Unsupervised machine learning for detection of phase transitions in off-lattice systems I. Foundations

We demonstrate the utility of an unsupervised machine learning tool for the detection of phase transitions in off-lattice systems. We focus on the application of principal component analysis (PCA) to detect the freezing transitions of two-dimensional hard-disk and three-dimensional hard-sphere systems as well as liquid-gas phase separation in a patchy colloid model. As we demonstrate, PCA autonomously discovers order-parameter-like quantities that report on phase transitions, mitigating the need for a priori construction or identification of a suitable order parameter--thus streamlining the routine analysis of phase behavior. In a companion paper, we further develop the method established here to explore the detection of phase transitions in various model systems controlled by compositional demixing, liquid crystalline ordering, and non-equilibrium active forces

Unsupervised machine learning for detection of phase transitions in off-lattice systems II. Applications

We outline how principal component analysis (PCA) can be applied to particle configuration data to detect a variety of phase transitions in off-lattice systems, both in and out of equilibrium. Specifically, we discuss its application to study 1) the nonequilibrium random organization (RandOrg) model that exhibits a phase transition from quiescent to steady-state behavior as a function of density, 2) orientationally and positionally driven equilibrium phase transitions for hard ellipses, and 3) compositionally driven demixing transitions in the non-additive binary Widom-Rowlinson mixture

Treating random sequential addition via the replica method

While many physical processes are non-equilibrium in nature, the theory and modeling of such phenomena lag behind theoretical treatments of equilibrium systems. The diversity of powerful theoretical tools available to describe equilibrium systems has inspired strategies that map non-equilibrium systems onto equivalent equilibrium analogs so that interrogation with standard statistical mechanical approaches is possible. In this work, we revisit the mapping from the non-equilibrium random sequential addition process onto an equilibrium multi-component mixture via the replica method, allowing for theoretical predictions of non-equilibrium structural quantities. We validate the above approach by comparing the theoretical predictions to numerical simulations of random sequential addition.Comment: 32 pages, 3 figure

The Rhetoric of Restraint and the Ideology of Activism

Part of Symposium: The Rehnquist Court in Empirical and Statistical Retrospectiv

Widespread Inhibition of Posttranscriptional Splicing Shapes the Cellular Transcriptome following Heat Shock

During heat shock and other proteotoxic stresses, cells regulate multiple steps in gene expression in order to globally repress protein synthesis and selectively upregulate stress response proteins. Splicing of several mRNAs is known to be inhibited during heat stress, often meditated by SRp38, but the extent and specificity of this effect have remained unclear. Here, we examined splicing regulation genome-wide during heat shock in mouse fibroblasts. We observed widespread retention of introns in transcripts from ~1,700 genes, which were enriched for tRNA synthetase, nuclear pore, and spliceosome functions. Transcripts with retained introns were largely nuclear and untranslated. However, a group of 580+ genes biased for oxidation reduction and protein folding functions continued to be efficiently spliced. Interestingly, these unaffected transcripts are mostly cotranscriptionally spliced under both normal and stress conditions, whereas splicing-inhibited transcripts are mostly spliced posttranscriptionally. Altogether, our data demonstrate widespread repression of splicing in the mammalian heat stress response, disproportionately affecting posttranscriptionally spliced genes.Weizmann Institute of Science (Postdoctoral Award for Advancing Women in Science)European Molecular Biology Organization (Long-term Fellowship)Machiah FoundationNational Science Foundation (U.S.) (Grant 0821391)National Institutes of Health (U.S.