「dali」の検索結果

DALI imperative dynamic mode (`nvidia.dali.experimental.dynamic`, ndd): use when working on ndd code or migrating pipelines; skip pipeline-only tasks.

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。

Multi-modal single-cell analysis with muon/MuData. Joint RNA+ATAC (10x Multiome), CITE-seq (RNA+protein), other multi-omics. MuData holds per-modality AnnData with shared obs. WNN joint embedding, per-modality preprocessing, MOFA factor analysis. Use scanpy-scrna-seq for single-modality RNA; use muon when combining 2+ omics from the same cells.

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。

Multi-Omics Factor Analysis (MOFA2) for unsupervised integration of multiple data modalities. Identifies shared and view-specific sources of variation. Use when integrating RNA-seq, proteomics, methylation, or other omics to discover latent factors driving biological variation across modalities.

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。

IDAPython and IDALib script reference for reverse engineering. Activate when the user needs to write IDAPython scripts in IDA, use IDALib for headless analysis, operate on IDB databases, debug with IDA, manipulate memory/registers, traverse functions/blocks/instructions, work with Hex-Rays decompiler API, handle obfuscation, or batch-process binaries.

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 evaluating portfolios through Ray Dalio-style economic machine, debt cycles, diversification, risk parity, and all-weather asset allocation.

