Results for “batch”

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

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Build AI batch processing systems for scale. TRIGGERS - Use when user needs help with ai-batch-processor related tasks.

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Build batch processing systems for AI workloads. TRIGGERS - Use when user needs help with ai-batch-processing related tasks.

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Quality control for IMC/MIBI data across pixel, channel, image, slide, and batch levels, covering Poisson-count SNR (cell-level Gaussian-mixture and empty-channel comparison), spillover-matrix QC (the three physical sources), drift and the missing EQ-bead analog, acquisition artifacts, and sample-of-origin batch effects. Use when deciding whether to keep or drop a channel, ROI, or slide, distinguishing a dim antibody from a failed one, reading a spillover matrix, or diagnosing batch-driven clustering before analysis.

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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

The English summary is being prepared. Showing the original description.

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

The English summary is being prepared. Showing the original description.

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

The English summary is being prepared. Showing the original description.

Quality control for IMC/MIBI data across pixel, channel, image, slide, and batch levels, covering Poisson-count SNR (cell-level Gaussian-mixture and empty-channel comparison), spillover-matrix QC (the three physical sources), drift and the missing EQ-bead analog, acquisition artifacts, and sample-of-origin batch effects. Use when deciding whether to keep or drop a channel, ROI, or slide, distinguishing a dim antibody from a failed one, reading a spillover matrix, or diagnosing batch-driven clustering before analysis.

The English summary is being prepared. Showing the original description.

Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

The English summary is being prepared. Showing the original description.

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

The English summary is being prepared. Showing the original description.

批量调用 Dreamifly 生成并下载图片或视频。Use when the user asks to batch-generate images, edit images with references, create text-to-video or image-to-video, queue prompts, validate or estimate generation cost, retry failed media jobs, or summarize Dreamifly outputs.

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Convert batches of design screenshots into Vue 3 page components, reusable interface sections, and initial routing code. Map visual elements to Vant, Element Plus, or Ant Design Vue, while using supplied image assets where appropriate.

• Turn a folder of design screenshots into Vue 3 pages
• Group list, detail, and loading states into one page
• Extract repeated interface sections into shared components

Plan manufacturing jobs around available machines, operators, materials, and delivery dates. Use bottleneck analysis, changeover planning, and workload balancing to compare production sequences and revise schedules when breakdowns, shortages, or absences disrupt the plan.

• Sequence jobs competing for limited equipment
• Revise production schedules after equipment breakdowns
• Compare batch sizes and changeover costs

Helps design ClickHouse tables, write analytical queries, and improve slow queries. Covers loading data in batches, preparing reusable summaries, and monitoring performance for large datasets, real-time dashboards, and time-series analysis.

• Designing tables for large-scale analytics
• Improving slow aggregation queries
• Loading large datasets in batches

Set up vLLM to serve large language models through an OpenAI-compatible API or process prompts in batches. The skill covers deployment, performance monitoring, and quantization, which reduces model memory requirements.

• Deploying an OpenAI-compatible language model API
• Generating text from large batches of prompts
• Serving large models with limited GPU memory

Set up and optimize large language model inference, or response generation, on NVIDIA GPUs. Use quantization, batch processing, and multiple GPUs to improve serving performance, and expose models through a chat completion API.

• Serving a language model through a chat completion API
• Processing many prompts in batches
• Configuring quantization to reduce memory use

Run ComfyUI workflows to generate images, video, audio, and 3D content. Prepare models and processing nodes, adjust generation settings, create batches of variations, monitor progress, and download results from a local server or Comfy Cloud.

• Generate images from text with Stable Diffusion or Flux
• Transform an image or replace a selected area
• Create batches with different generation settings

Coordinate large migrations, reviews, or research tasks across multiple agents. Keep plans and intermediate results in scripts and files, challenge findings with independent checks, and integrate verified work in controlled batches.

• Splitting large file migrations across agents
• Testing audit findings against counter-evidence
• Cross-checking research from different perspectives

Read, filter, create, update, and delete Airtable records through its API using curl. Inspect table fields before editing, process records in batches, and synchronize data by updating matching entries or creating new ones.

• Finding tasks by status, priority, or due date
• Adding records in batches
• Synchronizing records by email or another matching field

Improve GSAP animation performance by reviewing movement properties, batching page measurements and updates, and stopping unused animations. It guides changes for smoother motion, including efficient handling of long lists, pointer tracking, and scroll-driven effects.

• Reducing stutter in GSAP animations
• Improving animations across many list items
• Optimizing effects that follow the pointer

Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

• Reduce memory use in an AgentDB application
• Tune search speed while preserving accuracy
• Plan settings for millions of vectors

Help DeerFlow maintainers investigate GitHub issues and review pull requests, or proposed code changes. It prepares or posts evidence-based comments with fix strategies, risks, and validation guidance, and compares competing proposals for the same issue.

• Draft issue replies with fixes and validation steps
• Post evidence-based pull request review findings
• Review batches of issues and pull requests

Prepare and merge an owner-selected PR batch with contributor credit and exact-candidate admission. Use for a pasted PR list; combined focused validation is diagnostic, and every actual landing still requires its applicable checks and authorization.

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Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

• Reduce memory use in an AgentDB application
• Tune search speed while preserving accuracy
• Plan settings for millions of vectors