Semantic Decomposition Improves Learning of Large Language Models on EHR Data

Electronic health records (EHR) are widely believed to hold a profusion of actionable insights, encrypted in an irregular, semi-structured format, amidst a loud noise background. To simplify learning patterns of health and disease, medical codes in EHR can be decomposed into semantic units connected by hierarchical graphs. Building on earlier synergy between Bidirectional Encoder Representations from Transformers (BERT) and Graph Attention Networks (GAT), we present H-BERT, which ingests complete graph tree expansions of hierarchical medical codes as opposed to only ingesting the leaves and pushes patient-level labels down to each visit. This methodology significantly improves prediction of patient membership in over 500 medical diagnosis classes as measured by aggregated AUC and APS, and creates distinct representations of patients in closely related but clinically distinct phenotypes.Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, http://www.ml4h.cc, 9 page

Reactor physics assessment of Thick SiC clad PWR fuels

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 84-86).High temperature tolerance, chemical stability and low neutron affinity make silicon carbide (SiC) a potential fuel cladding material that may improve the economics and safety of light water reactors (LWRs). "Thick" SiC cladding (0.089 cm) is easier (and thus more economical) to manufacture than SiC of conventional Zircaloy (Zr) cladding thickness (0.057 cm). Five fuel and clad combinations are analyzed: Zr with solid U0 2 pellets, reduced fuel fraction "thick" SiC (Thick SiC) with annular U0 2 pellets, Thick SiC with solid U0 2/BeO pellets, reduced coolant fraction annular fuel with "thick" SiC (Thick SiC RCF), and Thick SiC with solid PuO2/ThO2 pellets. CASMO-4E and SIMULATE-3 have been utilized to model the above in a 193 assembly, 4-loop Westinghouse pressurized water reactor (PWR). A new program, CSpy, has been written to use CASMO/SIMULATE to conduct optimization searches of burnable poison layouts and core reload patterns. All fuel/clad combinations have been modeled using 84 assembly reloads, and Thick SiC clad annular U0 2 has been modeled using both 84 and 64 assembly reloads. Dual Binary Swap (DBS) optimization via three Objective Functions (OFs) has been applied to each clad/fuel/reload # case to produce a single reload enrichment equilibrium core reload map. The OFs have the goals of: minimal peaking, balancing lower peaking with longer cycle length, or maximal cycle length. Results display the tradeoff between minimized peaking and maximized cycle length for each clad/fuel/reload # case. The presented Zr reference cases and Thick SiC RCF cases operate for an 18 month cycle at 3587 MWth using 4.3% and 4.8% enrichment, respectively. A 90% capacity factor was applied to all SiC cladding cases to reflect the challenge to introduction of a new fuel. The Thick SiC clad annular U0 2 (84 reload cores) and Thick SiC U0 2/BeO exhibit similar reactor physics performance but require higher enrichments than 5%. The Thick SiC RCF annular U0 2 fuel cases provide the required cycle length with less than 5% enrichment. The Thick SiC clad PuO2/ThO 2 cores can operate with a Pu% of heavy metal of about 12%, however they may have unacceptable shutdown margins without altering the control rod materials.by David A. Bloore.S.M

