Nonuniform-spaced Critical Behavior of Dynamical Quantum Phase Transitions in Multi-band Bloch Hamiltonian

We investigate the dynamical quantum phase transition (DQPT) in the multi-band Bloch Hamiltonian of the one-dimensional periodic Kitaev model after a quench from a Bloch band. Our study goes beyond the limitations of previous works that primarily focused on two-band models and reveals significant differences in DQPT between the two-band and multi-band systems. Our results show that only the quench from the Bloch states, which causes the band gap to collapse at the critical point, induces the DQPT after crossing the quantum phase transition; otherwise, the DQPT will not occur. Additionally, the critical times of the DQPT are not evenly spaced due to the deviation in the critical momentum caused by the non-analytic singularities of the Pancharatnam geometric phase. Our findings provide a better understanding of the characteristics of non-equilibrium systems surrounding DQPTs.Comment: 9 pages, 10 figure

Dynamical relaxation behavior of extended XY chain with gapless phase following a quantum quench

We investigate the dynamical relaxation behavior of the two-point correlation in extended XY models with a gapless phase after quenches from various initial states. Specifically, we study the XY chain with gapless phase induced by the additional interactions: Dzyaloshinskii-Moriya interaction and XZY-YZX type of three-site interaction. When quenching from the gapped phase, we observe that the additional interactions have no effect on the relaxation behavior. The relaxation behavior is $\delta C_{mn}(t)\sim t^{-3/2}$ and $\sim t^{-1/2}$ for the quench to the commensurate phase and the incommensurate phase, respectively. However, when quenching from the gapless phase, we demonstrate that the scaling behavior of $\delta C_{mn}(t)$ is changed to $\sim t^{-1}$ for the quench to the commensurate phase, and the decay of $\delta C_{mn}(t)$ follows $\sim t^{-1}$ or $\sim t^{-1/2}$ for the quench to the incommensurate phase depending on the parameters of pre-quench Hamiltonian. We also establish the dynamical phase diagrams based on the dynamical relaxation behavior of $\delta C_{mn}(t)$ in the extended XY models.Comment: 12 pages, 10 figure

Dynamics of the Geometric Phase in Inhomogeneous Quantum Spin Chains

The dynamics of the geometric phase are studied in inhomogeneous quantum spin chains after a quench. Analytic expressions of the Pancharatnam geometric phase (PGP) $\mathcal{G}(t)$ are derived, for both the period-two quantum Ising chain (QIC) and the disordered QIC. In the period-two QIC, due to the periodic modulation, the PGP changes with time at the boundary of the Brillouin zone, and consequently, the winding number $\nu_{D}(t)=\int_{0}^{\pi}[\partial\phi_{k}^{G}(t)/\partial k]dk/2\pi$ based on the PGP is not quantized and thus not topological anymore. Nevertheless, the PGP and its winding number show non-analytic singularities at the critical times of the dynamical quantum phase transitions (DQPTs). This relation between the PGP and the DQPT is further confirmed in the disordered QIC, where the winding number is not defined. It is found that the critical time of DQPT inherited from the homogeneous system and the additional one induced by the weak disorder are also accompanied by the non-analytic singularity of the PGP, by decomposing the PGP into each quasiparticle mode. The connection between the non-analytic behavior of the PGP at the critical time and the DQPT, regardless of whether the winding number is topological, can be explained by the fact that they both arise when the Loschmidt amplitude vanishes.Comment: 14 pages, 8 figure

The expression and role of protein kinase C (PKC) epsilon in clear cell renal cell carcinoma

Protein kinase C epsilon (PKCε), an oncogene overexpressed in several human cancers, is involved in cell proliferation, migration, invasion, and survival. However, its roles in clear cell renal cell carcinoma (RCC) are unclear. This study aimed to investigate the functions of PKCε in RCC, especially in clear cell RCC, to determine the possibility of using it as a therapeutic target. By immunohistochemistry, we found that the expression of PKCε was up-regulated in RCCs and was associated with tumor Fuhrman grade and T stage in clear cell RCCs. Clone formation, wound healing, and Borden assays showed that down-regulating PKCε by RNA interference resulted in inhibition of the growth, migration, and invasion of clear cell RCC cell line 769P and, more importantly, sensitized cells to chemotherapeutic drugs as indicated by enhanced activity of caspase-3 in PKCε siRNA-transfected cells. These results indicate that the overexpression of PKCε is associated with an aggressive phenotype of clear cell RCC and may be a potential therapeutic target for this disease

