Security audit
Security checks across malware telemetry and agentic risk
The skill mainly does PDF renaming, but it ships leftover document text/path state and optional LLM/file-renaming scripts that need review before installation.
Install only if you are comfortable with a PDF organizer that reads the first pages of every PDF in a chosen folder, stores extracted text in manifest files, and can rename files when run with --execute. Run extract and preview before execute, avoid running the undocumented helper scripts, and do not use llm_parse.py on private or unpublished PDFs unless you trust the local LLM gateway and its downstream data handling.
"filepath": "C:/Users/taizun/Desktop/tmp\\Ch-var-adv.pdf",
"year_hint": null,
"status": "needs_llm_review",
"raw_text": "Contents\n10 Advanced variance reduction 3\n10.1 Grid-based stratification . . . . . . . . . . . . . . . . . . . . . . . 3\n10.2 Stratification and antithetics . . . . . . . . . . . . . . . . . . . . 6\n10.3 Latin hypercube sampling . . . . . . . . . . . . . . . . . . . . . . 8\n10.4 Orthogonal array sampling . . . . . . . . . . . . . . . . . . . . . 12\n10.5 Adaptive importance sampling . . . . . . . . . . . . . . . . . . . 17\n10.6 Nonparametric AIS . . . . . . . . . . . . . . . . . . . . . . . . . 28\n10.7 Generalized antithetic sampling . . . . . . . . . . . . . . . . . . . 36\n10.8 Control variates with antithetics and stratification . . . . . . . . 37\n10.9 Bridge, umbrella and path sampling . . . . . . . . . . . . . . . . 39\nEnd notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51\nExercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55\n1\n2 Contents\n© Art Owen 2009–2013 do not distribute or post electronically without\nauthor’s permission\n10\nAdvanced variance reduction\nThis chapter collects together some advanced and specialized topics in vari-\nance reduction. They are generalizations, extensions and hybrids of methods\npreviously considered.\nWe begin with § 10.1 on grid-based stratification, suitable for low dimensions\nIn dimension d it improves the Monte Carlo RMSE to O(n−1/2−1/d). In § 10.2\nwe apply antithetic sampling within those strata, yielding a method of Haber\nwith RMSE O(n−1/2−2/d). Then § 10.3 presents Latin hypercube sampling, a\nstratification method suitable for large or even unbounded dimension. We round\nout our mini-chapter on advanced stratification with § 10.4 on orthogonal array\nsampling, which is very well suited to intermediate dimensionalities.\nImportance sampling can provide great efficiency gains, but it is difficult to\ndo well and a poor choice can s
...[truncated 25 chars]"filepath": "C:/Users/taizun/Desktop/tmp\\Ch-var-adv.pdf",
"year_hint": null,
"status": "needs_llm_review",
"raw_text": "Contents\n10 Advanced variance reduction 3\n10.1 Grid-based stratification . . . . . . . . . . . . . . . . . . . . . . . 3\n10.2 Stratification and antithetics . . . . . . . . . . . . . . . . . . . . 6\n10.3 Latin hypercube sampling . . . . . . . . . . . . . . . . . . . . . . 8\n10.4 Orthogonal array sampling . . . . . . . . . . . . . . . . . . . . . 12\n10.5 Adaptive importance sampling . . . . . . . . . . . . . . . . . . . 17\n10.6 Nonparametric AIS . . . . . . . . . . . . . . . . . . . . . . . . . 28\n10.7 Generalized antithetic sampling . . . . . . . . . . . . . . . . . . . 36\n10.8 Control variates with antithetics and stratification . . . . . . . . 37\n10.9 Bridge, umbrella and path sampling . . . . . . . . . . . . . . . . 39\nEnd notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51\nExercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55\n1\n2 Contents\n© Art Owen 2009–2013 do not distribute or post electronically without\nauthor’s permission\n10\nAdvanced variance reduction\nThis chapter collects together some advanced and specialized topics in vari-\nance reduction. They are generalizations, extensions and hybrids of methods\npreviously considered.\nWe begin with § 10.1 on grid-based stratification, suitable for low dimensions\nIn dimension d it improves the Monte Carlo RMSE to O(n−1/2−1/d). In § 10.2\nwe apply antithetic sampling within those strata, yielding a method of Haber\nwith RMSE O(n−1/2−2/d). Then § 10.3 presents Latin hypercube sampling, a\nstratification method suitable for large or even unbounded dimension. We round\nout our mini-chapter on advanced stratification with § 10.4 on orthogonal array\nsampling, which is very well suited to intermediate dimensionalities.\nImportance sampling can provide great efficiency gains, but it is difficult to\ndo well and a poor choice can s
