arXiv subjects

Sidney Shapiro

Publications and source records attributed to Sidney Shapiro.

8 recordsLinked to original sources

arXiv2026

Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes

This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes. Rather than treating LangGraph, a low-level orchestration framework for stateful agents, as a model-quality benchmark target, we present three executable recipes -- SQL analytics with repair loops, agentic retrieval-augmented generation with evidence gating, and human-in-the-loop policy review with interrupt and checkpoint recovery -- to show how typed state, conditional routing, deterministic tools, retries, interrupts, checkpoints, and traces fit together. LangGraph is positioned by workflow-complexity fit, not as a universal default: simpler ReAct-style or plain SDK loops may be better for basic tool use, schema-first tools for structured extraction and validation, and DSPy when prompt or program optimization is the main goal. Each recipe explains when LangGraph is worth the extra structure and which implementation patterns make routes, pauses, and audit trails explicit product behavior rather than hidden prompt logic.

cs.AI↗

arXiv2026

From Patent Expiry to Business Pathways: AI Workflows for Activating Innovation Archives

Patent databases represent one of the largest public archives of technical knowledge, yet much of this knowledge remains difficult to identify, interpret, and reuse once patent rights expire or lapse. This paper proposes an AI-enabled framework for discovering expired and lapsing patents, identifying technology trends, and translating patent disclosures into business pathways. We use pathways to mean structured commercialization routes such as SaaS products, services, licensing packages, consulting playbooks, training offerings, data products, or internal process tools. The framework treats patent expiry as both a business signal and an archival transition, not primarily as a legal problem. Legal status remains important, but it is one risk-screening input alongside customer need, implementation feasibility, channel access, and market timing. We describe a system architecture that combines patent metadata, maintenance-fee records, legal-status indicators, semantic search, patent-family analysis, market signals, and generative AI workflows. A proof of concept parses all 378 records in an official weekly CIPO ST.96 archive, identifies 20 expired, lapsed, or near-expiry candidates, tests the stability of the transparent scoring model, and uses a locally hosted Qwen3.6 model to populate structured review packets. The evaluation demonstrates reproducible ingestion, stable rankings under weight perturbation, and schema-conformant model output, while also exposing incomplete legal-status coverage and the need for register and expert review. We argue that AI can function as a discovery and translation layer for dormant technical knowledge, but that such systems must explicitly represent legal uncertainty, data limitations, and commercialization risk.

cs.IR↗

arXiv2026

Pandas for Reproducible Data Analysis: From Spreadsheets to Research-Grade Python Workflows

Spreadsheet-heavy analytical work remains common in business analytics, operations reporting, and applied research, yet workbooks that grow through formulas, manual edits, and copy-paste refresh are difficult to audit, reproduce, and govern at scale. When tabular work requires repeatability, validation, version control, automated refresh, or integration with statistics and machine learning, analysts need a transformation layer that preserves familiar table concepts while making assumptions explicit. This paper treats the Python pandas library as that layer: a practical bridge between spreadsheet practice and research-grade workflows, not a wholesale replacement for Excel. The paper contributes an Excel-to-pandas migration mapping, a taxonomy of nine workflow categories, seven end-to-end examples drawn from business analytics and applied research, a failure-mode catalog, and reusable code recipes for governed tabular work. pandas is most useful when tabular analysis must be repeatable, auditable, and defensible, while Excel can remain a familiar input and output interface for stakeholders who need workbooks.

cs.SE↗

arXiv2026

Prophet as a Reproducible Forecasting Framework: A Methodological Guide for Business and Financial Analytics

Reproducibility remains a persistent challenge in forecasting research and practice, particularly in business and financial analytics, where forecasts inform high-stakes decisions. Traditional forecasting methods, while theoretically interpretable, often require extensive manual tuning and are difficult to replicate in proprietary environments. Machine learning approaches offer predictive flexibility but introduce challenges related to interpretability, stochastic training procedures, and cross-environment reproducibility. This paper examines Prophet, an open-source forecasting framework developed by Meta, as a reproducibility-enabling solution that balances interpretability, standardized workflows, and accessibility. Rather than proposing a new algorithm, this study evaluates how Prophet's additive structure, open-source implementation, and standardized workflow contribute to transparent and replicable forecasting practice. Using publicly available financial and retail datasets, we compare the performance and interpretability of Prophet with multiple ARIMA specifications (auto-selected, manually specified, and seasonal variants) and Random Forest, under a controlled and fully documented experimental design. This multi-model comparison provides a robust assessment of Prophet's relative performance and reproducibility advantages. Through concrete Python examples, we demonstrate how Prophet facilitates efficient forecasting workflows and integration with analytical pipelines. The study positions Prophet within the broader context of reproducible research. It highlights Prophet's role as a methodological building block that supports verification, auditability, and methodological rigor. This work provides researchers and practitioners with a practical reference framework for reproducible forecasting in Python-based research workflows.

