「quant」の検索結果

Chemical equilibrium, chemical kinetics, and electrochemistry — the quantitative core of physical chemistry. Covers equilibrium constants (Keq, Kc, Kp), Le Chatelier's principle, ICE tables, buffers and the Henderson-Hasselbalch equation, solubility product (Ksp) and the common-ion effect, rate laws, reaction order, integrated rate laws, half-life, the Arrhenius equation and activation energy, catalysis, redox reactions, galvanic and electrolytic cells, standard cell potentials, and the Nernst equation. Use when solving quantitative problems about how far a reaction goes (equilibrium), how fast it goes (kinetics), or how it converts chemical energy to electrical energy (electrochemistry).

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Spectroscopy, chromatography, titration, elemental analysis, X-ray crystallography, and mass spectrometry for chemical identification and quantitation. Covers UV-Vis and IR spectroscopy, NMR fundamentals, gas and liquid chromatography, gravimetric and volumetric analysis, diffraction methods, separation techniques, and quantitative error analysis. Use when identifying unknown substances, determining purity, measuring concentrations, or interpreting analytical data.

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Condensed ANIMA on quantum-classical and classical-quantum networks. All skill compositions materialized as s-expressions across the polyglot substrate.

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Use when adding support for a new model architecture to imp, porting a model family, or debugging a model that loads but produces wrong output - "add support for <model>", "new arch", loader detection, chat template, tokenizer parity, RoPE variant, "outputs garbage", "prompt-blind", "digits scrambled", "NaN logits", "describes a different picture", "does it fit in VRAM". Do NOT use for kernel performance (sm120-cuda-expert) or quant-format questions (quant-formats).

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Count-based quantity takeoff and tag completeness auditing for construction drawings. Vision + OCR reconciliation, sheet markup, Excel QTO output. Triggers: 'tag audit', 'quantity takeoff', 'QTO', 'count fixtures'.

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Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.

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Aggregate DiT inference outputs into per-dimension VBench metrics via AISBench. Run after the quant-tuning-evaluate DiT workflow produces a directory of mp4s and the user supplies a VBench-1.0-mini JSON + pre-populated cache dir. Single mode (Standard).

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Append a DiT-tuning round to history.yaml. DiT-side counterpart of the LLM-path accuracy_append.py; records practice.md5, inference_outputs, fp_baseline_outputs, and the scoring fields (scores / overall_score / loss_vs_baseline / is_satisfied) populated by quant-tuning-score-dit. Idempotent on practice_id.

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提供 msModelSlim 的通用快速量化指引，包含安装、最简 YAML 配置与基础执行校验。适用于用户询问 msmodelslim 安装、快速量化、配置 yaml、linear_quant 或 minmax 基础参数时。

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Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.

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需給分析。信用残高（信用倍率）、空売り比率、投資部門別売買動向から需給バランスを総合判定する。JQuants API v2を使用。

Parse natural-language quantities, units, dates, and ranges ("5'11\"", "1.5 cups", "72 in to cm", "three days ago", "between 5 and 10 kg", "it's hot") into canonical, validated values with issue codes and spans — and humanize them back. Use when a form input, LLM tool schema, MCP handler, or import pipeline takes free-text measurements/units/dates. Covers Standard Schema fields (quantityField/dateField/lingoObject) for the AI SDK and MCP, the headless <lingo-input>, unit conversion, and format/humanize round-trips. Zero-dependency TypeScript, deterministic.

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Use when configuring the Salesforce product catalog and pricing structure: creating Product2 records, setting up Standard and custom Pricebooks, managing PricebookEntry junction records, enabling multi-currency pricing, configuring Product Schedules (Revenue and Quantity), or troubleshooting incorrect prices on Opportunity line items and Quotes. Triggers: 'add product to pricebook', 'pricebook entry', 'standard pricebook', 'product catalog', 'multi-currency product pricing', 'product schedule', 'revenue schedule', 'quantity schedule', 'deactivate product'. NOT for bulk catalog loads — use data/product-catalog-data-model. NOT for CPQ bundles and product rules — use admin/cpq-product-catalog-setup.

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Quantum physics expert making counterintuitive concepts accessible while maintaining mathematical rigor

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Iterate on a GenAI notebook against the self-hosted stack via the genai-stack CLI (config dirs, auth, subdomains, quantization, GPU/VRAM). Arguments: <notebook|service> [--service comfyui|forge|vllm] [--quant int4|fp8] [--validate] [--bg]

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Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when correcting CyTOF signal drift, harmonizing multi-batch or multi-site studies, or deciding whether to normalize data versus model batch in the design.

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Serve LLMs com alta throughput usando PagedAttention do vLLM e continuous batching. Use ao fazer deploy de APIs LLM em produção, otimizar latência/throughput de inferência, ou servir modelos com memória GPU limitada. Suporta endpoints compatíveis com OpenAI, quantização (GPTQ/AWQ/FP8) e tensor parallelism.

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Executa inferência de LLM em CPU, Apple Silicon e GPUs consumer sem hardware NVIDIA. Use para edge deployment, Macs M1/M2/M3, GPUs AMD/Intel ou quando CUDA não está disponível. Suporta quantização GGUF (1,5-8 bits) para redução de memória e aceleração de 4-10× vs PyTorch em CPU.

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標準原価差異分析スキル。標準原価（予定原価）と実際原価の差異を価格差異・数量差異に分解し、 材料費・労務費・製造間接費のカテゴリ別に集計・分析する。差異の有利/不利判定、 責任部門の特定、根本原因の仮説提示を行う。CSVデータのアップロードによる自動分析に対応。 Use when: 製造原価の差異分析を行いたいとき。標準原価計算制度の運用、 月次原価差異レポート作成、原価低減活動の効果測定に使用。 Triggers: "標準原価", "原価差異", "価格差異", "数量差異", "standard cost", "cost variance", "price variance", "quantity variance", "予定原価", "操業度差異", "原価管理"

Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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METAINFORMANT rules for directory src/metainformant/math/quantitative_genetics. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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Knowledge base from the FAA Human Factors Design Standard (HF-STD-001B, 2016) — the FAA's consolidated human-factors / human-systems-integration design criteria for systems it manages, operates, or maintains. Use for requirement-level design rules on: general human-factors design principles; automation and function allocation; designing equipment for maintenance; displays, controls, and visual indicators; alarms, audio, and voice communications; the computer-human interface (information presentation, coding, interaction styles, windows, dialogue); keyboards, input devices, and workstation/workplace ergonomics; system security, personnel safety, environment, anthropometry, and user documentation; plus intended-use and tailoring guidance. It is a quantified, source-attributed synthesis (heavily citing MIL-STD-1472G, MIL-HDBK-759C, DOD-HFDG-ATCCS, NUREG-0700, NASA-STD-3000, ANSI, OSHA, ISO 9241). Complements nasa-hsi as a parallel HSI tradition from the civil-aviation domain. Scope limits: it is design criteria, NOT an HSI lifecycle/process model (use nasa-hsi, nasa-npr-7123, or dau-se-guidebook for process); it does NOT reproduce the third-party standards it cites; it is FAA-acquisition-oriented (ATC, towers, TRACONs, airway facilities), not a general consumer-UX guide; and it is thin on modern touchscreen/mobile, web, and AI-driven interface patterns and on quantitative human-reliability analysis.

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Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.

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Model Quantization Guide: Run 70B LLMs in 4 Bits — … — 12. Okt. 2025 · Run 70B parameter LLMs on con

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