Multiplexed structured illumination super-resolution imaging with lifetime-engineered upconversion nanoparticles.

The emerging optical multiplexing within nanoscale shows super-capacity in encoding information by using lifetime fingerprints from luminescent nanoparticles. However, the optical diffraction limit compromises the decoding accuracy and throughput of the nanoparticles during conventional widefield imaging. This, in turn, challenges the quality of nanoparticles to afford the modulated excitation condition and further retain the multiplexed optical fingerprints for super-resolution multiplexing.

Learning Data Streams With Changing Distributions and Temporal Dependency.

In a data stream, concept drift refers to unpredictable distribution changes over time, which violates the identical-distribution assumption required by conventional machine learning methods. Current concept drift adaptation techniques mostly focus on a data stream with changing distributions. However, since each variable of a data stream is a time series, these variables normally have temporal dependency problems in the real world.

Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning.

Highly controlled synthesis of upconversion nanoparticles (UCNPs) can be achieved in the heterogeneous design, so that a library of optical properties can be arbitrarily produced by depositing multiple lanthanide ions. Such a control offers the potential in creating nanoscale barcodes carrying high-capacity information. With the increasing creation of optical information, it poses more challenges in decoding them in an accurate, high-throughput, and speedy fashion.

Preselectable Optical Fingerprints of Heterogeneous Upconversion Nanoparticles.

The control in optical uniformity of single nanoparticles and tuning their diversity in multiple dimensions, dot to dot, holds the key to unlocking nanoscale applications. Here we report that the entire lifetime profile of the single upconversion nanoparticle (τ profile) can be resolved by confocal, wide-field, and super-resolution microscopy techniques. The advances in both spatial and temporal resolutions push the limit of optical multiplexing from microscale to nanoscale.

A Segment-Based Drift Adaptation Method for Data Streams.

In concept drift adaptation, we aim to design a blind or an informed strategy to update our best predictor for future data at each time point. However, existing informed drift adaptation methods need to wait for an entire batch of data to detect drift and then update the predictor (if drift is detected), which causes adaptation delay. To overcome the adaptation delay, we propose a sequentially updated statistic, called drift-gradient to quantify the increase of distributional discrepancy when every new instance arrives.

A Drift Region-Based Data Sample Filtering Method.

Concept drift refers to changes in the underlying data distribution of data streams over time. A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it is necessary to understand where the drift occurs to support the drift adaptation strategy and effectively update the outdated models.

Development of a hybrid model to predict construction and demolition waste: China as a case study.

Construction and demolition waste (C&DW) is currently a worldwide issue, and the situation is the worst in China due to a rapid increase in the construction industry and the short life span of China's buildings. To create an opportunity out of this problem, comprehensive prevention measures and effective management strategies are urgently needed. One major gap in the literature of waste management is a lack of estimations on future C&DW generation.