'segment' 검색 결과

Create, design, and maintain Calculated Insights in Data Cloud: SQL authoring, dimension and measure definition, scheduling, streaming insights. Trigger keywords: calculated insight, CI SQL, insight measure, insight dimension, streaming insight, insight schedule, Data Cloud metric, insight refresh. NOT for segment filters, audiences and activation - use admin/data-cloud-segmentation. NOT for ingestion, DLO to DMO mapping and identity resolution - use data/data-cloud-data-streams.

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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Divides a lead list into useful commercial segments. Use when the user provides a combined lead list and wants segments with criteria, sizes, recommended treatment, and priority.

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Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.

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Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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Expert patterns for Segment Customer Data Platform including Analytics.js, server-side tracking, tracking plans with Protocols, identity resolution, destinations configuration, and data governance best practices. Use when: segment, analytics.js, customer data platform, cdp, tracking plan.

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Enforce Kubernetes network segmentation using Calico CNI network policies and global network policies to control pod-to-pod traffic, restrict egress, and implement zero-trust microsegmentation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

오디오의 주파수 변화, 음량, 리듬을 시각화하고 여러 그래프를 격자로 배치합니다. 시간 구간, 색상, 이미지 크기를 지정해 JPG나 PNG로 저장합니다.

• 시간에 따른 오디오 주파수 변화를 확인합니다
• 여러 그래프로 음량과 리듬을 비교합니다
• 오디오의 특정 구간만 시각화합니다

가정 네트워크를 VLAN으로 용도별로 나누고 IoT 기기와 방문자를 PC 및 서버에서 격리하도록 스위치 포트, Wi-Fi 연결, 방화벽 규칙 설정을 돕습니다.

• 스마트 가전을 PC 및 서버에서 격리합니다
• 방문자 Wi-Fi를 인터넷 전용으로 제한합니다
• 스위치와 Wi-Fi에 VLAN을 배정합니다

음성 파일의 주파수, 음량, 템포 등을 이미지로 만듭니다. 여러 그래프를 한 이미지에 배치하거나 지정 구간만 시각화해 음성을 비교하고 합성 상태를 확인합니다.

• 여러 음성 출력의 특성 비교
• 합성 음성의 문제 확인
• 녹음의 지정 구간 시각화

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Evaluate 3D point clouds and movement trajectories against reference data with CloudAnalyzer. Prepare inputs, measure differences, apply quality thresholds, inspect results visually, and track baseline history for mapping, localization, and perception workflows.

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• Compare a generated point cloud with reference data
• Measure errors in estimated movement trajectories
• Check ground segmentation against quality thresholds

Create multilingual voice-over audio from scripts supplied for each language using the paid Beatra service. Guides voice selection for each market, pronunciation and timing pilots, and delivery organized by language and segment for review.

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• Produce voice-overs from scripts prepared for each language
• Choose voices suited to each target market
• Review pronunciation and timing with pilot audio

Configure pfSense to separate internal, guest, DMZ, and IoT networks. Set firewall rules, port forwarding, VPN connections, and bandwidth policies, then check traffic logs and test that each network can reach only its intended destinations.

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• Separate staff, guest, server, and IoT networks
• Set port forwarding rules for externally accessible services
• Configure VPN access and bandwidth policies

Plan customer retention and customer success staffing for a startup. Break down renewal and churn patterns, group customers by value and fit, and compare service models to guide resource allocation and hiring.

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• Reviewing retention metrics and reasons for churn
• Allocating support investment across customer tiers
• Estimating customer success staffing needs

Turn ePub books into training examples for models that reproduce an author's writing style. Covers text extraction, coherent segmentation, varied instructions, model training, and checks for copied phrases and style transfer to new scenarios.

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• Build training datasets from ePub books
• Train a model on an author's writing style
• Check for copied phrases in generated prose

Plan and carry out wireless security assessments, from surveying nearby networks to checking authentication weaknesses and recording findings. Covers personal and enterprise Wi-Fi, rogue access points, and testing considerations for Wi-Fi 6, 6E, and 7.

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• Planning a corporate Wi-Fi security assessment
• Reviewing WPA2 and WPA3 authentication weaknesses
• Testing enterprise Wi-Fi against rogue access points