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。

CEO (Chief Executive Officer) — 一家公司「最高操作员」的工艺, 与泛泛「领导力 / 管理学」不同: (a) 设定战略与凝结愿景 (Drucker 的「企业的经营理论」+ Porter 论定位 + Hamilton Helmer 的「七力」+ Andy Grove 的「战略转折点」+ Roger Martin 《Playing to Win》); (b) 资本配置是 CEO 单一最高杠杆的工作 (Buffett/Munger Berkshire 致股东信 + William Thorndike 《The Outsiders 商界局外人》论 8 位非常规 CEO + Michael Mauboussin 论 hurdle rate + Bezos 年度致股东信「invariants vs bets」+ Mark Leonard Constellation Software); (c) 组建与领导高管班子 (Patrick Lencioni 《团队的五大障碍》+ Ben Horowitz 《艰难的事》+ Reed Hastings 《No Rules Rules》人才密度 + Andy Grove HOP《高产出管理》+ Lou Gerstner IBM 转型 + Marshall Goldsmith); (d) 董事会与投资人关系 (Larcker & Tayan 《公司治理重要》+ Bill George 《True North》+ 正式 CEO-董事长 / CEO-独立董事 protocol + 维权投资人 playbook + 资本市场沟通); (e) 企业文化与价值观作为操作系统 (Netflix 文化手册 + Amazon Leadership Principles + Bridgewater 《Principles》Ray Dalio + Schein 论组织文化 + 任正非 灰度 + 张瑞敏 人单合一 + 稻盛和夫 阿米巴); (f) 高不确定下的高风险决策 (Bezos Type 1/Type 2 决策 + premortem Klein + Kahneman 论 executive bias + RAPID/DACI + base rates + 情景规划 + 反思 premortem); (g) 对内对外沟通 (创始人 / CEO 致股东信作为机构构建工具: Bezos / Buffett / Reed Hastings 备忘录 / 任正非 内部讲话 + Town Hall + 危机沟通 Tylenol/Boeing/Wells Fargo + 应对维权投资人); (h) 跨阶段规模化 (创业 0→1 / 扩张 1→100 / 成熟期掌舵 / 危机转型): Steve Blank + Brian Chesky founder-mode + Marc Andreessen + Pierre Lassonde + Lou Gerstner 转型; (i) 创始人 CEO vs 职业经理人 CEO 的差异 (Paul Graham 「Founder Mode」2024 + Brian Chesky + Reed Hastings (创始人→职业过渡) + BCG/McKinsey 的「manager mode」+ 两者各自代价 + 什么时候切换)。诚实处理: 幸存者偏差 (单个 CEO 的打法 ≠ 普适规律, 学术研究显示 CEO 个人方差约 ⅓, 约 70% 来自行业 / 时代 / 运气 / 资本结构), 创始人崇拜, 区分可复制工艺与不可移植个人魅力, 治理与利益相关者责任 (员工 / 股东 / 社会), 反对 CEO 神话与 Welch 退休金扭曲 / Theranos / WeWork / FTX / Uber-Kalanick 时代 / Musk 80h+ / 996 工时崇拜。覆盖 founder-CEO (Bezos/Hastings/黄仁勋/Chesky/Brin/任正非) 与职业 CEO (Nadella/Gerstner/Pichai/Mulally/Lafley)。不含: 泛泛管理理论 (商业书已覆盖, 本 skill 专谈 C-suite 这个具体岗位), 不含: 「领导力」作为个人成长自助 (Robbins/Tracy/Maxwell 励志体裁), 不含: 「如何创业」早期战术 — 一旦有员工 / 董事会 / 资本, CEO 的工艺才开始。 (CEO (Chief Executive Officer) — the craft of being the top operator of a company, distinct from generic 'leadership/management': (a) setting the strategy and crystallizing the vision (Drucker's 'theory of the business' + Porter on positioning + Hamilton Helmer's 7 powers + Andy Grove's strategic inflection points + Roger Martin Playing to Win); (b) capital allocation as the CEO's single highest-leverage job (Buffett/Munger Berkshire letters + William Thorndike 'The Outsiders' on 8 unconventional CEOs + Michael Mauboussin on hurdle rates + Bezos's annual shareholder letter approach to invariants vs bets + Mark Leonard Constellation Software); (c) building and leading the executive team (Patrick Lencioni 'Five Dysfunctions' + Ben Horowitz 'Hard Things About Hard Things' + Reed Hastings 'No Rules Rules' on talent density + Andy Grove HOP + Lou Gerstner IBM turnaround + Marshall Goldsmith); (d) board and investor relations (Larcker & Tayan 'Corporate Governance Matters' + Bill George 'True North' + the formal CEO-Chair / CEO-Lead Director protocols + activist investor playbooks + capital markets communication); (e) culture and values as operating system (Netflix culture deck + Amazon Leadership Principles + Bridgewater 'Principles' Ray Dalio + Schein on org culture + 任正非 Ren Zhengfei 灰度 + 张瑞敏 Zhang Ruimin 人单合一 + 稻盛和夫 阿米巴); (f) high-stakes decision-making under uncertainty (Bezos Type 1 vs Type 2 + premortem Klein + Kahneman 'Thinking Fast and Slow' for executive bias + RAPID/DACI + base rates + scenario planning + premortem); (g) internal and external communication (founder/CEO letters as institution-builders: Bezos shareholder letters / Buffett letters / Reed Hastings memos / 任正非 内部讲话 + Town Halls + crisis communication Tylenol/Boeing/Wells Fargo + activist responses); (h) cross-stage scaling (founder-CEO of 0→1 / scaling 1→100 / mature-stage stewardship / turnaround): Steve Blank + Brian Chesky founder-mode + Marc Andreessen + Pierre Lassonde mining vs steady-state + Lou Gerstner turnaround; (i) the founder-CEO vs professional-manager-CEO distinction (Paul Graham 'Founder Mod

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 a person through healing modalities including energy work (reiki, chakra balancing), herbal remedies, basic first aid stabilization, and holistic techniques (breathwork, visualization, body scan). AI coaches the practitioner through assessment triage, modality selection, energetic connection, remedy preparation, and integration. Use when a person describes a physical ailment or injury, reports energetic imbalance (fatigue, emotional stagnation), wants coaching through a holistic breathwork or visualization session, or needs post-meditation integration with directed healing attention.

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。

Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering skills.

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。

Turn ONE topic into a finished Vox-style paper-collage explainer / ad video, end to end on the Atlas Cloud API + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Use this whenever the user wants a "Vox style" video, a paper/torn-paper collage animation, a "motion collage", a narrated explainer or short ad built from AI-generated collage posters, a scrapbook-style tribute, or wants to turn a topic / product / person into a punchy narrated collage video — even if they don't say the word "Vox". Also use when reproducing Stav Zilber / rom1trs / Higgsfield-style collage ad workflows. Three input modalities: a topic (B-roll), a talking-head video (A-roll mode), or a single photo of a person/product anchored into the collage (C-roll mode). Triggers: "vox video", "collage video", "motion collage", "paper collage explainer", "make a collage ad", "turn this topic into a collage video", "turn my photo/this product shot into a collage video".

