(111)(111) surface states of SnTe

The characterization and applications of topological insulators depend critically on their protected surface states, which, however, can be obscured by the presence of trivial dangling bond states. Our first principle calculations show that this is the case for the pristine $(111)$ surface of SnTe. Yet, the predicted surface states unfold when the dangling bond states are passivated in proper chemisorption. We further extract the anisotropic Fermi velocities, penetration lengths and anisotropic spin textures of the unfolded $\bar\Gamma$- and $\bar M$-surface states, which are consistent with the theory in http://dx.doi.org/10.1103/PhysRevB.86.081303 Phys. Rev. B 86, 081303 (R). More importantly, this chemisorption scheme provides an external control of the relative energies of different Dirac nodes, which is particularly desirable in multi-valley transport.Comment: 6 pages, 6 figure

Few-Shot Deep Adversarial Learning for Video-based Person Re-identification

Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages. Existing methods rely on supervision signals to optimise a projected space under which the distances between inter/intra-videos are maximised/minimised. However, this demands exhaustively labelling people across camera views, rendering them unable to be scaled in large networked cameras. Also, it is noticed that learning effective video representations with view invariance is not explicitly addressed for which features exhibit different distributions otherwise. Thus, matching videos for person re-ID demands flexible models to capture the dynamics in time-series observations and learn view-invariant representations with access to limited labeled training samples. In this paper, we propose a novel few-shot deep learning approach to video-based person re-ID, to learn comparable representations that are discriminative and view-invariant. The proposed method is developed on the variational recurrent neural networks (VRNNs) and trained adversarially to produce latent variables with temporal dependencies that are highly discriminative yet view-invariant in matching persons. Through extensive experiments conducted on three benchmark datasets, we empirically show the capability of our method in creating view-invariant temporal features and state-of-the-art performance achieved by our method.Comment: Appearing at IEEE Transactions on Image Processin

Bis[6-(3,5-dimethyl-1H-pyrazol-1-yl)picolinato]nickel(II)–aqua­[6-(3,5-dimethyl-1H-pyrazol-1-yl)picolinic acid]dithio­cyanato­nickel(II) (1/1)

In the title cocrystal, [Ni(C11H10N3O2)2]·[Ni(NCS)2(C11H11N3O2)(H2O)], both NiII ions are in disorted octa­hedral coordination environments. One NiII ion is coordinated by four N atoms and two O atoms from two tridentate 6-(3,5-dimethyl-1H-pyrazol-1-yl)picolinate (DPP) ligands, while the other NiII ion is coordinated by a tridentate 6-((3,5-dimethyl-1H-pyrazol-1-yl))picolinic acid (DPPH) ligand and by two N atoms and one O atom from two thio­cyanate and one water ligand, respectively. In the crystal structure, mol­ecules are linked by inter­molecular O—H⋯O and O—H⋯S hydrogen bonds, forming extended chains along [010]

Research on sound radiation characteristics of the high-speed train wheel

Taking the standard wheel model as an example, the radiation noise of a single wheel under excitation force which is computed by multi-body dynamics model is computed by acoustic boundary element method (BEM). Then, the damped wheel is proposed, and the sound radiation characteristics of both wheels are analyzed and compared. The results show that sound field of a single wheel presents an obvious directivity with petaloid change and continuous decrease, and the wheel tread and web contribute the most rolling noise. Compared with the standard wheel, the acoustic radiation power of the damped wheel decreased significantly, especially at the peak frequency. After that, the radiation noise generated by the wheel in the train is researched. The results show that the radiation noise generated by the wheel in the train is a complex sound field after the superposition and interference of multiple wheel noises, which are mainly in the bogies at both ends and its vicinity region. Meanwhile, the basic directivity characteristics of the petaloid change and continuous reduction are remained. The radiation noise which is generated by the wheel in the train has obvious peak characteristic, whose corresponding peak noises are below 110 dB. The radiation noise of the damped wheel is significantly smaller than that of the standard wheel at most frequency bands, and the total SPL at the observation point has decreased by 14.5 dB with obvious noise reduction effect. In order to further research the radiation noise of the damping wheel, influence factors on the noise reduction are analyzed. Finally, these parameters such as thickness and material should be considered comprehensively during designing the damping wheel, in order to find the optimal combination of all parameters

Protocol for preparation of highly durable superhydrophobic bulks with hierarchical porous structures

Superhydrophobic surfaces face challenges in comprehensive durability when used in extreme outdoor environments. Here, we present a protocol for preparing nanocomposite bulks with hierarchical structures using the template technique. We describe steps for using hybrid nanoparticles of polytetrafluoroethylene and multi-walled carbon nanotube to fill inside and dip on the polyurethane (PU) foam. We then detail procedures for its removal by sintering treatment. The extra accretion layer on the PU foam surface was highlighted to construct hierarchical porous structures. For complete details on the use and execution of this protocol, please refer to Wu et al.

ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning

While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust representation training methods that are designed for traditional RL become less effective when applied to GCRL. To address this challenge, we first propose the Semi-Contrastive Representation attack, a novel approach inspired by the adversarial contrastive attack. Unlike existing attacks in RL, it only necessitates information from the policy function and can be seamlessly implemented during deployment. Then, to mitigate the vulnerability of existing GCRL algorithms, we introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer to improve the adversarial robustness of the underlying RL agent against various types of perturbations. Extensive experiments validate the superior performance of our attack and defence methods across multiple state-of-the-art GCRL algorithms. Our tool ReRoGCRL is available at https://github.com/TrustAI/ReRoGCRL.Comment: This paper has been accepted in AAAI24 (https://aaai.org/aaai-conference/