Golden ultrafast melting: Understanding fast melting of metals is important for applications such as welding and micromachining. However, fast melting leaves simulation as the only option for probing the process. Mo et al. performed ultrafast electron diffraction experiments on laser-pulsed gold films. This allowed detailed mapping of the melting process, which proceeds through two distinct regimes while the bonding behavior changes in unexpected ways. The results require adding new physical processes to high-energy melting models. Science , this issue p. 1451

Skin lesion segmentation is a primary step for skin lesion analysis, which can benefit the subsequent classification task. It is a challenging task since the boundaries of pigment regions may be fuzzy and the entire lesion may share a similar color. Prevalent deep learning methods for skin lesion segmentation make predictions by ensembling different convolutional neural networks (CNN), aggregating multi-scale information, or by multi-task learning framework. The main purpose of doing so is trying to make use of as much information as possible so as to make robust predictions. A multi-task learning framework has been proved to be beneficial for the skin lesion segmentation task, which is usually incorporated with the skin lesion classification task. However, multi-task learning requires extra labeling information which may not be available for the skin lesion images. In this paper, a novel CNN architecture using auxiliary information is proposed. Edge prediction, as an auxiliary task, is performed simultaneously with the segmentation task. A cross-connection layer module is proposed, where the intermediate feature maps of each task are fed into the subblocks of the other task which can implicitly guide the neural network to focus on the boundary region of the segmentation task. In addition, a multi-scale feature aggregation module is proposed, which makes use of features of different scales and enhances the performance of the proposed method. Experimental results show that the proposed method obtains a better performance compared with the state-of-the-art methods with a Jaccard Index (JA) of $79.46$, Accuracy (ACC) of $94.32$, SEN of $88.76$ with only one integrated model, which can be learned in an end-to-end manner.

The ignition concept for electron fast ignition inertial confinement fusion requires sufficient energy be transferred from an $\sim 20\text{ps}$ laser pulse to the compressed fuel via approximately MeV electrons. We have assembled a suite of diagnostics to characterize such transfer, simultaneously fielding absolutely calibrated extreme ultraviolet multilayer imagers at 68 and 256 eV; spherically bent crystal imagers at 4.5 and 8 keV; multi-keV crystal spectrometers; MeV x-ray bremmstrahlung, electron and proton spectrometers (along the same line of sight), and a picosecond optical probe interferometer. These diagnostics allow careful measurement of energy transport and deposition during and following the laser-plasma interactions at extremely high intensities in both planar and conical targets. Together with accurate on-shot laser focal spot and prepulse characterization, these measurements are yielding new insights into energy coupling and are providing critical data for validating numerical particle-in-cell (PIC) and hybrid PIC simulation codes in an area crucial for fast ignition and other applications. Novel aspects of these diagnostics and how they are combined to extract quantitative data on ultrahigh intensity laser-plasma interactions are discussed.

Light scattering has been used for label-free cell detection. The angular light scattering patterns from the cells are unique to them based on the cell size, nucleus size, number of mitochondria, and cell surface roughness. The patterns collected from the cells can then be classified based on different image characteristics. We have also developed a machine learning (ML) method to classify these cell light scattering patterns. As a case study we have used this light scattering technique integrated with the machine learning to analyze staurosporine-treated SH-SY5Y neuroblastoma cells and compare them to non-treated control cells. Experimental results show that the ML technique can provide a classification accuracy (treated versus non-treated) of over 90%. The predicted percentage of the treated cells in a mixed solution is within 5% of the reference (ground-truth) value and the technique has the potential to be a viable method for real-time detection and diagnosis.

An experimental setup capable of measuring simultaneous 2D scattered light angular distribution from two directions to study cell morphology without the use of bio-labels was developed. Experiments with hematopoietic stem cells (CD34⁺ cells) show good agreement with detailed numerical simulations of light scattering. Numerical simulations and computer models of cells are used to identify physical features of cells with the largest scattering cross sections. This allows for determination of size, geometry of the nucleus and distribution of mitochondria in hematopoietic stem cells by means of our label-free method.

Energy harvesting is the process of capturing the local ambient energy and converting it into useful energy. Piezoelectric energy harvesters (PEHs) are one of the solutions to convert vibration energy to electrical energy. An electrical interface is needed to bridge between the piezoelectric energy harvester and the energy storage element. A high-efficient AC/DC converter is designed for a low vibration power harvester with the milli- or micro-watt range power. This paper reports a novel self-powered high-efficiency interface to rectify the AC voltage generated by a vibration energy harvester to DC voltage. The reported interface consists of a triggering circuit and a Negative Voltage Converter (NVC) combined with a Parallel Synchronized Switch Harvesting on Inductor (PSSHI) forming the NVC-PSSHI. The analytical model and simulation of the designed NVC-PSSHI interface were derived and performed, respectively. The targeted input voltage, frequency and DC loading conditions were 3 Vpp to 7 Vpp, 100 Hz to 500 Hz and 5 kΩ to 30 kΩ, respectively. Experiments with a PEH were also performed to validate the analytical and simulation results. The maximum efficiency of the designed NVC-PSSHI interface reached 82.1% from the PEH experiment. The NVC-PSSHI interface efficiency was higher than the traditional PSSHI interface by up to 23.4%.