577 件 ・ 関連度順
概要と使いどころ
Use when deciding whether a project is a strong AAAI submission across its broad AI scope, should be reframed or routed to a dedicated track such as AI for Social Impact or AI Alignment, or should instead go to IJCAI, NeurIPS, ICML, ICLR, AISTATS, UAI, ACL, CVPR, KDD, CHI, ICRA, or another specialist venue.
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。
Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.
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Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
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Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairo_pdf TrueType embedding, programmatic aes via tidy evaluation, and the theme_classic publication baseline. Use when producing static figures in R for papers, presentations, or reports.
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Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.
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。
Detect syntenic blocks and structural rearrangements between genomes using MCScanX (Wang 2012), JCVI/MCScan (Tang 2008 Python), GENESPACE (Lovell 2022) for orthology-anchored riparian visualization, SyRI for structural variation, AnchorWave for sequence-level synteny, i-ADHoRe 3.0 for highly diverged species, SynNet for synteny networks, and ntSynt for multi-genome macrosynteny. Use when identifying collinear gene blocks across species, distinguishing macrosynteny from microsynteny, detecting inversions/translocations/duplications, anchoring orthology in WGD lineages, producing publication riparian plots, computing synteny block age via Ks (cross-references whole-genome-duplication), or running synteny-aware ortholog inference in polyploids.
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。
Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.
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。
Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).
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。
Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.
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。
Creates MATLAB interfaces to Python image processing and computer vision models from GitHub repositories or pip-installable packages using MPyReq. Use when asked to interface MATLAB with a Python CV/image model (segmentation, depth estimation, object detection, image generation, super-resolution, etc.), given a GitHub repo URL for an image/vision model, or asked to create an MPyReq demo for a deep-learning vision pipeline. Do NOT use for general-purpose Python-MATLAB interfacing, non-vision models (NLP, tabular, audio), model deployment/serving, or MATLAB-only image processing workflows.
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。
Scan systems and dependencies for CVEs and security vulnerabilities. Use tools like Nessus, OpenVAS, and Qualys to identify and prioritize vulnerabilities. Use when performing security assessments, compliance scanning, or vulnerability management.
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 an agent drives Unreal Engine 5.8 on a Mac for any task: Editor Python (the unreal module), headless UnrealEditor-Cmd -run=pythonscript jobs, Epic's Unreal MCP server, the Remote Control API, console commands and cvars; when a script breaks on 5.8 (EditorLevelLibrary, Remote Control calls refused, Substrate materials black, MRQ presets); when a level, material, shot or build must be judged by numbers and a screenshot before it is called done; or when a brief must go to the right scenario-unreal-* specialist.
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。
机器之心/Synced（国内首家系统性AI科技媒体）的AI内容创作思维——论文级深度+量化驱动+学术与产业双线并跑+产品化媒体运营。 触发词：「机器之心视角」「像机器之心那样写」「Synced Review」「AI论文解读」「SOTA评测」「AI产业分析」「GMIS大会」。 擅长：顶会论文拆解报道（ICLR/NeurIPS/CVPR）、AI模型数据评测（SOTA平台）、产业趋势三层推演（技术→商业→生态）、AI中国年度评选、学术中立+产业判断、PRO会员通讯。
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。
Migrate, modernize, and upgrade codebases to AWS. Run analysis on repos for tech debt, security vulnerabilities, and modernization opportunities. Transforms .NET Framework to .NET 8/10, mainframe COBOL to Java, VMware VMs to EC2, SQL Server to Aurora, and upgrades Java/Python/Node.js versions and AWS SDKs. Use when the user says "migrate .NET to AWS", "upgrade Java to 17/21", "modernize COBOL", "modernize mainframe", "move VMware to EC2", "convert SQL Server to Aurora", "upgrade Python version", "migrate AWS SDK", "transform this codebase", "analyze for issues", "find tech debt", "what tech debt", "security vulnerabilities", "CVEs", "what's wrong with my code", "assess my repos", "where do I start", "find what's outdated", "analyze my repos", "AWS Transform - continuous modernization", "continuous modernization" or "continuous-modernization". Don't use for infrastructure provisioning, CI/CD pipelines, or general coding 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。
Use when an application or system — including one built quickly with AI coding agents — needs a security review with regulatory grounding: a STRIDE threat model, a LINDDUN privacy threat model, a dependency exposure screen against live CVE / CISA-KEV / EPSS data, or a selected, non-exhaustive screen of which EU security obligations (GDPR, NIS2, Cyber Resilience Act, AI Act) may apply and which need determination. Orchestrates the server-enforced threat-modeling workflows of the Ansvar Gateway MCP connector and grounds every regulatory statement in officially published text fetched at answer time — scope, role, and application-date limits stated, never a compliance verdict. Never simulates a workflow and never answers legal questions from model memory.
