Floquet engineering of long-range p-wave superconductivity: Beyond the high-frequency limit

It has been shown that long-range {\it p}-wave superconductivity in a Kitaev chain can be engineered via an ac field with a high frequency [Benito et al., Phys. Rev. B 90, 205127 (2014)]. For its experimental realization, however, theoretical understanding of Floquet engineering with a broader range of driving frequencies becomes important. In this work, focusing on the ac-driven tunneling interactions of a Kitaev chain, we investigate effects from the leading correction to the high-frequency limit on the emergent {\it p}-wave superconductivity. Importantly, we find new engineered long-range {\it p}-wave pairing interactions that can significantly alter the ones in the high-frequency limit at long interaction ranges. We also find that the leading correction additionally generates nearest-neighbor {\it p}-wave pairing interactions with a renormalized pairing energy, long-range tunneling interactions, and in particular multiple pairs of Floquet Majorana edge states that are destroyed in the high- frequency limit.Comment: 13 pages, 8 figure

Collective quantum phase slips in multiple nanowire junctions

Realization of robust coherent quantum phase slips represents a significant experimental challenge. Here we propose a new design consisting of multiple nanowire junctions to realize a phase-slip flux qubit. It admits good tunability provided by gate voltages applied on superconducting islands separating nanowire junctions. In addition, the gates and junctions can be identical or distinct to each other leading to symmetric and asymmetric setups. We find that the asymmetry can improve the performance of the proposed device, compared with the symmetric case. In particular, it can enhance the effective rate of collective quantum phase slips. Furthermore, we demonstrate how to couple two such devices via a mutual inductance. This is potentially useful for quantum gate operations. Our investigation on how symmetry in multiple nanowire junctions affects the device performance should be useful for the application of phase-slip flux qubits in quantum information processing and quantum metrology.Comment: 12 pages, 6 figure

Cooling a nanomechanical resonator by a triple quantum dot

We propose an approach for achieving ground-state cooling of a nanomechanical resonator (NAMR) capacitively coupled to a triple quantum dot (TQD). This TQD is an electronic analog of a three-level atom in $\Lambda$ configuration which allows an electron to enter it via lower-energy states and to exit only from a higher-energy state. By tuning the degeneracy of the two lower-energy states in the TQD, an electron can be trapped in a dark state caused by destructive quantum interference between the two tunneling pathways to the higher-energy state. Therefore, ground-state cooling of an NAMR can be achieved when electrons absorb readily and repeatedly energy quanta from the NAMR for excitations.Comment: 6 pages, 3 figure

Synthesis and inclusion behavior of a heterotritopic receptor based on hexahomotrioxacalix[3]arene

A heterotritopic hexahomotrioxacalix[3]arene receptor with the capability of binding two alkali metals and a transition metal in a cooperative fashion was synthesized. The binding model was investigated by using ¹H NMR titration experiments in CDCl₃–CD₃CN (10:1, v/v), and the results revealed that the transition metal was bound at the upper rim and the alkali metals at the lower and upper rims. Interestingly, the alkali metal ions Li⁺ and Na⁺ bind at the lower and upper rim respectively depending on the dimensions of the alkali metal ions versus the size of the cavities formed by the calix[3]arene derivative. The hexahomotrioxacalix[3]arene receptor acts as a heterotritopic receptor, binding with the transition metal ion Ag⁺ and the alkali metals ions Li⁺ and Na⁺. These findings were not applicable to other different sized alkali metals, such as K⁺ and Cs⁺

Identification of a novel submergence response gene regulated by the Sub1A gene

Submergence is one of the major constraints to rice production in many rice growing areas in the world. The Sub1A gene has been demonstrated to dramatically improve submergence tolerance in rice. Here, we report the identification of a novel submergence response (RS1) gene that is specifically induced in the Sub1A-mediated submergence tolerance response. Under submergence, RS1 was upregulated in M202 (Sub1A) but downregulated in M202 in RNA-seq and microarray assays. Expression analyses of various tissues and developmental stages show that RS1 mRNA levels are high in leaves and sheaths, but low in roots, stems, and panicles. Our results also show that RS1 is highly expressed under submergence, drought, and NaCl stresses, but not under cold or dehydration stress. Hormone ABA treatment induces, whereas GA treatment decreases, RS1 expression. The RS1 and Sub1A genes are co-regulated under submergence. Overexpression of RS1 in transgenic Kitaake (without Sub1A) and M202(Sub1A)×Kitaake do not result in enhanced submergence tolerance. Conversely, down-regulation of RS1 in M202(Sub1A)×Kitaake lead to weaken submergence tolerance. We hypothesize that RS1 may play a role in the Sub1A-mediated submergence tolerance pathway.Key word: Rice (Oryza sativa L.), submergence, RNA-seq, Sub1A, abiotic stress

SUIT: Learning Significance-guided Information for 3D Temporal Detection

3D object detection from LiDAR point cloud is of critical importance for autonomous driving and robotics. While sequential point cloud has the potential to enhance 3D perception through temporal information, utilizing these temporal features effectively and efficiently remains a challenging problem. Based on the observation that the foreground information is sparsely distributed in LiDAR scenes, we believe sufficient knowledge can be provided by sparse format rather than dense maps. To this end, we propose to learn Significance-gUided Information for 3D Temporal detection (SUIT), which simplifies temporal information as sparse features for information fusion across frames. Specifically, we first introduce a significant sampling mechanism that extracts information-rich yet sparse features based on predicted object centroids. On top of that, we present an explicit geometric transformation learning technique, which learns the object-centric transformations among sparse features across frames. We evaluate our method on large-scale nuScenes and Waymo dataset, where our SUIT not only significantly reduces the memory and computation cost of temporal fusion, but also performs well over the state-of-the-art baselines.Comment: Accepted to IROS 202