Output Feedback Fractional-Order Nonsingular Terminal Sliding Mode Control of Underwater Remotely Operated Vehicles

For the 4-DOF (degrees of freedom) trajectory tracking control problem of underwater remotely operated vehicles (ROVs) in the presence of model uncertainties and external disturbances, a novel output feedback fractional-order nonsingular terminal sliding mode control (FO-NTSMC) technique is introduced in light of the equivalent output injection sliding mode observer (SMO) and TSMC principle and fractional calculus technology. The equivalent output injection SMO is applied to reconstruct the full states in finite time. Meanwhile, the FO-NTSMC algorithm, based on a new proposed fractional-order switching manifold, is designed to stabilize the tracking error to equilibrium points in finite time. The corresponding stability analysis of the closed-loop system is presented using the fractional-order version of the Lyapunov stability theory. Comparative numerical simulation results are presented and analyzed to demonstrate the effectiveness of the proposed method. Finally, it is noteworthy that the proposed output feedback FO-NTSMC technique can be used to control a broad range of nonlinear second-order dynamical systems in finite time

Phonon anharmonicity and negative thermal expansion in SnSe

The anharmonic phonon properties of SnSe in the Pnma phase were investigated with a combination of experiments and first-principles simulations. Using inelastic neutron scattering (INS) and nuclear resonant inelastic X-ray scattering (NRIXS), we have measured the phonon dispersions and density of states (DOS) and their temperature dependence, which revealed a strong, inhomogeneous shift and broadening of the spectrum on warming. First-principles simulations were performed to rationalize these measurements, and to explain the previously reported anisotropic thermal expansion, in particular the negative thermal expansion within the Sn-Se bilayers. Including the anisotropic strain dependence of the phonon free energy, in addition to the electronic ground state energy, is essential to reproduce the negative thermal expansion. From the phonon DOS obtained with INS and additional calorimetry measurements, we quantify the harmonic, dilational, and anharmonic components of the phonon entropy, heat capacity, and free energy. The origin of the anharmonic phonon thermodynamics is linked to the electronic structure.Comment: 14 pages, 12 figure

Backdoor Attack on Hash-based Image Retrieval via Clean-label Data Poisoning

A backdoored deep hashing model is expected to behave normally on original query images and return the images with the target label when a specific trigger pattern presents. To this end, we propose the confusing perturbations-induced backdoor attack (CIBA). It injects a small number of poisoned images with the correct label into the training data, which makes the attack hard to be detected. To craft the poisoned images, we first propose the confusing perturbations to disturb the hashing code learning. As such, the hashing model can learn more about the trigger. The confusing perturbations are imperceptible and generated by optimizing the intra-class dispersion and inter-class shift in the Hamming space. We then employ the targeted adversarial patch as the backdoor trigger to improve the attack performance. We have conducted extensive experiments to verify the effectiveness of our proposed CIBA. Our code is available at https://github.com/KuofengGao/CIBA.Comment: Accepted by BMVC 202

GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph

Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning of vision-language models (VLMs) under the low-data regime, where only a few additional parameters are introduced to excavate the task-specific knowledge based on the general and powerful representation of VLMs. However, most adapter-style works face two limitations: (i) modeling task-specific knowledge with a single modality only; and (ii) overlooking the exploitation of the inter-class relationships in downstream tasks, thereby leading to sub-optimal solutions. To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dual-modality structure knowledge (i.e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph. In particular, the dual knowledge graph is established with two sub-graphs, i.e., a textual knowledge sub-graph, and a visual knowledge sub-graph, where the nodes and edges represent the semantics/classes and their correlations in two modalities, respectively. This enables the textual feature of each prompt to leverage the task-specific structure knowledge from both textual and visual modalities, yielding a more effective classifier for downstream tasks. Extensive experimental results on 11 benchmark datasets reveal that our GraphAdapter significantly outperforms previous adapter-based methods. The code will be released at https://github.com/lixinustc/GraphAdapterComment: Accepted by NeurIPS 2023. The manuscript will be further revised based on the review

