'pth' 검색 결과

Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such

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Guide for theming .NET MAUI apps — light/dark mode via AppThemeBinding, ResourceDictionary theme switching, DynamicResource bindings, system theme detection, and user theme preferences. Use when: "dark mode", "light mode", "theming", "AppThemeBinding", "theme switching", "ResourceDictionary theme", "dynamic resources", "system theme detection", "color scheme", "app theme", "DynamicResource". Do not use for: localization or language switching (see .NET MAUI localization documentation), accessibility visual adjustments (see .NET MAUI accessibility documentation), app icons or splash screens (see .NET MAUI app icons documentation), or Bootstrap-style class theming (see Plugin.Maui.BootstrapTheme NuGet package).

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Upload video/audio media to BytePlus VOD (Video on Demand) storage, returning the Vid and playback references; supports both local file upload (ApplyUploadInfo + TOS + CommitUploadInfo) and URL pull upload (UploadMediaByUrl); also supports AI-based comprehensive quality restoration on already-uploaded videos (removing compression artifacts, noise, scratches, improving clarity, and configuring Pro-tier color depth). Trigger keywords: upload video, upload media, upload to VOD, URL upload, pull upload, local upload, file upload, UploadMediaByUrl, ApplyUploadInfo, media ingestion, quality restoration, quality enhancement, comprehensive restoration, video denoising, denoise, compression artifact removal, color depth, bit depth, 10-bit, 12-bit, 16-bit.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.

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将本地视频转换为近亮远暗的连续灰度深度视频，使用 Video Depth Anything 时序模型，保留时长和可选原声。用于深度控制视频、depth video、视频深度估计。

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Add or refine depth through elevation systems, raised and inset surfaces, layered overlap, and meaningful shadow behavior so interfaces feel structured instead of flat. Use when the user mentions depth, elevation, shadows, layering, flat cards, pressed states, drag feedback, or inset controls.

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Generate 3D models from text or images via 5 providers: Meshy (text-to-3D, texturing, batch), fal.ai Hunyuan3D Pro ($0.225, PBR), WaveSpeedAI Rapid ($0.0225, bulk), Trellis 2 (poly control), and Local Depth (free offline ONNX depth maps + mesh). Includes GLB viewer. Triggers on 3D model, image to 3D, GLB, depth map, text to 3D, texturing, 3D asset, Meshy, Hunyuan.

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Generate Rocaille-style domain warping shaders, mouse-reactive liquid logo effects, depth-map parallax heroes (Astryx-style hover-reactive statues), and stable fluid simulations over live DOM. Four modes — rocaille, liquid-logo, astryx-statue, fluid-dom. Triggers on shader art, depth parallax, hover-reactive depth map, statue hero, fluid sim, liquid logo, WebGL visualization, procedural background, pointer-reactive fluid.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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サービス開発のためのデプスインタビューを実施し、ユーザーの真のニーズ・課題・動機を引き出す。5 Whys、ラダリング法を用いてプロダクト開発に活かせるインサイトを発見する。ユーザーリサーチ、課題発見、ペルソナ構築、プロダクト仮説検証に使用する。Do NOT use for キャリア相談やキャリアカウンセリング（depth-interviewing-careerを使用すること）。

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キャリア設計のためのデプスインタビューを実施し、本人の価値観・強み・動機を引き出す。5 Whys、ラダリング法を用いてキャリアビジョンの明確化を支援する。転職相談、自己理解、キャリアカウンセリング、1on1面談の深掘りに使用する。Do NOT use for 製品・サービスのユーザーリサーチ（depth-interviewing-productを使用すること）。

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Applies depth-driven layered color and shadow principles to elevate average UI into polished, readable interfaces for both light and dark themes. Use when improving or reviewing UI components, setting up a design token system, applying consistent depth and shadow to an interface, or making a flat or inconsistent UI feel more refined without a full redesign.

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Execute and test GraphQL depth limit attacks using deeply nested recursive queries to identify denial-of-service vulnerabilities in GraphQL APIs.

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Add or refine depth through elevation systems, raised and inset surfaces, layered overlap, and meaningful shadow behavior so interfaces feel structured instead of flat. Use when the user mentions depth, elevation, shadows, layering, flat cards, pressed states, drag feedback, or inset controls.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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답변 전에 네 단계의 상세 수준과 예상 토큰 수를 제시합니다. 짧은 결론부터 자세한 설명까지 선택한 수준에 맞추고 같은 세션에서 유지합니다.

• 짧은 답변과 자세한 설명 중 선택
• 답변 전 예상 토큰 수 확인
• 배경과 예시가 있는 적당한 길이의 답변

조사 주제를 여러 질문으로 나누고 웹 검색과 본문 검토를 진행합니다. 출처, 확신 수준, 정보 부족 부분을 밝힌 보고서로 정리합니다.

• 경쟁사와 시장 동향 조사
• 도입 전 기술 비교
• 시장 규모와 성장 전망 조사

Investigate complex topics using web sources, stored knowledge, and relevant code. Break questions into smaller parts, compare evidence, and produce a structured report with confidence assessments, unresolved questions, and next steps.

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• Investigating a complex topic through smaller research questions
• Comparing web sources with stored knowledge and code
• Summarizing evidence quality and conflicting findings

Document a project's structure, commands, and specific conventions in a hierarchy of AGENTS.md files. Analyze directory complexity to choose useful locations, update existing guidance, and check that child files avoid repeating their parents.

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• Create project guidance for coding agents
• Refresh existing AGENTS.md documentation
• Document rules specific to complex directories

Add zooms, pans, masks, moving text, and depth to HyperFrames compositions. Build animation states that remain correct when jumping to any point in time, then inspect motion and final frames with diagnostics and snapshots.

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• Adding zooms and pans to a shot
• Revealing images with animated masks
• Animating text and SVG shapes

Design scene transitions and animated text using matched movement direction and speed across cuts. The catalog provides concrete settings for zooms, blur, word-by-word transitions, staggered entrances, and repositioning groups of elements.

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• Connect scenes with matching motion
• Transition between headlines word by word
• Bring title elements in with staggered timing

Delegate a feature, bug fix, or substantial code change and carry it through implementation, review, and verification. Start from a task description or issue link, with options for the workflow route and review depth.

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• Delegating implementation of a new feature
• Fixing a bug described in an issue
• Choosing review depth for a substantial code change