'ray data' 검색 결과

Build industrial-grade SCADA/HMI dashboards in MATLAB following industrial-HMI conventions (ISA-101-aligned): gray-field philosophy, alarms at source, write safeguards, fixed-range trends, drill-down layout. Produces a real App Designer app (.mlapp, or plain-text .m+.xml on R2026b+) by handing serialization to the matlab-build-app skill when available, and falls back to a programmatic .m app otherwise. Use when wrapping OPC UA / Modbus / MQTT / OSI PI / PI AF monitoring scripts into a live operator app, building plant overviews, designing operator dashboards, or any time a user asks for a "SCADA dashboard", "HMI", "plant dashboard", "operator screen", or "industrial monitoring app" in MATLAB. Trigger on: SCADA, HMI, industrial dashboard, plant overview, operator screen, uigauge, uilamp, alarm banner, gray-field, ISA-101, OPC UA dashboard, setpoint, write safeguards, alarm visualization, OSIsoft PI, AVEVA PI, PI Server, PI Data Archive, PI AF, PI Asset Framework, piclient, afclient.

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Use when building a Unity 6.3 level or open world: terrain from code or heightmaps, procedural islands, erosion, splat and foliage rules, trees and grass, ProBuilder graybox, level gym, modular kit and prefab placement, set dressing, landmarks and player guidance, the island's edge, spline roads, WFC, fog, additive scene streaming, or a world that hitches, pops in, leaks memory or runs slow on mobile. Keywords: Terrain, TerrainData, ProBuilder, blockout, graybox, modular kit, NavMesh, LoadSceneAsync, open world.

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The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of th

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Create, modify, debug, or review Windows Forms applications only when the request contains a concrete Windows Forms marker. USE FOR: WinForms, Windows Forms, System.Windows.Forms, WinForms Form or UserControl designer files (*.Designer.cs or *.Designer.vb), Visual Studio WinForms Designer, TableLayoutPanel, BindingSource, DataGridView, Control.InvokeAsync, component-tray ownership, or custom control serialization. DO NOT USE FOR: WPF or WPF XAML, including Window.InputBindings, KeyBinding, commands, or DataContext; .NET MAUI; Avalonia; or any request where none of the listed Windows Forms markers is present.

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Parallel/distributed computing for larger-than-RAM data. Components: DataFrames (parallel pandas), Arrays (parallel NumPy), Bags, Futures, Schedulers. Scales laptop to HPC cluster. For single-machine speed use polars; for out-of-core without cluster use vaex.

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Open-source bio-image data management. Use the omero-py client to connect to an OMERO server, retrieve images as numpy arrays, annotate with tags and key-value pairs, manage ROIs, and feed image data into Python analysis pipelines — programmatically, no GUI.

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Route DataFlow workflows for pipelines, text and document processing, serving, evaluation, and Ray acceleration.

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Data-Juicer repo router for local recipes, Ray recovery, and service/MCP workflows.

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Use for writing, reviewing, debugging, testing, or optimizing Python code that uses PyArrow arrays, schemas, Tables, RecordBatches, RecordBatchReaders, compute kernels, Dataset scans, Parquet, or Arrow IPC and C data interfaces. Trigger on schema drift, null/type semantics, chunking, batch streaming, predicate projection pushdown, conversion, and memory ownership. Do not use for pandas-only transformations, Polars-only expressions, generic SQL, Arrow Flight service design, C++/Rust Arrow, or Parquet work that does not use PyArrow.

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Author and maintain MANUAL and hybrid test cases and suites in TestRail, Xray (Jira), Zephyr Scale, and Qase. Covers test-case anatomy (title, preconditions, steps, expected results, test data), suite/section organization, bulk authoring from user stories and acceptance criteria, ambiguous-step linting, CSV/API import-export payloads per tool, requirement traceability and coverage gaps, review hygiene, and when a manual case should graduate to automation. Use when: "write a test case," "manual test case," "TestRail case," "Xray test," "Zephyr Scale case," "Qase case," "import CSV into TestRail," "lint these steps," "traceability report," "should this be automated." Not for: Generating automated TEST CODE — that is ai-test-generation. Sprint-level WHAT-to-test selection — that is test-planning. Related: ai-test-generation, test-planning, exploratory-testing, qa-project-context.

