Bridging Sensor Gaps via Single-Direction Tuning for Hyperspectral Image Classification

Recently, some researchers started exploring the use of ViTs in tackling HSI classification and achieved remarkable results. However, the training of ViT models requires a considerable number of training samples, while hyperspectral data, due to its high annotation costs, typically has a relatively small number of training samples. This contradiction has not been effectively addressed. In this paper, aiming to solve this problem, we propose the single-direction tuning (SDT) strategy, which serves as a bridge, allowing us to leverage existing labeled HSI datasets even RGB datasets to enhance the performance on new HSI datasets with limited samples. The proposed SDT inherits the idea of prompt tuning, aiming to reuse pre-trained models with minimal modifications for adaptation to new tasks. But unlike prompt tuning, SDT is custom-designed to accommodate the characteristics of HSIs. The proposed SDT utilizes a parallel architecture, an asynchronous cold-hot gradient update strategy, and unidirectional interaction. It aims to fully harness the potent representation learning capabilities derived from training on heterologous, even cross-modal datasets. In addition, we also introduce a novel Triplet-structured transformer (Tri-Former), where spectral attention and spatial attention modules are merged in parallel to construct the token mixing component for reducing computation cost and a 3D convolution-based channel mixer module is integrated to enhance stability and keep structure information. Comparison experiments conducted on three representative HSI datasets captured by different sensors demonstrate the proposed Tri-Former achieves better performance compared to several state-of-the-art methods. Homologous, heterologous and cross-modal tuning experiments verified the effectiveness of the proposed SDT

Bis(1,3-diethyl­benzimidazolium) tetra­bromidomercurate(II)

In the title compound, (C11H15N2)2[HgBr4], the tetra­coordinated HgII center of the complex anion adopts a distorted tetra­hedral geometry [Hg—Br = 2.5755 (8)–2.623 (11) Å and Br—Hg—Br = 103.78 (19)–116.4 (3)°]. One of the Br atoms is disordered over two sites [site-occupancy factors = 0.51 (6) and 0.49 (6)]. The N—C—N angles in the cations are 110.7 (6) and 111.4 (7)°. In the crystal packing, a supra­molecular chain is formed via both weak inter­molecular C—H⋯Br hydrogen bonds and π–π aromatic ring stacking inter­actions [centroid–centroid separation = 3.803 (1) Å]

On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective

Our situated environment is full of uncertainty and highly dynamic, thus hindering the widespread adoption of machine-led Intelligent Decision-Making (IDM) in real world scenarios. This means IDM should have the capability of continuously learning new skills and efficiently generalizing across wider applications. IDM benefits from any new approaches and theoretical breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the barriers between tasks and applications. Recent research has well-examined neural architecture, Transformer, as a backbone foundation model and its generalization to various tasks, including computer vision, natural language processing, and reinforcement learning. We therefore argue that a foundation decision model (FDM) can be established by formulating various decision-making tasks as a sequence decoding task using the Transformer architecture; this would be a promising solution to advance the applications of IDM in more complex real world tasks. In this paper, we elaborate on how a foundation decision model improves the efficiency and generalization of IDM. We also discuss potential applications of a FDM in multi-agent game AI, production scheduling, and robotics tasks. Finally, through a case study, we demonstrate our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters, which achieves human-level performance over 453 tasks, including text generation, images caption, video games playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 would be a baby step towards more autonomous and efficient real world IDM applications.Comment: 26 pages, 4 figure

Ultralong nitrogen/sulfur Co‐doped carbon nano‐hollow‐sphere chains with encapsulated cobalt nanoparticles for highly efficient oxygen electrocatalysis

The development of simple and effective strategies to prepare electrocatalysts, which possess unique and stable structures comprised of metal/nonmetallic atoms for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), is currently an urgent issue. Herein, an efficient bifunctional electrocatalyst featured by ultralong N, S-doped carbon nano-hollow-sphere chains about 1300 nm with encapsulated Co nanoparticles (Co-CNHSCs) is developed. The multifunctional catalytic properties of Co together with the heteroatom-induced charge redistribution (i.e., modulating the electronic structure of the active site) result in superior catalytic activities toward OER and ORR in alkaline media. The optimized catalyst Co-CNHSC-3 displays an outstanding electrocatalytic ability for ORR and OER, a high specific capacity of 1023.6 mAh gZn−1, and excellent reversibility after 80 h at 10 mA cm−2 in a Zn-air battery system. This work presents a new strategy for the design and synthesis of efficient multifunctional carbon-based catalysts for energy storage and conversion devices

Doxofyllinium tetra­chloridoanti­monate(III) monohydrate

The title compound, (C11H14N4O4)[SbCl4]·H2O, comprises a protonated doxofyllinium cation [7-(1,3-dioxolan-2-ylmeth­yl)-1,3-dimethyl-2,6-dioxo-3,7-dihydro-1H-purin-9-ium], an [SbCl4]− anion and a water mol­ecule linked by N—H⋯O and O—H⋯Cl hydrogen bonds: the [SbCl4]− anions form centrosymmetric dimers via weak Sb⋯Cl inter­actions [Sb⋯Cl = 3.1159 (9) Å]. The geometrical arrangement in the crystal structure is characterized by slipped π–π stacking of the parallel purine ring systems, with an inter­planar separation of 3.32 Å

Microbial functional traits are sensitive indicators of mild disturbance by lamb grazing.

Mild disturbances are prevalent in the environment, which may not be easily notable but could have considerable ecological consequences over prolonged periods. To evaluate this, a field study was designed to examine the effects of very light-intensity lamb grazing on grassland soil microbiomes with different soil backgrounds. No significant change (P > 0.05) was observed in any vegetation and soil variables. Nonetheless, hundreds of microbial functional gene families, but not bacterial taxonomy, were significantly (P < 0.05) shifted. The relative abundances of both taxonomic markers and functional genes related to nitrifying bacteria were also changed. The observation highlighted herein, showing a high level of sensitivity with respect to functional traits (functionally categorized taxa or genes) in differentiating mild environmental disturbance, suggests that the key level at which to address microbial responses may not be "species" (by means of rRNA taxonomy), but rather at the functional gene level

An update to the taxonomy of Serica MacLeay, 1819 (sensu lato) from China (Coleoptera, Scarabaeidae, Sericinae, Sericini)

In this paper we update the knowledge on the species of Serica McLeay, 1819 (sensu lato) occurring in Yunnan, Sichuan, and Shaanxi provinces, China. Three new species are described: Serica allonanhua Liu, Ahrens, Li & Su, sp. nov., S. breviantennalis Liu, Ahrens, Li & Su, sp. nov., and S. fengensis Liu, Ahrens, Li & Su, sp. nov. The key to the species groups and species is updated. The habitus and male genitalia of the new species are illustrated, and a map showing their distribution is provided. New distributional data are given for four species