4,541 research outputs found
Fermi-surface reconstruction involving two Van Hove singularities across the antiferromagnetic transition in BaFe2As2
We report an angle-resolved photoemission study of BaFe2As2, a parent
compound of iron-based superconductors. Low-energy tunable excitation photons
have allowed the first observation of a saddle-point singularity at the Z
point, as well as the Gamma point. With antiferromagnetic ordering, both of
these two van Hove singularities come down below the Fermi energy, leading to a
topological change in the innermost Fermi surface around the kz axis from
cylindrical to tear-shaped, as expected from first-principles calculation.
These singularities may provide an additional instability for the Fermi surface
of the superconductors derived from BaFe2As2.Comment: 14 pages, 4 figures, 1 tabl
Functional chromatin features are associated with structural mutations in cancer.
BACKGROUND: Structural mutations (SMs) play a major role in cancer development. In some cancers, such as breast and ovarian, DNA double-strand breaks (DSBs) occur more frequently in transcribed regions, while in other cancer types such as prostate, there is a consistent depletion of breakpoints in transcribed regions. Despite such regularity, little is understood about the mechanisms driving these effects. A few works have suggested that protein binding may be relevant, e.g. in studies of androgen receptor binding and active chromatin in specific cell types. We hypothesized that this behavior might be general, i.e. that correlation between protein-DNA binding (and open chromatin) and breakpoint locations is common across divergent cancers.
RESULTS: We investigated this hypothesis by comprehensively analyzing the relationship among 457 ENCODE protein binding ChIP-seq experiments, 125 DnaseI and 24 FAIRE experiments, and 14,600 SMs from 8 diverse cancer datasets covering 147 samples. In most cancers, including breast and ovarian, we found enrichment of protein binding and open chromatin in the vicinity of SM breakpoints at distances up to 200 kb. Furthermore, for all cancer types we observed an enhanced enrichment in regions distant from genes when compared to regions proximal to genes, suggesting that the SM-induction mechanism is independent from the bias of DSBs to occur near transcribed regions. We also observed a stronger effect for sites with more than one protein bound.
CONCLUSIONS: Protein binding and open chromatin state are associated with nearby SM breakpoints in many cancer datasets. These observations suggest a consistent mechanism underlying SM locations across different cancers
Density of Bloch Waves after a Quench
Production of Bloch waves during a rapid quench is studied by analytical and
numerical methods. The density of Bloch waves decays exponentially with the
quench time. It also strongly depends on temperature. Very few textures are
produced for temperatures lower than a characteristic temperature proportional
to the square of the magnetic field.Comment: 4 pages in RevTex + 3 .ps files; improved presentation; version to
appear in PR
Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models
We study the task of zero-shot vision-and-language navigation (ZS-VLN), a
practical yet challenging problem in which an agent learns to navigate
following a path described by language instructions without requiring any
path-instruction annotation data. Normally, the instructions have complex
grammatical structures and often contain various action descriptions (e.g.,
"proceed beyond", "depart from"). How to correctly understand and execute these
action demands is a critical problem, and the absence of annotated data makes
it even more challenging. Note that a well-educated human being can easily
understand path instructions without the need for any special training. In this
paper, we propose an action-aware zero-shot VLN method (Nav) by exploiting
the vision-and-language ability of foundation models. Specifically, the
proposed method consists of an instruction parser and an action-aware
navigation policy. The instruction parser utilizes the advanced reasoning
ability of large language models (e.g., GPT-3) to decompose complex navigation
instructions into a sequence of action-specific object navigation sub-tasks.
Each sub-task requires the agent to localize the object and navigate to a
specific goal position according to the associated action demand. To accomplish
these sub-tasks, an action-aware navigation policy is learned from freely
collected action-specific datasets that reveal distinct characteristics of each
action demand. We use the learned navigation policy for executing sub-tasks
sequentially to follow the navigation instruction. Extensive experiments show
Nav achieves promising ZS-VLN performance and even surpasses the
supervised learning methods on R2R-Habitat and RxR-Habitat datasets
- …