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。
U.S. customs and trade-law research assistant for HTS classification, CROSS ruling research, CIT/CAFC decision briefing, duty compilation (General + Special + Chapter 99 + AD/CVD + MPF/HMF), country of origin determination, FTA qualification, and end-to-end import compliance review. Triggers on product classification, tariff questions, customs rulings, trade remedy screening (Section 301/232/201), Partner Government Agency admissibility, and UFLPA forced-labor analysis. Produces attorney-reviewable draft work product with an enforced HTSUS authority hierarchy and explicit evidence ledger.
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。
用于 RSA、AES、ECC、格攻击、LWE、CVP、Coppersmith、Pollard、Wiener、填充预言机、GCM、KDF、伪随机数与零知识证明等密码学和数学类 CTF 题;触发名:ctf-crypto
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。
Drive the real Course Video Manager web app in a browser and capture proof of what it did. Use to verify a change before opening a PR, to reproduce a reported UI bug, to see what a page actually renders, or to capture a screenshot of app behaviour. Runs on a per-run, writable clone of a local copy of Matt's data by default — click anything that mutates; production is never written to.
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 knowledge for Azure Confidential Computing development including decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building SGX/SEV-SNP apps, AKS confidential containers, vTPM/CVMs, Fortanix/Key Vault SKR, or clean rooms, and other Azure Confidential Computing related development tasks. Not for Azure Enclave (use azure-enclave), Azure Dedicated HSM (use azure-dedicated-hsm), Azure Cloud Hsm (use azure-cloud-hsm), Azure Payment Hsm (use azure-payment-hsm).
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用于将包含自定义时间列（如'bob'）的Excel金融数据读入Backtrader。该技能包含处理时间格式转换、映射OHLCV列，以及通过Pandas倒序解决K线图时间轴反向问题的完整流程。
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。
使用Python从视频中平均截取9帧画面，并将它们合并成一张图片。
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。
固件 / IoT 渗透链。从拿到一坨 .bin / .img 开始，闭环走完逆向 → 提取 → 模拟 → 利用。 方法论遵循 OWASP FSTM 九阶段；工具链以 binwalk v3、unblob、EMBA、Firmadyne、AFL++ 为主。 适用场景：路由器/摄像头/智能家居固件审计、固件升级包逆向、IoT CVE 复现、嵌入式 0day 挖掘。 触发关键词：固件、firmware、IoT、binwalk、unblob、UART、JTAG、squashfs、UBI、JFFS2、Firmadyne、QEMU 全系统仿真、EMBA、固件渗透、路由器固件、嵌入式漏洞利用、bootloader、NVRAM、FAT、firmware analysis toolkit。
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Audit Hex deps for supply-chain security risk — bidi chars, compile-time exec, maintainer changes, typosquats, CVEs. Use after mix deps.update, when checking if a package upgrade is safe, or reviewing mix.lock PR diffs.
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。
Audit Hex deps for supply-chain security risk — bidi chars, compile-time exec, maintainer changes, typosquats, CVEs. Use after mix deps.update, when checking if a package upgrade is safe, or reviewing mix.lock PR diffs.
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。