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Reformulations Example

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Preview of 2022 Reports to the Membership
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2022 Reports to the Membership (Committee-reports-2022-1.pdf)

2.6 MBAmy SullivanEN 52 pages
2022 AAPCO Check Sample Program was not conducted due to renovation of the Pesticide Formulations Laboratory of Indiana State Chemist Office.
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Preview of PDF (1.097 MB)
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PDF (1.097 MB) (epjconf_dymat2021_01010.pdf)

1.1 MBFang XiangfanEN 10 pages
A new method for determining plastic deformation properties at high strain rates has been developed, addressing the challenge of system ringing in conventional...
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Preview of **Invariants and Mutexes in Planning and Optimization**
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**Invariants and Mutexes in Planning and Optimization** (po-e02-handout4.pdf)

176 KBMalte Helmert and Gabriele RögerEN 7 pages
It explores how humans inherently utilize invariants to plan tasks and provides examples to illustrate this principle
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Preview of #36 Sample and/or Specimen Preparation for Testing Multilayer Geomembranes
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#36 Sample and/or Specimen Preparation for Testing Multilayer Geomembranes (paper36.pdf)

991 KBMarilynEN 12 pages
The Geosynthetic Institute's White Paper #36 discusses sample and specimen preparation for testing multilayer geomembranes, which are composed of more than one...
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Preview of Download (174Kb)
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Download (174Kb) (46-1985.pdf)

174 KBEN 4 pages
The Central Electrochemical Research Institute (CECRI) has implemented a microcomputer-based information retrieval system using a DataWealth microcomputer from...
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Preview of Rapport d’activité 2019 du Pôle Assurance Banque Epargne de l’ACPR et de l’AMF (PDF)
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Rapport d’activité 2019 du Pôle Assurance Banque Epargne de l’ACPR et de l’AMF (PDF) (acpr_amf_rapport_2019.pdf)

4.42 MBFR 29 pages
Le Pôle commun entre l'ACPR et l'AMF a poursuivi ses actions en 2019 pour protéger les clients et les épargnants, notamment en matière d'information et de...
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Preview of Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
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Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks (2106.00774.pdf)

5.64 MBEN 32 pages
Gradient flows are used to optimize functionals in probability spaces with the Wasserstein metric. A typical approach involves the Jordan-Kinderlehrer-Otto scheme, but this is challenging in high dimensions. We propose using input-convex neural networks to approximate the scheme.
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