「nsf」の検索結果

Expert-level mass transfer covering molecular diffusion, convective mass transfer, interphase transfer, mass transfer coefficients, and design of mass transfer equipment.

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。

Guide for obtaining explicit consent for international data transfers under GDPR Article 49(1)(a). Covers informed consent requirements including risks of transfers without adequacy decisions or appropriate safeguards, specific destination country disclosure, and the narrow scope of derogation-based transfers.

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。

After the learner demonstrates understanding of a concept, present near-transfer and far-transfer challenges. Use to test whether learning is portable or task-specific — this is what separates understanding from familiarity.

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 the post-Schrems II Transfer Impact Assessment process following EDPB Recommendations 01/2020 six-step methodology. Covers assessment of third country legal frameworks, supplementary measures evaluation, and TIA scoring. Activate for international data transfers, SCCs, third-country adequacy, or Chapter V compliance. Keywords: TIA, Schrems II, transfer assessment, EDPB, supplementary measures, SCCs, international transfer.

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 implementation of UK international data transfer mechanisms post-Brexit including the International Data Transfer Agreement (IDTA), UK Addendum to EU SCCs, UK adequacy assessments, and ICO transfer risk assessment tool. Keywords: UK IDTA, UK addendum, ICO TRA, post-Brexit transfers, UK GDPR.

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。

After the learner demonstrates understanding of a concept, present near-transfer and far-transfer challenges. Use to test whether learning is portable or task-specific — this is what separates understanding from familiarity.

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。

"Apply the three-level framework of digital transformation — Digitization, Digitalization, and Digital Transformation — to diagnose and plan organizational change enabled by digital technologies. Use this skill when the user needs to assess an organization's digital maturity, distinguish between automating processes versus transforming business models, plan a DX roadmap, or when they ask 'where are we on digital transformation', 'is this digitization or real transformation', or 'how do we build a DX strategy'.".

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 re-assigning record OwnerId across many records — territory realignment, employee departure, region split, integration cleanup. Triggers: 'mass transfer accounts', 'reassign opportunities to new owner', 'transfer all records on user deactivation', 'OwnerId migration', 'mass transfer records tool', 'bulk owner change', 'transfer before deactivating user', 'account team lost after owner change', 'assignment rule overrode my OwnerId column', 'defer sharing calculations', 'roll back an owner transfer'. NOT for auto-assigning new records — use admin/assignment-rules. NOT for territory assignment data — use data/territory-data-alignment.

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。

長い入力を扱うTransformerの学習・推論で、注意機構の計算を高速化し、GPUメモリ使用量を抑えるスキル。導入から性能測定、出力の比較まで案内します。

• 長い入力での学習を高速化したいとき
• 長文推論のメモリ使用量を抑えたいとき
• 処理速度とモデル出力の比較

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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 systematic mapping of international personal data flows across an organisation. Covers system-by-system inventory methodology, third-party identification, transfer mechanism assignment, gap analysis, and data flow visualisation. Keywords: data flow mapping, international transfers, data inventory, transfer register, cross-border data flows.

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。

Generate a state document (context transfer) that allows any fresh AI instance to resume work without conversation history. Use this skill whenever the user asks to checkpoint, create a state transfer, save state, generate a handoff document, create compaction insurance, or says anything like 'snapshot this', 'create a context transfer', 'checkpoint', 'save state for handoff', 'write a recovery doc', or 'transfer context'. Also trigger when the user mentions compaction concerns, session handoff, multi-session continuity, or wants to preserve the current conversation's state. Works for any conversation type: projects, research, debugging, investigations, design sessions, or long-running chats. This skill produces a structured markdown document optimized for AI cold-start recovery, not human notes.

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。

Processes LP interest transfer requests with GP consent evaluation, ROFR administration, and transfer documentation requirements. Use when managing LP transfers, evaluating transfer requests, or coordinating interest assignments.

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。

Navigates LP transfer requirements including GP consent, ROFR compliance, and partnership agreement transfer provisions. Use when managing LP transfers, obtaining GP consent, or navigating transfer restrictions.

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。

Expert-level heat transfer covering conduction, convection, radiation, heat exchangers, fins, transient heat transfer, and computational heat transfer.

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。

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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。

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

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。

Draft a structured grant proposal from research ideas and literature. Supports KAKENHI (Japan), NSF (US), NSFC (China, including 面上/青年/优青/杰青/海外优青/重点), ERC (EU), DFG (Germany), SNSF (Switzerland), ARC (Australia), NWO (Netherlands), and generic formats. Use when user says "write grant", "grant proposal", "申請書", "write KAKENHI", "科研費", "基金申请", "写基金", "NSF proposal", or wants to turn research ideas into a funding application.

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。

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

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。

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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。

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

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。

Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for 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。

当用户明确要求"标书QC/质量控制/润色前质检/引用真伪核查/篇幅与结构检查"时使用。对 NSFC 标书进行只读质量控制：并行多线程独立检查文风生硬、引用假引/错引风险、篇幅与章节分布、逻辑清晰度等，最终输出标准化 QC 报告；中间文件默认归档到 `project_root/.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/nsfc-qc/{yyyy-mm-dd-hh-mm}/`，并兼容 legacy `.nsfc-qc/` 读取/清理。

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。

当用户明确要求"写/润色 NSFC 标书摘要""生成中文摘要和英文摘要""把中文摘要翻译成英文摘要"时使用。输出中文、英文两个版本（英文必须是中文的忠实翻译版），同时输出标题建议（1个推荐标题+5个候选标题及理由）。中文摘要默认≤400字符，英文摘要默认≤4000字符。输出方式：将结果写入工作目录下的 `NSFC-ABSTRACTS.md`。⚠️ 不适用：用户只想翻译一段与标书无关的通用文本（应直接翻译）；用户只想写立项依据/研究内容/研究基础正文（应使用对应 nsfc 系列 skill）。

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。