PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery

Satellite imagery analysis plays a vital role in remote sensing, but the information loss caused by cloud cover seriously hinders its application. This study presents a high-performance cloud removal architecture called Progressive Multi-scale Attention Autoencoder (PMAA), which simultaneously leverages global and local information. It mainly consists of a cloud detection backbone and a cloud removal module. The cloud detection backbone uses cloud masks to reinforce cloudy areas to prompt the cloud removal module. The cloud removal module mainly comprises a novel Multi-scale Attention Module (MAM) and a Local Interaction Module (LIM). PMAA establishes the long-range dependency of multi-scale features using MAM and modulates the reconstruction of the fine-grained details using LIM, allowing for the simultaneous representation of fine- and coarse-grained features at the same level. With the help of diverse and multi-scale feature representation, PMAA outperforms the previous state-of-the-art model CTGAN consistently on the Sen2_MTC_Old and Sen2_MTC_New datasets. Furthermore, PMAA has a considerable efficiency advantage, with only 0.5% and 14.6% of the parameters and computational complexity of CTGAN, respectively. These extensive results highlight the potential of PMAA as a lightweight cloud removal network suitable for deployment on edge devices. We will release the code and trained models to facilitate the study in this direction.Comment: 8 pages, 5 figure

How Do Nonlinear Transformers Learn and Generalize in In-Context Learning?

Transformer-based large language models have displayed impressive in-context learning capabilities, where a pre-trained model can handle new tasks without fine-tuning by simply augmenting the query with some input-output examples from that task. Despite the empirical success, the mechanics of how to train a Transformer to achieve ICL and the corresponding ICL capacity is mostly elusive due to the technical challenges of analyzing the nonconvex training problems resulting from the nonlinear self-attention and nonlinear activation in Transformers. To the best of our knowledge, this paper provides the first theoretical analysis of the training dynamics of Transformers with nonlinear self-attention and nonlinear MLP, together with the ICL generalization capability of the resulting model. Focusing on a group of binary classification tasks, we train Transformers using data from a subset of these tasks and quantify the impact of various factors on the ICL generalization performance on the remaining unseen tasks with and without data distribution shifts. We also analyze how different components in the learned Transformers contribute to the ICL performance. Furthermore, we provide the first theoretical analysis of how model pruning affects ICL performance and prove that proper magnitude-based pruning can have a minimal impact on ICL while reducing inference costs. These theoretical findings are justified through numerical experiments.Comment: ICML 202

Optimal design and performance analysis of a hybrid system combing a floating wind platform and wave energy converters

Combined floating offshore wind platform and Wave Energy Converters (WECs) systems have the potential to provide a cost-effective solution to offshore power supply and platform protection. The objective of this paper is to optimize the size and layout of WECs within the hybrid system under a given sea state with a numerical study. The numerical model was developed based on potential flow theory with viscous correction in frequency domain to investigate the hydrodynamic performance of a hybrid system consisting of a floating platform and multiple heaving WECs. A non-dimensional method was presented to determine a series of variables, including radius, draft, and layout of the cylindrical WEC at a typical wave frequency as the initial design. WECs with larger diameter to draft ratio were found to experience relatively smaller viscous effects, and achieve more wave power, larger effective frequency range and similar wave power per unit weight compared with those with the smaller diameter to draft ratio in the same sea state. The addition of WECs reduced the maximum horizontal force and pitch moment on the platform, whereas the maximum vertical force increased due to the increasing power take-off force, especially at low frequencies. The results presented in this paper provide guidance for the optimized design of WECs and indicate the potential for synergies between wave and wind energy utilization on floating platforms

A BAC-Based Physical Map of Zhikong Scallop (Chlamys farreri Jones et Preston)