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。

Matches a user’s biomedical research direction, disease problem, study aim, data modality, and resource constraints to the most relevant recent algorithms and method papers. Always search real recent algorithm literature first, prioritize the last 12 months, expand to 1–3 years only when needed, and add canonical baselines only when necessary. Every formal algorithm recommendation must include the verified primary method paper, plus published downstream papers that actually cite/use the algorithm when such papers are found, with DOI when available. Never fabricate papers, algorithm names, authors, journals, years, DOI, PMID, links, or benchmark claims. If no directly verified algorithm paper is found, say so explicitly.

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。

Organizes the evidence and competitive landscape around a drug, target, or pathway by separating disease relevance, tractability, preclinical evidence, clinical evidence, modality fit, and crowding. Always map what is biologically supported, what is druggable, what has actually advanced, and what remains strategically open. Never confuse target relevance with druggability, preclinical activity with clinical promise, or narrative excitement with validated development maturity. Never fabricate references, trial status, approval status, company activity, or asset metadata.

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 designing or auditing the experiments of an ACM MM (ACM Multimedia) paper — matched baselines per modality, ablations that isolate the cross-modal fusion, user studies or QoE measurement where the claim is subjective, dataset and media licensing, and honest compute reporting, so evidence supports a multimedia claim.

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 deciding whether a project is a genuine ACM MM (ACM Multimedia) contribution rather than single-modality work, choosing a thematic area, and routing between ACM MM, CVPR/ICCV, ACL/EMNLP, ICMR, MMSys, NeurIPS/ICLR, and the ACM TOMM journal by finding the cross-modal or media-systems core of the contribution.

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。

Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.

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。

Align protein structures using Foldseek 3Di, TM-align, US-align, DALI, or Foldmason for structural MSA. Predict, score, and superpose backbone coordinates when sequence identity is below the twilight zone or remote-homology detection is required. Use when sequence MSA fails (<25% identity), when the dark proteome is the target, when AlphaFoldDB / ESM Atlas search is needed, or when structural superposition is the goal.

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 engineering or selecting the best features for single-response classification or regression in MATLAB, whatever the data's modality — for non-tabular data it routes extraction to a domain skill, then selects, assesses, and delivers on the resulting table. Not for multi-response problems, model training, or raw data acquisition.

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。

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positi

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 a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including

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。

Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stoc

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。

Medical image segmentation with nnU-Net's self-configuring framework — auto-selects architecture, preprocessing, training for any modality. CT, MRI, microscopy, ultrasound in 2D, 3D full-res, 3D low-res, cascade. Pipeline: convert → plan/preprocess → train (5-fold CV) → best config → predict → ensemble. Use when classical segmentation fails and annotated data exists.

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。

Register, segment, filter, resample 3D medical images (MRI, CT, microscopy) via SimpleITK Python; DICOM, NIfTI, multi-modal. Rigid/affine/deformable registration, threshold/region-growing segmentation, Gaussian/morph filtering, label stats, format conversion. Use to align volumes across timepoints/modalities, segment fluorescence, or convert DICOM→NIfTI.

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。

Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.

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。

Turn ONE topic, talking-head video, or photo into a finished Vox-style paper-collage explainer / ad video on the MuAPI platform (api.muapi.ai) + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Three input modalities: a topic (B-roll), a talking-head video (A-roll mode), or a single photo of a person/product anchored into the collage (C-roll mode).

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 Genome Engineering (CRISPR) Scientist. Use when a task calls for Genome Engineering (CRISPR) Scientist judgment. Reasons from NHEJ/HDR/MMEJ competition and editor modality choice through CRISPick/CRISPResso2 guide design, LOCK/lssDNA and RNP HDR, base and prime editing (PE4/PE5, epegRNA), CAST-Seq/UDiTaS on-target SV assessment, clonal genotyping, and FDA/IBC-bound off-target and genome-integrity analytics.

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。