Search for: All records

This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask R-CNN with SORT-based tracking techniques, the framework is capable of analyzing each bubble's location, dimensions, interface shape, and velocity over its lifetime and capturing dynamic events such as bubble departure. BubbleID is trained and tested on boiling images across diverse heater surfaces and operational settings. This paper also offers a comparative analysis of bubble interface dynamics prior to and post-critical heat flux conditions. 

ABSTRACT The International Pulsar Timing Array (IPTA)’s second data release (IPTA DR2) combines decades of observations of 65 millisecond pulsars from 7 radio telescopes. IPTA data sets should be the most sensitive data sets to nanohertz gravitational waves (GWs), but take years to assemble, often excluding valuable recent data. To address this, we introduce the IPTA ‘Lite’ analysis, where a Figure of Merit is used to select an optimal PTA data set to analyse for each pulsar, enabling immediate access to new data and preliminary results prior to full combination. We test the capabilities of the Lite analysis using IPTA DR2, finding that ‘DR2 Lite’ can be used to detect the common red noise process with an amplitude of $$A = 4.8^{+1.8}_{-1.8} \times 10^{-15}$$ at $$\gamma = 13/3$$. This amplitude is slightly large in comparison to the combined analysis, and likely biased high as DR2 Lite is more sensitive to systematic errors from individual pulsars than the full data set. Furthermore, although there is no strong evidence for Hellings-Downs correlations in IPTA DR2, we still find the full data set is better at resolving Hellings-Downs correlations than DR2 Lite. Alongside the Lite analysis, we also find that analysing a subset of pulsars from IPTA DR2, available at a hypothetical ‘early’ stage of combination (EDR2), yields equally competitive results as the full data set. Looking ahead, the Lite method will enable rapid synthesis of the latest PTA data, offering preliminary GW constraints before the superior full data set combinations are available. 

Abstract Geometrically frustrated kagome lattices are raising as novel platforms to engineer correlated topological electron flat bands that are prominent to electronic instabilities. Here, we demonstrate a phonon softening at thek_z = πplane in ScV₆Sn₆. The low energy longitudinal phonon collapses at ~98 K andq = $$\frac{1}{3}\frac{1}{3}\frac{1}{2}$$ $\frac{1}{3}\frac{1}{3}\frac{1}{2}$due to the electron-phonon interaction, without the emergence of long-range charge order which sets in at a different propagation vectorq_CDW = $$\frac{1}{3}\frac{1}{3}\frac{1}{3}$$ $\frac{1}{3}\frac{1}{3}\frac{1}{3}$. Theoretical calculations corroborate the experimental finding to indicate that the leading instability is located at$$\frac{1}{3}\frac{1}{3}\frac{1}{2}$$ $\frac{1}{3}\frac{1}{3}\frac{1}{2}$of a rather flat mode. We relate the phonon renormalization to the orbital-resolved susceptibility of the trigonal Sn atoms and explain the approximately flat phonon dispersion. Our data report the first example of the collapse of a kagome bosonic mode and promote the 166 compounds of kagomes as primary candidates to explore correlated flat phonon-topological flat electron physics. 

Pairs of interacting transcription factors (TFs) have previously been shown to bind to enhancers and promoters and contribute to their physical interactions. However, to date, we have limited knowledge about such TF pairs. To fill this void, we systematically studied the co-occurrence of TF-binding motifs in interacting enhancer–promoter (EP) pairs in seven human cell lines. We discovered 423 motif pairs that significantly co-occur in enhancers and promoters of interacting EP pairs. We demonstrated that these motif pairs are biologically meaningful and significantly enriched with motif pairs of known interacting TF pairs. We also showed that the identified motif pairs facilitated the discovery of the interacting EP pairs. The developed pipeline, EPmotifPair, together with the predicted motifs and motif pairs, is available at https://doi.org/10.6084/m9.figshare.14192000. Our study provides a comprehensive list of motif pairs that may contribute to EP physical interactions, which facilitate generating meaningful hypotheses for experimental validation.