Zhikong scallop (Chlamys farreri) is one of the most economically important aquaculture species in China. Physical maps are crucial tools for genome sequencing, gene mapping and cloning, genetic improvement and selective breeding. In this study, we have developed a genome-wide, BAC-based physical map for the species. A total of 81,408 clones from two BAC libraries of the scallop were fingerprinted using an ABI 3130xl Genetic Analyzer and a fingerprinting kit developed in our laboratory. After data processing, 63,641 (∼5.8× genome coverage) fingerprints were validated and used in the physical map assembly. A total of 3,696 contigs were assembled for the physical map. Each contig contained an average of 10.0 clones, with an average physical size of 490 kb. The combined total physical size of all contigs was 1.81 Gb, equivalent to approximately 1.5 fold of the scallop haploid genome. A total of 10,587 BAC end sequences (BESs) and 167 markers were integrated into the physical map. We evaluated the physical map by overgo hybridization, BAC-FISH (fluorescence in situ hybridization), contig BAC pool screening and source BAC library screening. The results have provided evidence of the high reliability of the contig physical map. This is the first physical map in mollusc; therefore, it provides an important platform for advanced research of genomics and genetics, and mapping of genes and QTL of economical importance, thus facilitating the genetic improvement and selective breeding of the scallop and other marine molluscs

Synthesis, photophysical properties and two-photon absorption study of tetraazachrysene-based N-heteroacenes

Three novel N‐heteroacene molecules (SDNU‐1, SDNU‐2 and SDNU‐3) based on tetraazachrysene units as cores have been designed, synthesized and fully characterized. Their photophysical, electrochemical and fluorescence properties were investigated, and they exhibited blue to green emission in the solid state. Interestingly, SDNU‐2 exhibited high solid photoluminescence quantum efficiencies (75.3 %), which is the highest value of N‐heteroacenes derivatives to date. Two‐photon absorption studies have been conducted by using the open and close aperture Z‐san technique. SDNU‐3 showed a significant enhancement in the two‐photon absorption cross‐section with magnitudes as high as about 700 GM (1 GM=1×10−50 cm4 s/photon) when excited with 800 nm light, which is the largest value based on a heteroacene system measured by using a Z‐scan experiment so far. We attribute the outcome to sufficient electronic coupling between the strong charge transfer of quadrupolar substituents and the tetraazachrysene core. Our result would provide a new guideline to design novel efficient two‐photon materials based on N‐heteroacene cores

Label-free detection of breast cancer cells using a functionalized tilted fiber grating.

peer reviewedThe detection of circulating tumor cells (CTCs) still faces a huge challenge partially because of low abundance of CTCs (1-10 cells/mL). In this work, a plasmonic titled fiber Bragg grating biosensor is proposed for detection of breast cancer cells. The biosensor is made by an 18° TFBG with a 50 nm-thick gold nanofilm coating over the surface of the fiber, further immobilized with a specific antibody against GPR30, which is a membrane receptor expressed in many breast cancers, serving as bait. In vitro tests have confirmed that the proposed biosensor can detect breast cancer cells in concentration of 5 cells/mL within 20 minutes and has good linearity in the range of 5-1000 cells/mL, which has met the requirement of CTC detection in real conditions. Furthermore, theoretical analysis based on the experimental results shows that the limit of detection can even reach single-cell level. Our proposed biosensor has a simple structure, is easy to manufacture, is of small size, and has a good performance, making it a good choice for real-time, label-free, and milliliter-volume detection of cancer cells in future

Atomistic Control in Molecular Beam Epitaxy Growth of Intrinsic Magnetic Topological Insulator MnBi2Te4

Intrinsic magnetic topological insulators have emerged as a promising platform to study the interplay between topological surface states and ferromagnetism. This unique interplay can give rise to a variety of exotic quantum phenomena, including the quantum anomalous Hall effect and axion insulating states. Here, utilizing molecular beam epitaxy (MBE), we present a comprehensive study of the growth of high-quality MnBi2Te4 thin films on Si (111), epitaxial graphene, and highly ordered pyrolytic graphite substrates. By combining a suite of in-situ characterization techniques, we obtain critical insights into the atomic-level control of MnBi2Te4 epitaxial growth. First, we extract the free energy landscape for the epitaxial relationship as a function of the in-plane angular distribution. Then, by employing an optimized layer-by-layer growth, we determine the chemical potential and Dirac point of the thin film at different thicknesses. Overall, these results establish a foundation for understanding the growth dynamics of MnBi2Te4 and pave the way for the future applications of MBE in emerging topological quantum materials.Comment: 20 pages, 4 figure

