Deconfinement Phase Transition Heating and Thermal Evolution of Neutron Stars

The deconfinement phase transition will lead to the release of latent heat during spins down of neutron stars if the transition is the first-order one.We have investigated the thermal evolution of neutron stars undergoing such deconfinement phase transition. The results show that neutron stars may be heated to higher temperature.This feature could be particularly interesting for high temperature of low-magnetic field millisecond pulsar at late stage.Comment: 4 pages, to be published by American Institute of Physics, ed. D.Lai, X.D.Li and Y.F.Yuan, as the Proceedings of the conference Astrophysics of Compact Object

Generalized Rindler Wedge and Holographic Observer Concordance

We study the most general horizons of accelerating observers and find that in a general spacetime, only spacelike surfaces satisfying a global condition could become horizons of well-defined accelerating observers, which we name the Rindler-convexity condition. The entanglement entropy associated with a Rindler-convex region is proportional to the area of the enclosing surface. This observer physics provides a novel perspective to define a well-defined subregion in spacetime, named the generalized Rindler wedge, whose degrees of freedom should be fully encoded within the subregion. We propose the holographic interpretation of generalized Rindler wedges and provide evidence from the observer correspondence, the subregion subalgebra duality, and the equality of the entanglement entropy, respectively. We introduce time/space cutoffs in the bulk to substantiate this proposition, generalize it, and establish a holographic observer concordance framework, which asserts that the partitioning of degrees of freedom through observation is holographically concordant.Comment: v2: 41 pages, 9 figures; major expansion for the GRW spacetime subregion duality and observer concordanc

Corner-to-Center Long-range Context Model for Efficient Learned Image Compression

In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time resulting from the serial autoregressive context model, the parallel context model has been proposed as an alternative that necessitates only two passes during the decoding phase, thus facilitating efficient image compression in real-world scenarios. However, performance degradation occurs due to its incomplete casual context. To tackle this issue, we conduct an in-depth analysis of the performance degradation observed in existing parallel context models, focusing on two aspects: the Quantity and Quality of information utilized for context prediction and decoding. Based on such analysis, we propose the \textbf{Corner-to-Center transformer-based Context Model (C$^3$M)} designed to enhance context and latent predictions and improve rate-distortion performance. Specifically, we leverage the logarithmic-based prediction order to predict more context features from corner to center progressively. In addition, to enlarge the receptive field in the analysis and synthesis transformation, we use the Long-range Crossing Attention Module (LCAM) in the encoder/decoder to capture the long-range semantic information by assigning the different window shapes in different channels. Extensive experimental evaluations show that the proposed method is effective and outperforms the state-of-the-art parallel methods. Finally, according to the subjective analysis, we suggest that improving the detailed representation in transformer-based image compression is a promising direction to be explored

Increase in neuroexcitability of unmyelinated C-type vagal ganglion neurons during initial postnatal development of visceral afferent reflex functions

BACKGROUND: Baroreflex gain increase up closely to adult level during initial postnatal weeks, and any interruption within this period will increase the risk of cardiovascular problems in later of life span. We hypothesize that this short period after birth might be critical for postnatal development of vagal ganglion neurons (VGNs). METHODS: To evaluate neuroexcitability evidenced by discharge profiles and coordinate changes, ion currents were collected from identified A- and C-type VGNs at different developmental stages using whole-cell patch clamping. RESULTS: C-type VGNs underwent significant age-dependent transition from single action potential (AP) to repetitive discharge. The coordinate changes between TTX-S and TTX-R Na(+) currents were also confirmed and well simulated by computer modeling. Although 4-AP or iberiotoxin age dependently increased firing frequency, AP duration was prolonged in an opposite fashion, which paralleled well with postnatal changes in 4-AP- and iberiotoxin-sensitive K(+) current activity, whereas less developmental changes were verified in A-types. CONCLUSION: These data demonstrate for the first time that the neuroexcitability of C-type VGNs increases significantly compared with A-types within initial postnatal weeks evidenced by AP discharge profiles and coordinate ion channel changes, which explain, at least in part, that initial postnatal weeks may be crucial for ontogenesis in visceral afferent reflex function

Benchmarking knowledge-driven zero-shot learning

External knowledge (a.k.a. side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge, such as text and attribute, have been widely investigated, but they alone are limited with incomplete semantics. Some very recent studies thus propose to use Knowledge Graph (KG) due to its high expressivity and compatibility for representing kinds of knowledge. However, the ZSL community is still in short of standard benchmarks for studying and comparing different external knowledge settings and different KG-based ZSL methods. In this paper, we proposed six resources covering three tasks, i.e., zero-shot image classification (ZS-IMGC), zero-shot relation extraction (ZS-RE), and zero-shot KG completion (ZS-KGC). Each resource has a normal ZSL benchmark and a KG containing semantics ranging from text to attribute, from relational knowledge to logical expressions. We have clearly presented these resources including their construction, statistics, data formats and usage cases w.r.t. different ZSL methods. More importantly, we have conducted a comprehensive benchmarking study, with two general and state-of-the-art methods, two setting-specific methods and one interpretable method. We discussed and compared different ZSL paradigms w.r.t. different external knowledge settings, and found that our resources have great potential for developing more advanced ZSL methods and more solutions for applying KGs for augmenting machine learning. All the resources are available at https://github.com/China-UK-ZSL/Resources_for_KZSL.Comment: Published in Journal of Web Semantics, 2022. Final version please refer to our Github repository

Systematic analysis of leucine-rich repeat disease resistance genes in maize

Leucine-rich repeat disease resistance (LRRDR) genes are important for defending plants from a range of pathogens. However, little information has been reported on the systematic analysis of LRRDR genes in maize. In this study, 235 LRRDR genes were identified in the complete genome sequence of maize (Zea mays cv. B73), classified as six different structural types, and then characterized based on conserved protein motifs, chromo- somal locations and gene duplications. Subsequent phylogenetic comparisons indicated that ~20 pairs of maize LRRDR proteins possessed high similarities to LRRDR proteins with known functions. Analyses of the physical locations and duplications of LRRDR genes indicated that gene duplication events involving LRRDR genes were high in maize and 84% occurred between chromosomes, which may ensure the functional performance and en- hancement of maize LRRDR genes. Meanwhile, the functions and expression patterns of the LRRDR genes were associated with their conserved protein secondary structures, suggesting that different conserved domains might distinguish their biological functions. Transcripts of 13 genes were regulated by two or more fungal pathogens, respectively, indicating that one LRRDR gene might mediate resistance to multiple fungal pathogens, suggest- ing that the signal networks of the maize-fungal pathogen interactions were partially crossed. Additionally, we screened five candidate LRRDR genes for ear rot resistance. The results reported in this study contribute to an improved understanding of the LRRDR gene family in maize