Spin-Aware Neural Network Interatomic Potential for Atomistic Simulation

Computational modeling is key in materials science for developing mechanistic insight that enables new applications. ab initio methods capture exceptional phenomenological richness to high numerical accuracy, but at high cost and limited scale. Empirical potentials are faster and scale better, but cannot compare to ab initio in numerical and physical accuracy. Machine learning (ML) interatomic potentials (IPs) of recent years offer a balance: excellent phenomenology and accuracy, while scaling well and at moderate cost. Interatomic potentials are generally formulated as functions of atomic coordinates only—i.e. spin-agnostic. For materials whose structures or energetics are influenced by spin, this is insufficient. Iron’s strong magnetism is coupled to its mechanical properties. This confounds spin-agnostic IPs because they implicitly use an expectation value across spin states for a given geometry. Thus, this work offers a novel ML engine employing: (1) novel basis functions that translate spin information into neural network (NN) inputs, (2) and novel NN architectures that improve their ability to learn and express relationships between geometry, spin, and energy. When applied to a broad dataset with high variance in both geometry and spin, the new bases achieve a 4x reduction in energy prediction error compared to the spin-agnostic Behler- Parrinello (BP) framework, and 5x using both the new bases and new NN architecture. When applied to a high spin-variance dataset, the new bases reduce energy prediction error by over 10x. Even when applied to a dataset with low spin-variance, the new bases reduce energy prediction error by 45%. These predictive improvements come at an increased computational cost of about 5% compared to spin-agnostic BP using only the new bases, but roughly 3x using both the new bases and NN. This work presents two physical predictions to further elucidate the capabilities and value of the Spin-Aware NN IP (SANNIP). First, Monte Carlo (MC) spin relaxations using SANNIP exhibit behavior consistent with hysteresis in that the relaxed spin state is dependent on its initial alignment. Second, MC spin relaxations resolve the temperature beyond which ferromagnetically initialized systems lose their magnetization to between 1100 and 1150K, which is roughly consistent with experimental measurement of the Curie Temperature (TC) of 1043K. The evaluation of numerical accuracy and physical predictions demonstrate the utility of the novel bases and NN architectures. Future work can generate a broader dataset and deploy SANNIP potentials in molecular dynamics (MD) seeking insight into the role of spin in mechanical properties, defect interactions, etc. Additional bases and can explicitly treat externally applied electric and magnetic fields. Further NN architecture innovations can incorporate transfer learning into treatment of multi-component systems. This work is foun- dational to and enabling of many new avenues of investigation in computational materials science with the aim of improving materials design, fabrication, remediation, recycling, and disposal.Ph.D

Reactor physics assessment of thick silicon carbide clad PWR fuels

CIVINS (Civilian Institutions) Thesis documentHigh temparature tolerance, chemical stability and low neutron affinity make silicon carbide (SiC) a potential fuel cladding material that may improve the economics and safety of light water reactors (LWRs). "Thick" SiC cladding (0.089 cm.) is easier (and thus more economical) to manufacture than SiC of conventional Zircaloy (Zr) classing thickness (0.05 cm.) Five fuels and clad combinations are analyzed: Zr with solid UO2 pellets, reduced fuel fraction "thick" SiC (Thick SiC) with annular UO2 pellets, Thick Sic with solid UO2/BeO pellets, reduced coolant fraction annular fuel with "Thick" SiC (Thick SiC RCF), and Thick Sic with solid PuO2/ThO2 pellets. CASMO-4E and SIMULATE-3 have been utilized to model the above in a 193 assembly, 4-loop Westinghouse pressurized water reactor (PWR). A new program, CSpy, has been written to use CASMO/SIMULATE to conduct optimization searches of burnable poison layouts and core reload patterns. All fuel/clad combinations have been modeled using 84 assembly reloads, and Thick SiC clad annular UO2 has been modeled using both 84 and 64 assembly reloads. Dual Binary Swap (DBS) optimization via three Objective Functions (OFs) has been applied to each clad/fuel/reload # case to produce a single reload enrichment equilibrium core reload map. The OFs have the goals of minimal peaking, balancing lower peaking with longer cycle length, or maximal cycle length. Results display the tradeoff betwween minimized peaking and maximized cycle length for each clad/fuel/reload # case. The presented Zr reference cases and Thick SiC RCF cases operate for an 18 month cycle at 3587 MWth using 4/3% and 4/8% enrichment, respectively. A 90% capacity factor was applied to all SiC cladding cases to reflect the challenge to introduction of a new fuel. The Thick SiC clad annular UO2 (84 reload cores) and Think SiC UO2/BeO exhibit similar reactor physics performance but require higher enrichments that 5%. The Thick SiC RCF annular UO2 fuel cases provide the required cycle length with less than 5% enrichment. The Thick SiC clad PuO/2/ThO2 cores can operate with a Pu% of heavy metal of about 12%, however they may have unacceptable shutdown margins without altering the control rod materials.http://archive.org/details/reactorphysicsss109454021