Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

/pdf/multivariate-analysis-in-metabolomics-3qjszfszcn.pdf

Multivariate Analysis in Metabolomics.

We update the capabilities of the software instrument Modules for Experiments in Stellar Astrophysics (MESA) and enhance its ease of use and availability. Our new approach to locating convective boundaries is consistent with the physics of convection, and yields reliable values of the convective-core mass during both hydrogen- and helium-burning phases. Stars with  become white dwarfs and cool to the point where the electrons are degenerate and the ions are strongly coupled, a realm now available to study with MESA due to improved treatments of element diffusion, latent heat release, and blending of equations of state. Studies of the final fates of massive stars are extended in MESA by our addition of an approximate Riemann solver that captures shocks and conserves energy to high accuracy during dynamic epochs. We also introduce a 1D capability for modeling the effects of Rayleigh–Taylor instabilities that, in combination with the coupling to a public version of the  radiation transfer instrument, creates new avenues for exploring Type II supernova properties. These capabilities are exhibited with exploratory models of pair-instability supernovae, pulsational pair-instability supernovae, and the formation of stellar-mass black holes. The applicability of MESA is now widened by the capability to import multidimensional hydrodynamic models into MESA. We close by introducing software modules for handling floating point exceptions and stellar model optimization, as well as four new software tools—  ,  -Docker,  , and mesastar.org—to enhance MESA's education and research impact.

https://iopscience.iop.org/article/10.3847/1538-4365/aaa5a8/pdf

Modules for Experiments in Stellar Astrophysics (MESA) : Convective Boundaries, Element Diffusion, and Massive Star Explosions

JavaScript Object Notation (JSON) is a lightweight, text-based,
language-independent data interchange format. It was derived from the
ECMAScript Programming Language Standard. JSON defines a small set of
formatting rules for the portable representation of structured data.
This document removes inconsistencies with other specifications of
JSON, repairs specification errors, and offers experience-based
interoperability guidance.

The JavaScript Object Notation (JSON) Data Interchange Format

Volume II: Privileged Architecture Privileged Architecture Version 1.10 Document Version 1.10 Warning! This draft specification may change before being accepted as standard by the RISC-V Foundation. While the editors intend future changes to this specification to be forward compatible, it remains possible that implementations made to this draft specification will not conform to the future standard.

/pdf/the-risc-v-instruction-set-manual-30b1gwzcmv.pdf

The RISC-V Instruction Set Manual

We update the capabilities of the software instrument Modules for Experiments in Stellar Astrophysics (MESA) and enhance its ease of use and availability. Our new approach to locating convective boundaries is consistent with the physics of convection, and yields reliable values of the convective core mass during both hydrogen and helium burning phases. Stars with $M<8\,{\rm M_\odot}$ become white dwarfs and cool to the point where the electrons are degenerate and the ions are strongly coupled, a realm now available to study with MESA due to improved treatments of element diffusion, latent heat release, and blending of equations of state. Studies of the final fates of massive stars are extended in MESA by our addition of an approximate Riemann solver that captures shocks and conserves energy to high accuracy during dynamic epochs. We also introduce a 1D capability for modeling the effects of Rayleigh-Taylor instabilities that, in combination with the coupling to a public version of the STELLA radiation transfer instrument, creates new avenues for exploring Type II supernovae properties. These capabilities are exhibited with exploratory models of pair-instability supernova, pulsational pair-instability supernova, and the formation of stellar mass black holes. The applicability of MESA is now widened by the capability of importing multi-dimensional hydrodynamic models into MESA. We close by introducing software modules for handling floating point exceptions and stellar model optimization, and four new software tools -- MESAWeb, MESA-Docker, pyMESA, and this http URL -- to enhance MESA's education and research impact.

Modules for Experiments in Stellar Astrophysics (MESA): Convective Boundaries, Element Diffusion, and Massive Star Explosions

IEEE Standard for Floating-Point Arithmetic

FP8 is a natural progression for accelerating deep learning training inference beyond the 16-bit formats common in modern processors. In this paper we propose an 8-bit ﬂoating point (FP8) binary interchange format consisting of two encodings - E4M3 (4-bit exponent and 3-bit mantissa) and E5M2 (5-bit exponent and 2-bit mantissa). While E5M2 follows IEEE 754 conventions for representatio of special values, E4M3’s dynamic range is extended by not representing inﬁnities and having only one mantissa bit-pattern for NaNs. We demonstrate the efﬁcacy of the FP8 format on a variety of image and language tasks, effectively matching the result quality achieved by 16-bit training sessions. Our study covers the main modern neural network architectures - CNNs, RNNs, and Transformer-based models, leaving all the hyperparameters unchanged from the 16-bit baseline training sessions. Our training experiments include large, up to 175B parameter, language models. We also examine FP8 post-training-quantization of language models trained using 16-bit formats that resisted ﬁxed point int8 quantization.

Michael Siu

Papers

IEEE Standard for Floating-Point Arithmetic

FP8 Formats for Deep Learning