cs.LG↗

arXiv2025

HQPEF-Py: Metrics, Python Patterns, and Guidance for Evaluating Hybrid Quantum Programs

We study how to evaluate hybrid quantum programs as end-to-end workflows rather than as isolated devices or algorithms. Building on the Hybrid Quantum Program Evaluation Framework (HQPEF), we formalize a workflow-aware Quantum Readiness Level (QRL) score; define a normalized speedup under quality constraints for the Utility of Quantumness (UQ); and provide a timing-and-drift audit for hybrid pipelines. We complement these definitions with concise Python reference implementations that illustrate how to instantiate the metrics and audit procedures with state-of-the-art classical and quantum solvers (e.g., via Qiskit or PennyLane), while preserving matched-budget discipline and reproducibility.

cs.SE↗

arXiv2025

Hybrid Quantum-Classical Machine Learning with PennyLane: A Comprehensive Guide for Computational Research

Hybrid quantum-classical machine learning represents a frontier in computational research, combining the potential advantages of quantum computing with established classical optimization techniques. PennyLane provides a Python framework that seamlessly bridges quantum circuits and classical machine learning, enabling researchers to build, optimize, and deploy variational quantum algorithms. This paper introduces PennyLane as a versatile tool for quantum machine learning, optimization, and quantum chemistry applications. We demonstrate use cases including quantum kernel methods, variational quantum eigensolvers, portfolio optimization, and integration with classical ML frameworks such as PyTorch, TensorFlow, and JAX. Through concrete Python examples with widely used libraries such as scikit-learn, pandas, and matplotlib, we show how PennyLane facilitates efficient quantum circuit construction, automatic differentiation, and hybrid optimization workflows. By situating PennyLane within the broader context of quantum computing and machine learning, we highlight its role as a methodological building block for quantum-enhanced data science. Our goal is to provide researchers and practitioners with a concise reference that bridges foundational quantum computing concepts and applied machine learning practice, making PennyLane a default citation for hybrid quantum-classical workflows in Python-based research.

cs.SE↗

arXiv2025

Pattern-Based File and Data Access with Python Glob: A Comprehensive Guide for Computational Research

Pattern-based file access is a fundamental but often under-documented aspect of computational research. The Python glob module provides a simple yet powerful way to search, filter, and ingest files using wildcard patterns, enabling scalable workflows across disciplines. This paper introduces glob as a versatile tool for data science, business analytics, and artificial intelligence applications. We demonstrate use cases including large-scale data ingestion, organizational data analysis, AI dataset construction, and reproducible research practices. Through concrete Python examples with widely used libraries such as pandas,scikit-learn, and matplotlib, we show how glob facilitates efficient file traversal and integration with analytical pipelines. By situating glob within the broader context of reproducible research and data engineering, we highlight its role as a methodological building block. Our goal is to provide researchers and practitioners with a concise reference that bridges foundational concepts and applied practice, making glob a default citation for file pattern matching in Python-based research workflows.

cs.SE↗

arXiv2025

Novel Direct Alpha Spectroscopy Technique for $^{225}$Ac Radiopharmaceutical detection in Cancer Cells

Targeted alpha-particle therapy (TAT) employs alpha-emitting radionuclides conjugated to tumor-targeting molecules to deliver localized radiation to cancer cells, showing great promise in treating metastatic cancers. Among these radionuclides, Actinium-225 ($^{225}$Ac, t$_{1/2}$ = 9.9 days) has emerged as a clinically promising candidate. Its decay chain generates four successive alpha emissions, resulting in highly localized and effective cytotoxic damage to cancer cells when delivered to tumor sites. However, the assumption of complete retention of $^{225}$Ac and its radioactive daughters at these target sites is often inaccurate. The nuclear recoil effect can lead to off-target distribution and unintended toxicity. Our results revealed distinct spectral differences between radiolabeled cells and reference samples, demonstrating [$^{225}$Ac]Ac-crown-TATE uptake by AR42J cells. Detection of $^{213}$Po, one of the $^{225}$Ac decay daughters, highlighted partial retention and release of decay products from cells, providing information on intracellular retention and daughter redistribution. Geant4 simulations confirmed the alignment of experimental data with theoretical models, validating the method's accuracy. This study establishes a direct alpha spectroscopy approach for investigating $^{225}$Ac and its daughters' behavior in cells and offers a powerful tool for microdosimetry estimation.

physics.med-ph↗