Singapore's efforts to achieve measles elimination in 2018

The World Health Organization verified that Singapore had eliminated endemic transmission of measles in October 2018. This report summarizes the evidence presented to the Regional Verification Commission for Measles and Rubella Elimination, comprising information about immunization schedules; laboratory testing protocols and the surveillance system; and data on immunization coverage and the epidemiology of cases. Between 2015 and 2017, a total of 246 laboratory confirmed cases of measles were reported. The source or country of infection was unknown for most cases (195; 79.3%). There were 22 clusters, ranging from two to five cases. The most common genotypes detected were D8 and D9. Transmission of B3 was interrupted in 2017, and H1 cases were sporadic and imported. Phylogenetic analyses of the D8 isolates showed the existence of 13 lineages or clusters. Although a few lineages were circulating concurrently, no lineage propagated continuously for a prolonged period, and transmission of each lineage eventually stopped. Although cases and clusters were reported yearly, molecular data showed that none of the lineages resulted in prolonged transmission. There were fewer measles cases in 2017 compared with 2016. The higher number of clusters was likely due to the overall increase in cases because cluster sizes remained small. The occurrence of small clusters is not unexpected since measles is highly infectious. The majority of imported cases did not result in secondary transmission. With the global increase in the number of measles cases, Singapore needs to stay vigilant and continue to promptly test suspected cases; vaccination is the key to preventing infection

Coordinated Plasticity between Barrel Cortical Glutamatergic and GABAergic Neurons during Associative Memory

Neural plasticity is associated with memory formation. The coordinated refinement and interaction between cortical glutamatergic and GABAergic neurons remain elusive in associative memory, which we examine in a mouse model of associative learning. In the mice that show odorant-induced whisker motion after pairing whisker and odor stimulations, the barrel cortical glutamatergic and GABAergic neurons are recruited to encode the newly learnt odor signal alongside the innate whisker signal. These glutamatergic neurons are functionally upregulated, and GABAergic neurons are refined in a homeostatic manner. The mutual innervations between these glutamatergic and GABAergic neurons are upregulated. The analyses by high throughput sequencing show that certain microRNAs related to regulating synapses and neurons are involved in this cross-modal reflex. Thus, the coactivation of the sensory cortices through epigenetic processes recruits their glutamatergic and GABAergic neurons to be the associative memory cells as well as drive their coordinated refinements toward the optimal state for the storage of the associated signals

Characterization of the Modes of Binding between Human Sweet Taste Receptor and Low-Molecular-Weight Sweet Compounds

One of the most distinctive features of human sweet taste perception is its broad tuning to chemically diverse compounds ranging from low-molecular-weight sweeteners to sweet-tasting proteins. Many reports suggest that the human sweet taste receptor (hT1R2–hT1R3), a heteromeric complex composed of T1R2 and T1R3 subunits belonging to the class C G protein–coupled receptor family, has multiple binding sites for these sweeteners. However, it remains unclear how the same receptor recognizes such diverse structures. Here we aim to characterize the modes of binding between hT1R2–hT1R3 and low-molecular-weight sweet compounds by functional analysis of a series of site-directed mutants and by molecular modeling–based docking simulation at the binding pocket formed on the large extracellular amino-terminal domain (ATD) of hT1R2. We successfully determined the amino acid residues responsible for binding to sweeteners in the cleft of hT1R2 ATD. Our results suggest that individual ligands have sets of specific residues for binding in correspondence with the chemical structures and other residues responsible for interacting with multiple ligands