Progressively Hybrid Transformer for Multi-Modal Vehicle Re-Identification.

Multi-modal (i.e., visible, near-infrared, and thermal-infrared) vehicle re-identification has good potential to search vehicles of interest in low illumination. However, due to the fact that different modalities have varying imaging characteristics, a proper multi-modal complementary information fusion is crucial to multi-modal vehicle re-identification. For that, this paper proposes a progressively hybrid transformer (PHT). The PHT method consists of two aspects: random hybrid augmentation (RHA) and a feature hybrid mechanism (FHM). Regarding RHA, an image random cropper and a local region hybrider are designed. The image random cropper simultaneously crops multi-modal images of random positions, random numbers, random sizes, and random aspect ratios to generate local regions. The local region hybrider fuses the cropped regions to let regions of each modal bring local structural characteristics of all modalities, mitigating modal differences at the beginning of feature learning. Regarding the FHM, a modal-specific controller and a modal information embedding are designed to effectively fuse multi-modal information at the feature level. Experimental results show the proposed method wins the state-of-the-art method by a larger 2.7% mAP on RGBNT100 and a larger 6.6% mAP on RGBN300, demonstrating that the proposed method can learn multi-modal complementary information effectively.

Quantized Collision/Fusion Events of Anionic Ionosomes at a Polarized Soft Micro-Interface.

Single-entity collisional electrochemistry (SECE) can capture physicochemical information at the single entity level. In the present work, we systematically studied in-situ generation and detection of single anionic ionosomes via SECE combined with a miniaturized interface between two immiscible electrolyte solutions (ITIES). Ionosome is an ionic-bilayer encapsulated nanoscopic water cluster/droplet that carries a net charge. Discrete spiky ionic currents were observed upon collisions/fusions of individual F- or Cl- -ionosomes with a positively polarized micro-ITIES. This fusion process was proved to follow the bulk electrolysis model. With this method, some essential factors such as concentration and charge density of the hydrated anions, and the interfacial area, were revealed. It demonstrates that anionic ionosomes share a common theoretical framework with their counterparts (i. e., cationic ionosomes, like Li+ -ionosomes). This work will spur the advancements in a myriad of fields, including such as the colloid and interface science, micro- and/or nanoscale electrochemistry, and electrophysiology and brain sciences.

Faradaic Counter for Liposomes Loaded with Potassium, Sodium Ions, or Protonated Dopamine.

Collisional electrochemistry between single particles and a biomimetic polarized micro-liquid/liquid interface has emerged as a novel and powerful analytical method for measurements of single particles. Using this platform, rapid detection of liposomes at the single particle level is reported herein. Individual potassium, sodium, or protonated dopamine-encapsulated (pristine or protein-decorated) liposomes collide and fuse with the polarized micro-liquid/liquid interface accompanying the release of ions, which are recorded as spike-like current transients of stochastic nature. The sizing and concentration of the liposomes can be readily estimated by quantifying the amount of encapsulated ions in individual liposomes via integrating each current spike versus time and the spike frequency, respectively. We call this type of nanosensing technology "Faradaic counter". The estimated liposome size distribution by this method is in line with the dynamic light scattering (DLS) measurements, implying that the quantized current spikes are indeed caused by the collisions of individual liposomes. The reported electrochemical sensing technology may become a viable alternative to DLS and other commercial nanoparticle analysis systems, for example, nanoparticle tracking analysis.