Results for “mfe”

Use when predicting RNA secondary structures, calculating minimum free energy (MFE) folds, or computing partition functions and base pairing probabilities for RNA sequences.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Applies the Federal Enterprise Architecture Framework to design scalable, maintainable, and interoperable enterprise systems and solutions.

Featurização molecular para ML (100+ featurizadores). ECFP, MACCS, descritores, modelos pré-treinados (ChemBERTa), converter SMILES em features, para QSAR e ML molecular.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Generates frontend source code files and a PR description from BA, TechLead, PM, and BE artifacts, following a level-based implementation plan.

Generates frontend source code and a PR description from BA, TechLead, PM, and BE artifacts, following a level-based implementation plan.

Recover metadata and content of deleted files from NTFS volumes by analyzing the Master File Table, $LogFile, $UsnJrnl, and MFT slack space using forensic tools like MFTECmd and analyzeMFT.

Benchmarks automatic post-editing (APE) models on WMT'18 SMT, SubEdits, and MLQE-PE datasets, reporting BLEU, ChrF, and TER scores computed with SacreBLEU and TERCOM.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning, covering 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa, with support for QSAR modeling and virtual screening.

Provides guidance for training Mixture-of-Experts models with long context windows, covering context parallelism sizing, selective recomputation, dispatcher choices, and practical patterns from recent experiments.

Fetch design context, screenshots, variables, and assets from Figma, and translate Figma nodes into production code using the Figma MCP server.

Generate a complete PHP Model Context Protocol server project with tools, resources, prompts, and tests using the official PHP SDK.

Govern Power Automate flows and Power Apps at scale by classifying business impact, detecting orphaned resources, auditing connectors, enforcing compliance, and computing archive scores — all without Dataverse or the CoE Starter Kit.

Detect NTFS timestamp manipulation (MITRE T1070.006) by comparing $STANDARD_INFORMATION vs $FILE_NAME timestamps in the MFT using analyzeMFT and Python.

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrupt-driven I/O, and peripheral drivers.

Kernel-Mode Driver Framework (KMDF), the Microsoft-recommended way to write Windows kernel-mode drivers. Covers DriverEntry, EvtDeviceAdd, IRPs and IOCTLs, I/O queues, PnP and Power state machines, IRQL discipline, memory pools, WPP tracing, SAL annotations, and Driver Verifier. USE WHEN: user mentions "KMDF", "WDF kernel", "Windows kernel driver", "DriverEntry", "WdfDriverCreate", "EvtDeviceAdd", "IRP", "IOCTL", "DISPATCH_LEVEL", "PASSIVE_LEVEL", "NTSTATUS", "PoolTag", "WdfRequestComplete" DO NOT USE FOR: UMDF v2 (use `wdf-umdf`), classic WDM-only drivers, file-system filters (FltMgr is a separate framework)

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

Configure and optimize .faf files, MCP servers, and bi-directional sync for AI context across multiple platforms, with championship scoring to achieve 85%+ AI-readiness.

Use when annotating VCF files with flanking sequence information (INFO/FS tag) or masking regions/variants in flanking sequences.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks, including model serving, feature engineering, A/B testing, and monitoring.

Installs the Snowflake Connector for Python and builds a wheel package using PEP-517.

生产级机器学习工程工作流——数据契约、可复现训练、模型评估、服务部署、监控与回滚

Use when a carrier asks about fuel-card vendor selection (Comdata, EFS, WEX, RTS, Pilot Flying J, Love's Connect), discount levels, fraud control, driver authorization PINs, IFTA reporting features, cash advance, money codes, or how the card fits into payroll/settlement workflows.

Expert framework for assessing and achieving product-market fit. Combines PMF measurement methodologies, Sean Ellis survey, retention analysis, segment-specific PMF, and post-PMF scaling strategy. Use when determining if product has PMF, measuring user engagement, running PMF surveys, or deciding whether to scale or keep iterating.

Detect and respond to Adversary-in-the-Middle (AiTM) phishing attacks that use reverse proxy kits like EvilProxy, Evilginx, and Tycoon 2FA to bypass MFA and steal session tokens.

Integrates on-device and cloud machine learning into Flutter apps with TensorFlow Lite and Firebase ML Kit, covering image classification, object detection, OCR, face detection, and barcode scanning.

Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.

Cost-reimbursement IGCE scaffold for CPFF, CPAF, and CPIF with fee caps and estimated-cost structure (BLS + CALC+ + per diem MCPs). USE WHEN the user asks to "build a CPFF IGCE", "cost reimbursement estimate", "CPAF fee cap", "CPIF incentive structure", "CR IGCE", or "estimated cost plus fee". DO NOT USE FOR FFP (`igce-builder-ffp`) or T&M (`igce-builder-lh-tm`).

Refactor messy Pharo code safely through the genie MCP tools, working from the live image (no files). Use for renames, method extraction, moving behavior, or general cleanup of existing code.