...[truncated 25 chars]"filepath": "C:/Users/taizun/Desktop/tmp\\Ch-var-adv.pdf",
"year_hint": null,
"status": "needs_llm_review",
"raw_text": "Contents\n10 Advanced variance reduction 3\n10.1 Grid-based stratification . . . . . . . . . . . . . . . . . . . . . . . 3\n10.2 Stratification and antithetics . . . . . . . . . . . . . . . . . . . . 6\n10.3 Latin hypercube sampling . . . . . . . . . . . . . . . . . . . . . . 8\n10.4 Orthogonal array sampling . . . . . . . . . . . . . . . . . . . . . 12\n10.5 Adaptive importance sampling . . . . . . . . . . . . . . . . . . . 17\n10.6 Nonparametric AIS . . . . . . . . . . . . . . . . . . . . . . . . . 28\n10.7 Generalized antithetic sampling . . . . . . . . . . . . . . . . . . . 36\n10.8 Control variates with antithetics and stratification . . . . . . . . 37\n10.9 Bridge, umbrella and path sampling . . . . . . . . . . . . . . . . 39\nEnd notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51\nExercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55\n1\n2 Contents\n© Art Owen 2009–2013 do not distribute or post electronically without\nauthor’s permission\n10\nAdvanced variance reduction\nThis chapter collects together some advanced and specialized topics in vari-\nance reduction. They are generalizations, extensions and hybrids of methods\npreviously considered.\nWe begin with § 10.1 on grid-based stratification, suitable for low dimensions\nIn dimension d it improves the Monte Carlo RMSE to O(n−1/2−1/d). In § 10.2\nwe apply antithetic sampling within those strata, yielding a method of Haber\nwith RMSE O(n−1/2−2/d). Then § 10.3 presents Latin hypercube sampling, a\nstratification method suitable for large or even unbounded dimension. We round\nout our mini-chapter on advanced stratification with § 10.4 on orthogonal array\nsampling, which is very well suited to intermediate dimensionalities.\nImportance sampling can provide great efficiency gains, but it is difficult to\ndo well and a poor choice can s
...[truncated 25 chars]"filepath": "C:/Users/taizun/Desktop/tmp\\Ch-var-adv.pdf",
"year_hint": null,
"status": "needs_llm_review",
"raw_text": "Contents\n10 Advanced variance reduction 3\n10.1 Grid-based stratification . . . . . . . . . . . . . . . . . . . . . . . 3\n10.2 Stratification and antithetics . . . . . . . . . . . . . . . . . . . . 6\n10.3 Latin hypercube sampling . . . . . . . . . . . . . . . . . . . . . . 8\n10.4 Orthogonal array sampling . . . . . . . . . . . . . . . . . . . . . 12\n10.5 Adaptive importance sampling . . . . . . . . . . . . . . . . . . . 17\n10.6 Nonparametric AIS . . . . . . . . . . . . . . . . . . . . . . . . . 28\n10.7 Generalized antithetic sampling . . . . . . . . . . . . . . . . . . . 36\n10.8 Control variates with antithetics and stratification . . . . . . . . 37\n10.9 Bridge, umbrella and path sampling . . . . . . . . . . . . . . . . 39\nEnd notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51\nExercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55\n1\n2 Contents\n© Art Owen 2009–2013 do not distribute or post electronically without\nauthor’s permission\n10\nAdvanced variance reduction\nThis chapter collects together some advanced and specialized topics in vari-\nance reduction. They are generalizations, extensions and hybrids of methods\npreviously considered.\nWe begin with § 10.1 on grid-based stratification, suitable for low dimensions\nIn dimension d it improves the Monte Carlo RMSE to O(n−1/2−1/d). In § 10.2\nwe apply antithetic sampling within those strata, yielding a method of Haber\nwith RMSE O(n−1/2−2/d). Then § 10.3 presents Latin hypercube sampling, a\nstratification method suitable for large or even unbounded dimension. We round\nout our mini-chapter on advanced stratification with § 10.4 on orthogonal array\nsampling, which is very well suited to intermediate dimensionalities.\nImportance sampling can provide great efficiency gains, but it is difficult to\ndo well and a poor choice can s