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

A Dual Neural Architecture Combined SqueezeNet with OctConv for LiDAR Data Classification

Light detection and ranging (LiDAR) is a frequently used technique of data acquisition and it is widely used in diverse practical applications. In recent years, deep convolutional neural networks (CNNs) have shown their effectiveness for LiDAR-derived rasterized digital surface models (LiDAR-DSM) data classification. However, many excellent CNNs have too many parameters due to depth and complexity. Meanwhile, traditional CNNs have spatial redundancy because different convolution kernels scan and store information independently. SqueezeNet replaces a part of 3 × 3 convolution kernels in CNNs with 1 × 1 convolution kernels, decomposes the original one convolution layer into two layers, and encapsulates them into a Fire module. This structure can reduce the parameters of network. Octave Convolution (OctConv) pools some feature maps firstly and stores them separately from the feature maps with the original size. It can reduce spatial redundancy by sharing information between the two groups. In this article, in order to improve the accuracy and efficiency of the network simultaneously, Fire modules of SqueezeNet are used to replace the traditional convolution layers in OctConv to form a new dual neural architecture: OctSqueezeNet. Our experiments, conducted using two well-known LiDAR datasets and several classical state-of-the-art classification methods, revealed that our proposed classification approach based on OctSqueezeNet is able to provide competitive advantages in terms of both classification accuracy and computational amount

Genome-Wide Analysis of TCP Transcription Factors and Their Expression Pattern Analysis of Rose Plants (<i>Rosa chinensis</i>)

The plant-specific transcription factor TEOSINTE BRANCHED, CYCLOIDEA, AND PROLIFERATING CELL FACTOR (TCP) gene family plays vital roles in various biological processes, including growth and development, hormone signaling, and stress responses. However, there is a limited amount of information regarding the TCP gene family in roses (Rosa sp.). In this study, we identified 18 TCP genes in the rose genome, which were further classified into two subgroups (Group A and Group B) via phylogenetic analysis. Comprehensive characterization of these TCP genes was performed, including gene structure, motif composition, chromosomal location, and expression profiles. Synteny analysis revealed that a few TCP genes are involved in segmental duplication events, indicating that these genes played an important role in the expansion of the TCP gene family in roses. This suggests that segmental duplication events have caused the evolution of the TCP gene family and may have generated new functions. Our study provides an insight into the evolutionary and functional characteristics of the TCP gene family in roses and lays a foundation for the future exploration of the regulatory mechanisms of TCP genes in plant growth and development

Insights of Oxygen Reduction Selectivity and Fe Leaching from Fe-N-C Nanozyme in Ascorbate Oxidation

Ascorbic acid (H2A) is a well-known antioxidant to protect cellular components from free radical damage, meanwhile it is also emerged as pro-oxidant in cancer therapy. However, such contradictory mechanisms underlying H2A oxidation are not well understood. Here, we report the discovery of Fe leaching during catalytic H2A oxidation using Fe-N-C nanozyme as a ferritin mimic and its influence in selectivity of oxygen reduction reaction (ORR). Owing to the heterogeneity, Fe-Nx sites in Fe-N-C primarily catalyzed the H2A oxidation and 4e ORR via an iron-oxo intermediate; meanwhile marginal N-C sites catalyzed the 2e ORR via an O2 intermediate with H2O2 production, although which was less favorable in kinetics and hardly observable in the early stage. Nonetheless, trace O2 accumulated and attacked Fe-Nx sites, leading to a linear leakage of unstable Fe ions up to 420 ppb when the concentration of H2A increased. As a result, a substantial fraction (~40%) of N-C sites on Fe-N-C were activated, and a new path for Fenton-type H2A oxidation was finally enabled. After Fe ions diffused into the bulk solution, the ORR at the N-C sites stopped at H2O2 production, which was the origin for the pro-oxidant effect by H2A. This work highlights the Fe-leakage occurring on Fe-N-C nanozymes and uncovers the multifaceted insights of ORR selectivity in H2A oxidation under realistic conditions