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As the number of pre-trained machine learning (ML) models is growing exponentially, data reduction tools are not catching up. Existing data reduction techniques are not specifically designed for pre-trained model (PTM) dataset files. This is largely due to a lack of understanding of the patterns and characteristics of these datasets, especially those relevant to data reduction and compressibility.

This paper presents the first, exhaustive analysis to date of PTM datasets on storage compressibility. Our analysis spans different types of data reduction and compression techniques, from hash-based data deduplication, data similarity detection, to dictionary-coding compression. Our analysis explores these techniques at three data granularity levels, from model layers, model chunks, to model parameters. We draw new observations that indicate that modern data reduction tools are not effective when handling PTM datasets. There is a pressing need for new compression methods that take into account PTMs' data characteristics for effective storage reduction.

Motivated by our findings, we design Elf, a simple yet effective, error-bounded, lossy floating-point compression method. Elf transforms floating-point parameters in such a way that the common exponent field of the transformed parameters can be completely eliminated to save storage space. We develop Elves, a compression framework that integrates Elf along with several other data reduction methods. Elves uses the most effective method to compress PTMs that exhibit different patterns. Evaluation shows that Elves achieves an overall compression ratio of 1.52×, which is 1.31×, 1.32× and 1.29× higher than a general-purpose compressor (zstd), an error-bounded lossy compressor (SZ3), and the uniform model quantization, respectively, with negligible model accuracy loss.

 

Thermosets are well known for their advantages such as high stability and chemical resistance. However, developing sustainable thermosets with degradability and recyclability faces several principal challenges, including reconciling the desired characteristics during service with the recycling and reprocessing properties required at the end of life, establishing efficient methods for large‐scale synthesis, and aligning with current manufacturing process. Here a general strategy is presented for the on‐demand degradation and recycling of thermosets under mild conditions utilizing dynamic precursors with dual‐factor‐controlled reversibility. Specifically, dynamic triazine crosslinkers are introduced through dynamic nucleophilic aromatic substitution (S_NAr) into the precursor polyols used in polyurethane (PU) synthesis. Upon removal of the catalyst and alcohol, the reversibility of S_NAr is deactivated, allowing for the use of standard PU polymerization techniques such as injection molding, casting, and foaming. The resulting cyanurate‐crosslinked PUs maintain high stability and diverse mechanical properties of traditional crosslinked PUs, yet offer the advantage of easy on‐demand depolymerization for recycling by activating the reversibility of S_NAr under specific but mild conditions—a combination of base, alcohol, and mild heat. It is envisioned that this approach, involving the pre‐installation of dual‐factor‐controlled dynamic crosslinkers, can be broadly applied to current thermosetting plastic manufacturing processes, introducing enhanced sustainability.

 

Abstract The semiconductor tracker (SCT) is one of the tracking systems for charged particles in the ATLAS detector. It consists of 4088 silicon strip sensor modules.During Run 2 (2015–2018) the Large Hadron Collider delivered an integrated luminosity of 156 fb -1 to the ATLAS experiment at a centre-of-mass proton-proton collision energy of 13 TeV. The instantaneous luminosity and pile-up conditions were far in excess of those assumed in the original design of the SCT detector.Due to improvements to the data acquisition system, the SCT operated stably throughout Run 2.It was available for 99.9% of the integrated luminosity and achieved a data-quality efficiency of 99.85%.Detailed studies have been made of the leakage current in SCT modules and the evolution of the full depletion voltage, which are used to study the impact of radiation damage to the modules.