「segmentation」の検索結果

Expert TSA cybersecurity compliance advisor for critical infrastructure owners and operators. Use this skill whenever a user asks about TSA Security Directives for pipelines, freight railroads, passenger rail, public transit, or bus operators; the TSA Cyber Risk Management Program (CRMP); Cybersecurity Implementation Plan (CIP); Cybersecurity Operational Implementation Plan (COIP); Cybersecurity Assessment Plan (CAP); incident reporting to CISA; designation of a Cybersecurity Coordinator; Critical Cyber Systems (CCS); OT/IT network segmentation; the TSA November 2024 NPRM; or any directive in the SD Pipeline-2021 series, SD 1580-21-01 (freight rail), or SD 1582-21-01 (public transit/passenger rail). Also trigger for questions like "are we covered by TSA directives?", "what does the TSA require for pipeline cybersecurity?", "how do I build a CIP?", "what must I report to CISA?", or any request involving transportation critical infrastructure cybersecurity compliance.

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 PCI DSS compliance advisor covering PCI DSS v4.0.1 (current) and v4.0. Use this skill whenever a user asks about PCI DSS, payment card security, cardholder data protection, CDE scoping, SAQ types (A, A-EP, B, B-IP, C, C-VT, P2PE, D), ROC, AOC, QSA assessments, ASV scans, merchant levels, service provider levels, network segmentation, penetration testing, tokenisation, encryption of PAN data, or any of the 12 PCI DSS requirements. Also trigger for questions like "are we PCI compliant?", "how do I scope my CDE?", "which SAQ applies to us?", "what changed in PCI DSS v4.0?", "how do I prepare for a QSA audit?", or any request involving payment data security, cardholder data environment, or PCI certification readiness.

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。

Produce the Part 3 Four Core Documents: business, segmentation, positioning, DMFlow. "produce the four core documents"

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 TSA cybersecurity compliance advisor for critical infrastructure owners and operators. Use this skill whenever a user asks about TSA Security Directives for pipelines, freight railroads, passenger rail, public transit, or bus operators; the TSA Cyber Risk Management Program (CRMP); Cybersecurity Implementation Plan (CIP); Cybersecurity Operational Implementation Plan (COIP); Cybersecurity Assessment Plan (CAP); incident reporting to CISA; designation of a Cybersecurity Coordinator; Critical Cyber Systems (CCS); OT/IT network segmentation; the TSA November 2024 NPRM; or any directive in the SD Pipeline-2021 series, SD 1580-21-01 (freight rail), or SD 1582-21-01 (public transit/passenger rail). Also trigger for questions like "are we covered by TSA directives?", "what does the TSA require for pipeline cybersecurity?", "how do I build a CIP?", "what must I report to CISA?", or any request involving transportation critical infrastructure cybersecurity compliance.

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 PCI DSS compliance advisor covering PCI DSS v4.0.1 (current) and v4.0. Use this skill whenever a user asks about PCI DSS, payment card security, cardholder data protection, CDE scoping, SAQ types (A, A-EP, B, B-IP, C, C-VT, P2PE, D), ROC, AOC, QSA assessments, ASV scans, merchant levels, service provider levels, network segmentation, penetration testing, tokenisation, encryption of PAN data, or any of the 12 PCI DSS requirements. Also trigger for questions like "are we PCI compliant?", "how do I scope my CDE?", "which SAQ applies to us?", "what changed in PCI DSS v4.0?", "how do I prepare for a QSA audit?", or any request involving payment data security, cardholder data environment, or PCI certification readiness.

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。

Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.

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。

Brand Tailored Promotions — audience targeting, discount tiers, customer segmentation, repeat purchase incentives

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。

Designs products around price using the 9 rules from Ramanujam and Tacke - WTP conversations, needs-based segmentation, Good/Better/Best configuration, monetization models, behavioral pricing, and price integrity. Use when designing new products, validating pricing for SaaS/B2B/B2C launches, choosing between subscription/usage/freemium models, fixing post-launch sales below plan, diagnosing failed launches as Feature Shock/Minivation/Hidden Gem/Undead, or when product teams say 'let's price it later'. Not for pure commodities or cost-plus regulated environments.

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。

Structured persona creation and JTBD analysis methodology - persona templates, ODI job step tables, pain point mapping, success metric quantification, and multi-persona segmentation

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 needs email or lifecycle automation — welcome series, nurture, abandoned cart, re-engagement, broadcast, and transactional flows with subject lines, segmentation, cadence, and deliverability, plus legal variants: CAN-SPAM for US, GDPR opt-in for EU, PDPA for SEA, LGPD for LATAM. Covers Klaviyo, Mailchimp, and ActiveCampaign. Trigger on 'email marketing', 'welcome sequence', 'drip campaign', 'newsletter', 'abandoned cart emails', 'nobody opens our emails'. Not for — coding HTML that renders correctly in Outlook, see `49-html-email-template-global`; cold outbound to B2B prospects, see `33-b2b-lead-gen-global`; refer-a-friend loops, see `18-referral-program-global`.

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 object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language 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。

Design cloud network architectures with VPC patterns, subnet strategies, zero trust principles, and hybrid connectivity. Use when planning VPC topology, implementing multi-cloud networking, or establishing secure network segmentation for cloud workloads.

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。

Configure host-based firewalls (iptables, nftables, UFW) and cloud security groups (AWS, GCP, Azure) with practical rules for common scenarios like web servers, databases, and bastion hosts. Use when exposing services, hardening servers, or implementing network segmentation with defense-in-depth strategies.

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。

Operational Technology and industrial control system security — Purdue model segmentation, industrial protocol analysis (Modbus, DNP3, S7, EtherNet/IP), PLC/HMI exposure, IEC 62443 alignment, and MITRE ATT&CK for ICS, for authorized and safety-conscious assessments

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 applications and infrastructure handling payment card data against PCI DSS v4.0. Heavy emphasis on scope determination (the single most-leveraged variable) plus the engineering-relevant requirements — Req 3 (storage of CHD), Req 4 (transmission), Req 6 (secure SDLC), Req 7-8 (access), Req 10 (logging), Req 11 (testing), Req 12 (program). Use when the user mentions 'PCI,' 'PCI DSS,' 'PCI DSS 4.0,' 'payment card,' 'cardholder data,' 'CHD,' 'PAN,' 'PCI scope,' 'PCI compliance,' 'SAQ,' 'AoC,' 'attestation of compliance,' 'tokenization,' 'P2PE,' 'network segmentation for PCI,' or audits any system that stores, processes, or transmits payment card data.

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。

Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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。

Computational pathology toolkit for whole-slide images (WSIs): load slides, extract tiles, stain normalization, nuclear segmentation, feature extraction, and ML training. Supports H&E and multiplex. For end-to-end pipelines from raw WSIs to quantitative outputs.

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。

Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.

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。

Interactive viewer for microscopy. Displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, Tracks layers; supports annotation, plugin analysis, headless screenshots. Core visualization for Python bioimage workflows. Use ImageJ/FIJI for macro processing; napari for Python-native interactive visualization and DL segmentation review.

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。

Comprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception pipelines.

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 you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.

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 Detectron2 for object detection, segmentation, configuration, datasets, training, inference, export, and extension 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。

Use the DeepGCNs PyTorch operating guide for graph convolution layers, dynamic and dilated KNN blocks, point-cloud classification or segmentation, PPI, OGB/DeeperGCN, and reversible memory-efficient GNN 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。