An automated system extracts data from vector figures with bit-exact recovery for matplotlib markers, maps precision across renderers and formats, and provides injective verification via re-rendering certificates.
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IEEE Standard for Floating-Point Arithmetic. IEEE Std 754TM- 2019 (Revision of IEEE Std 754-2008)
12 Pith papers cite this work. Polarity classification is still indexing.
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Local attention in fixed-precision transformers introduces a second past operator in linear temporal logic, strictly increasing expressivity over global attention alone, with hybrids being most expressive.
LLMs match or exceed state-of-the-art traditional methods for stabilizing numerical expressions in scientific software, succeeding on 97.9% of expressions where baselines fail to improve accuracy, but struggle with control flow and high-precision literals.
Establishes sufficient more-general conditions for FastTwoSum as an error-free transformation under faithful rounding modes and introduces a configurable ExtractScalar splitting for round-to-odd.
Controlled benchmarks of five algorithms across six languages show C and C++ tied for fastest, Rust 9% behind, Julia 3.3x slower, Go 5x slower, and Python 315x slower, with workload-dependent rank shifts and differing memory footprints.
MixFP4 extends NVFP4 by adaptively selecting between two FP4 micro-formats per block using repurposed scale sign bits and a unified E2M2 compute path, claiming better accuracy than standard NVFP4 at 3.1% area and 1.5% power overhead.
Error analysis and cost estimator for recasting floating-point matrix multiplication as accumulated integer products on mixed-precision hardware.
PROMISE tool automates mixed-precision tuning with user-defined floating-point formats, validated on linear solvers and Rodinia benchmarks showing many variables can use lower precision safely.
The Parker-Sochacki method delivers 4 to 13 orders of magnitude better kinetic energy conservation than Runge-Kutta methods for charged particle motion in static magnetic fields while running faster at matched accuracy.
MaRDI Open Interfaces supplies common interfaces for nonlinear optimization solvers, shown via an application to physics-informed neural network training on the viscous Burgers' equation.
Updated microbenchmarks on modern CPUs show low-bit tagging fastest for symbolic workloads while NaN-boxing avoids allocation overhead for floats.
Augments incremental collision laws using the Bouc-Wen model to incorporate external forces as inputs, extends valid parameter ranges, and performs further identification studies on convex viscoplastic body collisions.
citing papers explorer
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Automated High-Precision Extraction and Forensic Verification of Data-Bearing Vector Figures
An automated system extracts data from vector figures with bit-exact recovery for matplotlib markers, maps precision across renderers and formats, and provides injective verification via re-rendering certificates.
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Characterizing the Expressivity of Local Attention in Transformers
Local attention in fixed-precision transformers introduces a second past operator in linear temporal logic, strictly increasing expressivity over global attention alone, with hybrids being most expressive.
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Assessing Large Language Models for Stabilizing Numerical Expressions in Scientific Software
LLMs match or exceed state-of-the-art traditional methods for stabilizing numerical expressions in scientific software, succeeding on 97.9% of expressions where baselines fail to improve accuracy, but struggle with control flow and high-precision literals.
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Odd but Error-Free FastTwoSum: More General Conditions for FastTwoSum as an Error-Free Transformation for Faithful Rounding Modes
Establishes sufficient more-general conditions for FastTwoSum as an error-free transformation under faithful rounding modes and introduces a configurable ExtractScalar splitting for round-to-odd.
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Behind Python: The Languages That Power AI
Controlled benchmarks of five algorithms across six languages show C and C++ tied for fastest, Rust 9% behind, Julia 3.3x slower, Go 5x slower, and Python 315x slower, with workload-dependent rank shifts and differing memory footprints.
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MixFP4: Enhancing NVFP4 with Adaptive FP4/INT4 Block Representations
MixFP4 extends NVFP4 by adaptively selecting between two FP4 micro-formats per block using repurposed scale sign bits and a unified E2M2 compute path, claiming better accuracy than standard NVFP4 at 3.1% area and 1.5% power overhead.
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Analysis of Floating-Point Matrix Multiplication Computed via Integer Arithmetic
Error analysis and cost estimator for recasting floating-point matrix multiplication as accumulated integer products on mixed-precision hardware.
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Floating-point autotuning with customized precisions
PROMISE tool automates mixed-precision tuning with user-defined floating-point formats, validated on linear solvers and Rodinia benchmarks showing many variables can use lower precision safely.
-
High-Accuracy Numerical Solutions of Particle Motion in Static Magnetic Fields
The Parker-Sochacki method delivers 4 to 13 orders of magnitude better kinetic energy conservation than Runge-Kutta methods for charged particle motion in static magnetic fields while running faster at matched accuracy.
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Software package MaRDI Open Interfaces for improved interoperability in numerical optimization
MaRDI Open Interfaces supplies common interfaces for nonlinear optimization solvers, shown via an application to physics-informed neural network training on the viscous Burgers' equation.
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Look Before You Leap: Checking In on Type Tag Checking
Updated microbenchmarks on modern CPUs show low-bit tagging fastest for symbolic workloads while NaN-boxing avoids allocation overhead for floats.
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Incremental Collision Laws Based on the Bouc-Wen Model: Improved Collision Models and Further Results
Augments incremental collision laws using the Bouc-Wen model to incorporate external forces as inputs, extends valid parameter ranges, and performs further identification studies on convex viscoplastic body collisions.