...[truncated 25 chars]"filepath": "C:/Users/taizun/Desktop/tmp\\Ch-var-adv.pdf",
"year_hint": null,
"status": "needs_llm_review",
"raw_text": "Contents\n10 Advanced variance reduction 3\n10.1 Grid-based stratification . . . . . . . . . . . . . . . . . . . . . . . 3\n10.2 Stratification and antithetics . . . . . . . . . . . . . . . . . . . . 6\n10.3 Latin hypercube sampling . . . . . . . . . . . . . . . . . . . . . . 8\n10.4 Orthogonal array sampling . . . . . . . . . . . . . . . . . . . . . 12\n10.5 Adaptive importance sampling . . . . . . . . . . . . . . . . . . . 17\n10.6 Nonparametric AIS . . . . . . . . . . . . . . . . . . . . . . . . . 28\n10.7 Generalized antithetic sampling . . . . . . . . . . . . . . . . . . . 36\n10.8 Control variates with antithetics and stratification . . . . . . . . 37\n10.9 Bridge, umbrella and path sampling . . . . . . . . . . . . . . . . 39\nEnd notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51\nExercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55\n1\n2 Contents\n© Art Owen 2009–2013 do not distribute or post electronically without\nauthor’s permission\n10\nAdvanced variance reduction\nThis chapter collects together some advanced and specialized topics in vari-\nance reduction. They are generalizations, extensions and hybrids of methods\npreviously considered.\nWe begin with § 10.1 on grid-based stratification, suitable for low dimensions\nIn dimension d it improves the Monte Carlo RMSE to O(n−1/2−1/d). In § 10.2\nwe apply antithetic sampling within those strata, yielding a method of Haber\nwith RMSE O(n−1/2−2/d). Then § 10.3 presents Latin hypercube sampling, a\nstratification method suitable for large or even unbounded dimension. We round\nout our mini-chapter on advanced stratification with § 10.4 on orthogonal array\nsampling, which is very well suited to intermediate dimensionalities.\nImportance sampling can provide great efficiency gains, but it is difficult to\ndo well and a poor choice can s
...[truncated 25 chars]"filepath": "C:/Users/taizun/Desktop/tmp\\Ch-var-adv.pdf",
"year_hint": null,
"status": "needs_llm_review",
"raw_text": "Contents\n10 Advanced variance reduction 3\n10.1 Grid-based stratification . . . . . . . . . . . . . . . . . . . . . . . 3\n10.2 Stratification and antithetics . . . . . . . . . . . . . . . . . . . . 6\n10.3 Latin hypercube sampling . . . . . . . . . . . . . . . . . . . . . . 8\n10.4 Orthogonal array sampling . . . . . . . . . . . . . . . . . . . . . 12\n10.5 Adaptive importance sampling . . . . . . . . . . . . . . . . . . . 17\n10.6 Nonparametric AIS . . . . . . . . . . . . . . . . . . . . . . . . . 28\n10.7 Generalized antithetic sampling . . . . . . . . . . . . . . . . . . . 36\n10.8 Control variates with antithetics and stratification . . . . . . . . 37\n10.9 Bridge, umbrella and path sampling . . . . . . . . . . . . . . . . 39\nEnd notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51\nExercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55\n1\n2 Contents\n© Art Owen 2009–2013 do not distribute or post electronically without\nauthor’s permission\n10\nAdvanced variance reduction\nThis chapter collects together some advanced and specialized topics in vari-\nance reduction. They are generalizations, extensions and hybrids of methods\npreviously considered.\nWe begin with § 10.1 on grid-based stratification, suitable for low dimensions\nIn dimension d it improves the Monte Carlo RMSE to O(n−1/2−1/d). In § 10.2\nwe apply antithetic sampling within those strata, yielding a method of Haber\nwith RMSE O(n−1/2−2/d). Then § 10.3 presents Latin hypercube sampling, a\nstratification method suitable for large or even unbounded dimension. We round\nout our mini-chapter on advanced stratification with § 10.4 on orthogonal array\nsampling, which is very well suited to intermediate dimensionalities.\nImportance sampling can provide great efficiency gains, but it is difficult to\ndo well and a poor choice can s
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No suspicious patterns detected.