「math」の検索結果

Deterministic router for math cognitive stack - maps user intent to exact CLI commands

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。

Statistics, probability, linear algebra, and mathematical foundations for data science

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。

Unified math capabilities - computation, solving, and explanation. I route to the right tool.

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 in formal logic, model theory, computability, and foundations of mathematics

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 problems to appropriate mathematical frameworks using expert heuristics

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。

Master discrete mathematics, logic, formal proofs, and computational thinking. Build the mathematical foundation for all computer science.

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。

Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.

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 the user asks to "read back" a paid campaign change, "did this ad change work", or "compare ROAS/CPA before and after"; reads ROAS/CPA against a control over a fixed readback window and returns a Promote / Keep-testing / Rollback / Unproven readback decision with the math delegated to roi-calculator. Not for RQS scoring or veto adjudication — use ad-account-auditor; not for the ROI ratio math — use roi-calculator; not for cross-channel rollups — use performance-analyzer. 付费广告复盘/ROAS回看/投放效果归因

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。

当用户要求数学建模、建模竞赛、建模分析、代码求解、结果可视化或生成数学建模论文时使用。支持完整三阶段流程、单独执行任一角色，以及在关键节点使用独立 Subagent 进行阶段内质检；默认只启用质检 Subagent，用户可明确选择额外协作。

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 working with Lean 4 (.lean files), writing mathematical proofs, seeing "failed to synthesize instance" errors, managing sorry/axiom elimination, or searching mathlib for lemmas - provides build-first workflow, haveI/letI patterns, compiler-guided repair, and LSP integration

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。

Explain a cap table, dilution, SAFEs, option pools, and round mechanics in plain English with the actual math. Use when asked to explain dilution, model a SAFE or priced round, size an option pool, understand a term sheet's economics, or figure out who owns what after a raise. Produces a worked ownership breakdown before/after the round, the dilution math step by step, and the traps founders miss. Not legal or financial advice.

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。

Decode a 401k or workplace retirement plan — the real cost of its funds, the match's fine print, vesting math, and the plan features worth using or avoiding. Use when someone asks 'is my 401k any good', 'decode my 401k plan', 'which funds should I look at', or 'what fees am I paying'. Produces a fee decode in dollars-over-time, match and vesting math, a fund-lineup triage by cost, and the questions for HR or the plan administrator.

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。

Trigger when: (1) User mentions "manim" or "Manim Community" or "ManimCE", (2) Code contains `from manim import *`, (3) User runs `manim` CLI commands, (4) Working with Scene, MathTex, Create(), or ManimCE-specific classes. Best practices for Manim Community Edition - the community-maintained Python animation engine. Covers Scene structure, animations, LaTeX/MathTex, 3D with ThreeDScene, camera control, styling, and CLI usage. NOT for ManimGL/3b1b version (which uses `manimlib` imports and `manimgl` CLI).

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 correct MATLAB code using the Symbolic Math Toolbox. Use when the user asks for symbolic computations, analytical solutions, symbolic differentiation/integration, equation solving, or converting symbolic results to numeric MATLAB functions. Also use when converting differential equations to transfer functions or state-space form, extracting PDE coefficients (pdeCoefficients), symbolic-to-C code generation (matlabFunction + codegen), symbolic matrix variables (symmatrix), physical units and constants (symunit, newUnit), or rewriting/combining algebraic expressions (rewrite, combine).

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。

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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。

Perform the final submission-level audit of mathematical-modeling workflow integrity, evidence quality, anti-fabrication, paper coherence, figures, references, and contest readiness after consistency and completeness audits pass.

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。

Manage and verify references for mathematical modeling contest papers, generating BibTeX entries, checking citation completeness, and ensuring all references are traceable to actual sources.

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 submission-ready mathematical-modeling paper sections from the approved solution package, frozen numbers, human decision ledger, and verified figures without searching scattered exploratory outputs or inventing interpretation.

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。

Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.

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。

Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.

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。

Classify each parsed mathematical-modeling subquestion by required output and structure, surface ambiguous framing trade-offs for human choice, and record primary/secondary task types without selecting algorithms.

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。

Parse a mathematical-modeling problem into goals, objects, data, constraints, outputs, subquestions, dependencies, variables, relationships, and human-confirmed success criteria before any method selection.

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。

Build and risk-screen a compact role-based method shortlist for a mathematical-modeling subquestion. Use after problem framing and data profiling, before model code generation, to propose a main candidate, a usable baseline, and at most one conditional fallback without padding the pool.

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。

Extract and maintain global and method-specific mathematical-model assumptions from the problem frame, active method cards, data profile, and risk probes, while leaving necessity and impact judgments to the human modeler.

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。