Deep Multimodal Fusion for Generalizable Person Re-identification

Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a competitive performance on a specific target domain. However, when Re-ID models are directly deployed in a new domain without target samples, they always suffer from considerable performance degradation and poor domain generalization. To address this challenge, in this paper, we propose DMF, a Deep Multimodal Fusion network for the general scenarios on person re-identification task, where rich semantic knowledge is introduced to assist in feature representation learning during the pre-training stage. On top of it, a multimodal fusion strategy is introduced to translate the data of different modalities into the same feature space, which can significantly boost generalization capability of Re-ID model. In the fine-tuning stage, a realistic dataset is adopted to fine-tine the pre-trained model for distribution alignment with real-world. Comprehensive experiments on benchmarks demonstrate that our proposed method can significantly outperform previous domain generalization or meta-learning methods. Our source code will also be publicly available at https://github.com/JeremyXSC/DMF

Matryoshka Phonon Twinning in alpha-GaN

Understanding lattice dynamics is crucial for effective thermal management in high-power electronic devices because phonons dominate thermal transport in most semiconductors. This study utilizes complementary inelastic X-ray and neutron scattering techniques and reports the temperature-dependent phonon dynamics of alpha-GaN, one of the most important third-generation power semiconductors. A prominent Matryoshka phonon dispersion is discovered with the scattering tools and confirmed by the first-principles calculations. Such Matryoshka twinning throughout the three-dimension reciprocal space is demonstrated to amplify the anharmonicity of the related phonon modes through creating abundant three-phonon scattering channels and cutting the phonon lifetime of affected modes by more than 50%. Such phonon topology effectively contributes to the reduction of the in-plane thermal transport, thus the anisotropic thermal conductivity of alpha-GaN. The results not only have significant implications for engineering the thermal performance and other phonon-related properties of alpha-GaN, but also offer valuable insights on the role of anomalous phonon topology in thermal transport of other technically important semiconductors.Comment: 34 pages, 15 figure

Hetero2^2Net: Heterophily-aware Representation Learning on Heterogenerous Graphs

Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs) in handling homogeneous graphs with heterophily, little work has been conducted on investigating the heterophily properties in the context of heterogeneous graphs. To bridge this research gap, we identify the heterophily in heterogeneous graphs using metapaths and propose two practical metrics to quantitatively describe the levels of heterophily. Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily. To address the challenge, we present Hetero$^2$Net, a heterophily-aware HGNN that incorporates both masked metapath prediction and masked label prediction tasks to effectively and flexibly handle both homophilic and heterophilic heterogeneous graphs. We evaluate the performance of Hetero$^2$Net on five real-world heterogeneous graph benchmarks with varying levels of heterophily. The results demonstrate that Hetero$^2$Net outperforms strong baselines in the semi-supervised node classification task, providing valuable insights into effectively handling more complex heterogeneous graphs.Comment: Preprin

Discrete Dimers of Redox-Active and Fluorescent Perylene Diimide-Based Rigid Isosceles Triangles in the Solid State

The development of rigid covalent chiroptical organic materials, with multiple, readily available redox states, which exhibit high photoluminescence, is of particular importance in relation to both organic electronics and photonics. The chemically stable, thermally robust, and redox-active perylene diimide (PDI) fluorophores have received ever-increasing attention owing to their excellent fluorescence quantum yields in solution. Planar PDI derivatives, however, generally suffer from aggregation-caused emission quenching in the solid state. Herein, we report on the design and synthesis of two chiral isosceles triangles, wherein one PDI fluorophore and two pyromellitic diimide (PMDI) or naphthalene diimide (NDI) units are arranged in a rigid cyclic triangular geometry. The optical, electronic, and magnetic properties of the rigid isosceles triangles are fully characterized by a combination of optical spectroscopies, X-ray diffraction (XRD), cyclic voltammetry, and computational modeling techniques. Single-crystal XRD analysis shows that both isosceles triangles form discrete, nearly cofacial PDI–PDI π-dimers in the solid state. While the triangles exhibit fluorescence quantum yields of almost unity in solution, the dimers in the solid state exhibit very weak—yet at least an order of magnitude higher—excimer fluorescence yield in comparison with the almost completely quenched fluorescence of a reference PDI. The triangle containing both NDI and PDI subunits shows superior intramolecular energy transfer from the lowest excited singlet state of the NDI to that of the PDI subunit. Cyclic voltammetry suggests that both isosceles triangles exhibit multiple, easily accessible, and reversible redox states. Applications beckon in arenas related to molecular optoelectronic devices