Spatial Transcriptomics (ST) is a method that captures gene expression profiles aligned with spatial coordinates. The discrete spatial distribution and the super-high dimensional sequencing results make ST data challenging to be modeled effectively. In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA, empowered by the great approximation capability of Implicit Neural Representations (INRs) that can enhance both the spatial density and the gene expression. Concretely within the proposed SUICA, we incorporate a graph-augmented Autoencoder to effectively model the context information of the unstructured spots and provide informative embeddings that are structure-aware for spatial mapping. We also tackle the extremely skewed distribution in a regression-by-classification fashion and enforce classification-based loss functions for the optimization of SUICA. By extensive experiments of a wide range of common ST platforms under varying degradations, SUICA outperforms both conventional INR variants and SOTA methods regarding numerical fidelity, statistical correlation, and bio-conservation. The prediction by SUICA also showcases amplified gene signatures that enriches the bio-conservation of the raw data and benefits subsequent analysis.

A sub-nanoliter, light-tolerant (780 µW/mm2), and power-adaptive forward-bulk optoelectronic microsystem for tetherless neural recording is presented. The CMOS bulk is used as a power harvester to utilize photogenerated carriers and a lower transistor threshold voltage for low power design (~0.3 µA at ~0.32 V). Increasing optical power boosts the system bandwidth to 14 kHz and the sampling frequency to 18.2 kHz, while maintaining the integrated input-referred noise (~10 µVRMS) stable, with an NEF of 2.34.

Accurate partial discharge (PD) detection and insulation diagnosis are essential for ensuring the operational safety of high-voltage (HV) power equipment, while wideband high sensitivity PD detection is extremely imperative since most of the PD events reflecting potential insulation defects have low strength (<1 pC) with wideband features. However, conventional wideband detection methods such as high-frequency (HF) antennas often suffer from the trade-off between low sensitivity, increased noise and the bandwidth requirements due to their low-Q features in the wideband sensing, and the vulnerability to in-band narrowband interference, especially in complex industrial environments. To address these challenges, this paper proposes a novel high sensitivity super-wideband PD detection system for Industrial Internet of Things (IIoT) applications integrating a reconfigurable high-frequency antenna sensor (RHAS), a bandwidth-reconfigurable low-noise amplifier (BRLNA), and a sub-band synthesis algorithm with built-in narrowband interference rejection (NIR) function. The RHAS captures weak PD signals across 48 high-Q sub-bands, which are individually amplified and digitally synthesized to reconstruct the wideband signal with enhanced sensitivity. The proposed system enables remote, clamp-free PD detection with strong NIR capability near grounded conductors, significantly outperforming the traditional sensors such as High-Frequency Current Transformers (HFCTs). Experimental results demonstrate a detection bandwidth from 1.4 MHz to 98.2 MHz (194.38% relative bandwidth) and a minimum detectable PD level of 0.3 pC. Compared with HFCT based systems, the proposed method achieves a 0.4–4.2 dB improvement in PD signal-to-noise ratio (SNR) and a 6.1–8.4 dB increase in total system gain. These results validate the effectiveness and robustness of the proposed approach for high-sensitivity wideband PD monitoring.

Various quadrotor drones have been developed in recent years, mainly focusing on either improving maximum thrust per platform area or flight maneuverability. Evidently, achieving both advantages simultaneously is a challenging task, since they call for opposing rotor requirements. Specifically, improving the drone's maximum thrust per platform area mainly requires reducing the number of rotors to make way for larger and more powerful rotors. While this can be an effective method to increase overall thrust, improving flight maneuverability requires a greater number of rotors to generate larger rotating torques or to increase the thrust vectoring capability. To address this challenge, we design a novel coaxial drone with two contra-rotating rotors for high thrust efficiency while enabling independent dual-axis rotor rotation to maintain maneuverability along the roll and pitch axes. The thrust vectoring capability is provided by two dedicated servo motors connected vertically in series with the coaxial propellers to produce a compact and elongated fuselage frame. A nonlinear flight model in six degrees of freedom is developed for the underactuated system, incorporating four control inputs from the two propellers and servos respectively. Consequently, a nonlinear control allocation approach is proposed such that the drone can produce a desired control force and yaw torque to stabilize the drone's position and yaw angle. For the uncontrolled roll and pitch dynamics, a damping component is added such that the roll and pitch angular velocities can also be stabilized. Both numerical simulations and real experiments are conducted to validate the design of the drone and the effectiveness of the proposed control strategy.

In eukaryotes, polyadenylation (poly(A)) is an essential process during mRNA maturation. Identifying the cis-determinants of poly(A) signal (PAS) on the DNA sequence is the key to understand the mechanism of translation regulation and mRNA metabolism. Although machine learning methods were widely used in computationally identifying PAS, the need for tremendous amounts of annotation data hinder applications of existing methods in species without experimental data on PAS. Therefore, cross-species PAS identification, which enables the possibility to predict PAS from untrained species, naturally becomes a promising direction. In our works, we propose a novel deep learning method named Poly(A)-DG for cross-species PAS identification. Poly(A)-DG consists of a Convolution Neural Network-Multilayer Perceptron (CNN-MLP) network and a domain generalization technique. It learns PAS patterns from the training species and identifies PAS in target species without re-training. To test our method, we use four species and build cross-species training sets with two of them and evaluate the performance of the remaining ones. Moreover, we test our method against insufficient data and imbalanced data issues and demonstrate that Poly(A)-DG not only outperforms state-of-the-art methods but also maintains relatively high accuracy when it comes to a smaller or imbalanced training set.

In this paper, we address the problem of multi-class semantic segmentation of high resolution aerial imagery with a deep-convolutional-neural-network-based model named D-LinkNet, which was initially designed for the task of road extraction. D-LinkNet extends LinkNet, which is considered as an efficient method for semantic segmentation and adopts encoder-decoder architecture, by inserting dilated convolution layers between LinkNets encoder and decoder to enlarge the receptive field of kernel without reducing the resolution of the feature maps. Besides, we use the multi-class form of the original loss function combined by Dice loss and Binary Cross Entropy loss, which has proven to be effective in classification tasks with imbalanced class distribution that is prevalent for aerial imagery. Experimental results on public available Potsdam dataset demonstrate that D-LinkNet obtains an overall accuracy of 86.1%, which outperforms state-of-the-art methods. Moreover, D-LinkNet achieves a competitive speed in the experiment so it can produce relatively accurate predictions more efficiently.