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Use when modifying, converting, or transforming structured data with dasel v3 — in-place mutations, format conversion, batch operations, array manipulation, object construction, and merge patterns across JSON, YAML, TOML, XML, CSV, HCL, INI

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Style every matplotlib figure like the Stata 18/19 default (stcolor) scheme — Arial embedded as TrueType, white background, recessive light-gray grid below the data, no top/right spines, unframed legends, and the validated blue/red/gray palette. Use whenever a task generates or restyles charts, plots, or figures for papers, reports, or slides, even if the user doesn't mention Stata — this is the house style for all publication figures. Also use when asked to make figures "look like Stata", match the stcolor scheme, or restyle existing matplotlib output.

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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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Implement Swift Codable models for JSON and property-list encoding and decoding with JSONDecoder, JSONEncoder, CodingKeys, and custom init(from:) or encode(to:). Use when parsing API responses, remapping keys, flattening nested JSON, handling date or data decoding strategies, decoding heterogeneous arrays, or integrating Codable with URLSession, SwiftData, or UserDefaults.

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Extract structured data from multiple invoice files in a single API call using an array schema.

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Rust 製 AI/ML ライブラリ fandhe-ai のリファレンス。Burn / candle / tch 非依存の from-scratch 実装。 tape(), tape_for, Tape, Gradients, Tensor, Var, LinearVars, autodiff, kernel fusion, FusionPlan, BackendOps, Device (Cpu / Cuda / Metal), DeviceParamStore, compat::array, compat::Sequential, nn::Module, optim (AdamW / GradScaler), data::DataLoader, ONNX import / export, safetensors, inference::generate / KvCache, guardrail / self-repair CLI。

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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.

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Locate CCSPlayer_ItemServices::SetWearables in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayer_ItemServices::SetWearables" (symbol CCSPlayer_ItemServices_SetWearables). VIRTUAL function, but WeaponPaints consumes it as a byte signature. Resolve the CCSPlayer_ItemServices vtable via RTTI to locate the slot, then generate a function-head byte signature of the resolved function. The body copies wearable item handles into the pawn's wearable array (m_Item wearables netvar array) and marks state changed. Trigger: CCSPlayer_ItemServices_SetWearables, CCSPlayer_ItemServices::SetWearables

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답변 평가를 활용하는 강화 학습으로 대규모 언어 모델을 추가 훈련하는 절차를 안내합니다. Megatron-LM과 SGLang을 결합해 데이터 준비, 병렬 실행, 맞춤형 생성을 다룹니다.

• 언어 모델의 GRPO 훈련 시작
• 답변 생성과 훈련을 동시에 실행
• 도구를 사용하는 에이전트 훈련

Set up reinforcement learning, which trains models using rewards, with verl. Follow workflows for preparing data, configuring GRPO or PPO training, scaling across GPUs, and addressing memory errors or unstable training.

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• Training a math reasoning model with GRPO
• Configuring PPO training with a critic model
• Scaling large model training across multiple nodes

Develop and modify the Handsontable wrapper for Vue 3 with guidance on component structure, settings updates, column configuration, and shared data references. It also explains the build order and tests used to check wrapper changes.

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• Fix settings updates in the Vue 3 wrapper
• Modify per-column configuration
• Investigate how array changes reach the grid

Guides migration of existing codebases to Rust without unsafe code, covering architecture, implementation, testing, and output comparisons. It measures performance before and after optimization and produces an interactive HTML report documenting results and unresolved differences.

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• Migrating a GitHub or local codebase to Rust
• Comparing original and migrated program outputs
• Measuring and optimizing a Rust port

Manage the KubeSphere ServiceMesh extension built on Istio, Kiali, and Jaeger. Install or remove it, check deployment status, and investigate problems with staged releases, proxy injection, service topology, traffic metrics, and tracing across application components.

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• Installing, upgrading, or removing ServiceMesh
• Investigating staged release routing problems
• Checking missing Istio proxy containers