g-Factors and the Interplay of Collective and Single-Particle Degrees of Freedom in Superdeformed Mass-190 Nuclei

Interplay of collective and single-particle degrees of freedom is a common phenomenon in strongly correlated many-body systems. Despite many successful efforts in the study of superdeformed nuclei, there is still unexplored physics that can be best understood only through the nuclear magnetic properties. We point out that study of the gyromagnetic factor (g-factor) may open a unique opportunity for understanding superdeformed structure. Our calculations suggest that investigation of the g-factor dependence on spin and particle number can provide important information on single-particle structure and its interplay with collective motion in the superdeformed mass-190 nuclei. Modern experimental techniques combined with the new generation of sensitive detectors should be capable of testing our predictions.Comment: 4 pages, 2 eps figures, accepted by Phys. Rev.

Temporal Profiles and Spectral Lags of XRF 060218

The spectral and temporal properties of the non-thermal emission ofthe nearby XRF 060218 in 0.3-150 keV band are studied. We show that both the spectral energy distribution and the light curve properties suggest the same origin of the non-thermal emission detected by {\em Swift} BAT and XRT. This event has the longest pulse duration and spectral lag observed to date among the known GRBs. The pulse structure and its energy dependence are analogous to typical GRBs. By extrapolating the observed spectral lag to the {\em CGRO/BATSE} bands we find that the hypothesis that this event complies with the same luminosity-lag relation with bright GRBs cannot be ruled out at $2\sigma$ significance level. These intriguing facts, along with its compliance with the Amati-relation, indicate that XRF 060218 shares the similar radiation physics as typical GRBs.Comment: 9 pages in emulateapj format, including 4 figures and 1 table, accepted for publication in ApJ Letter

VAMP721 conformations unmask an extended motif for K+ channel binding and gating control

Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins play a major role in membrane fusion and contribute to cell expansion, signaling, and polar growth in plants. The SNARE SYP121 of Arabidopsis thaliana that facilitates vesicle fusion at the plasma membrane also binds with, and regulates, K+ channels already present at the plasma membrane to affect K+ uptake and K+-dependent growth. Here, we report that its cognate partner VAMP721, which assembles with SYP121 to drive membrane fusion, binds to the KAT1 K+ channel via two sites on the protein, only one of which contributes to channel-gating control. Binding to the VAMP721 SNARE domain suppressed channel gating. By contrast, interaction with the amino-terminal longin domain conferred specificity on VAMP721 binding without influencing gating. Channel binding was defined by a linear motif within the longin domain. The SNARE domain is thought to wrap around this structure when not assembled with SYP121 in the SNARE complex. Fluorescence lifetime analysis showed that mutations within this motif, which suppressed channel binding and its effects on gating, also altered the conformational displacement between the VAMP721 SNARE and longin domains. The presence of these two channel-binding sites on VAMP721, one also required for SNARE complex assembly, implies a well-defined sequence of events coordinating K+ uptake and the final stages of vesicle traffic. It suggests that binding begins with VAMP721, and subsequently with SYP121, thereby coordinating K+ channel gating during SNARE assembly and vesicle fusion. Thus, our findings also are consistent with the idea that the K+ channels are nucleation points for SNARE complex assembly

Cartography Active Learning

We propose Cartography Active Learning (CAL), a novel Active Learning (AL) algorithm that exploits the behavior of the model on individual instances during training as a proxy to find the most informative instances for labeling. CAL is inspired by data maps, which were recently proposed to derive insights into dataset quality (Swayamdipta et al., 2020). We compare our method on popular text classification tasks to commonly used AL strategies, which instead rely on post-training behavior. We demonstrate that CAL is competitive to other common AL methods, showing that training dynamics derived from small seed data can be successfully used for AL. We provide insights into our new AL method by analyzing batch-level statistics utilizing the data maps. Our results further show that CAL results in a more data-efficient learning strategy, achieving comparable or better results with considerably less training data.Comment: In Findings EMNLP 202