ROSES synthesis, counterfactual impact evaluation, METT/SMART PAME, and eDNA false-positive models while treating pseudoreplication, GBIF effort bias, offset baselines, and Red List≠priority conflation as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Computational Social Scientist. Use when a task calls for Computational Social Scientist judgment. Reasons from social mechanisms, measurement validity, and sampling frames through DAGs, fixed-effects and IV/DiD/RDD designs, ERGM/SAOM network models, and human-audited text classifiers while treating unobserved homophily, network interference and SUTVA violations, platform-driven selection, bot contamination, and digital-skew unrepresentativeness as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。