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Recent advances in semantic image segmentation have helped researchers understand an image by distinguishing different objects and understanding their relationships. Semantic image segmentation algorithms have been effectively used to identify the characteristics of complex types of tissue cells in histopathological slide images. Still, the standardization of colors has been one of the major challenges to proceeding with semantic image segmentation algorithms due to the color variation in the histopathological slide images. In this paper, we perform a two-way analysis of color normalization, evaluating four representative color normalization methods with six evaluation metrics on 19 tissue types and reducing dimensions for visualization. The experiment results show that Reinhard's color normalization outperforms other color normalization methods regarding the six evaluation metrics. Additionally, we compared the experiment results based on the color normalization methods and the tissue types using a dimensionality reduction technique. The additional experiment results demonstrate that the types of tissue images are not directly related to the color normalization results, but the dimensionality reduction technique is effective to split different color normalization methods. 

Advanced thermal management technologies represent an important research frontier because such materials and systems show promise for enhancing personal physiological comfort and reducing building energy consumption. These technologies typically offer the advantages of excellent portability, user-friendly tunability, energy efficiency, and straightforward manufacturability, but they frequently suffer from critical challenges associated with poor breathability, inadequate wash stability, and difficult fabric integration. Within this broader context, our laboratory has previously developed heat-managing composite materials by drawing inspiration from the color-changing skin of the common squid. Herein, we describe the design, fabrication, and testing of breathable, washable, and fabric-integrated variants of our composite materials, which demonstrate state-of-the-art adaptive infrared properties and dynamic thermoregulatory functionalities. The combined findings directly advance the performance and applications scope of our bioinspired thermoregulatory composites and ultimately may guide the incorporation of desirable multifunctionality into other wearable technologies. 

Deep learning algorithms have been successfully adopted to extract meaningful information from digital images, yet many of them have been untapped in the semantic image segmentation of histopathology images. In this paper, we propose a deep convolutional neural network model that strengthens Atrous separable convolutions with a high rate within spatial pyramid pooling for histopathology image segmentation. A well-known model called DeepLabV3Plus was used for the encoder and decoder process. ResNet50 was adopted for the encoder block of the model which provides us the advantage of attenuating the problem of the increased depth of the network by using skip connections. Three Atrous separable convolutions with higher rates were added to the existing Atrous separable convolutions. We conducted a performance evaluation on three tissue types: tumor, tumor-infiltrating lymphocytes, and stroma for comparing the proposed model with the eight state-of-the-art deep learning models: DeepLabV3, DeepLabV3Plus, LinkNet, MANet, PAN, PSPnet, UNet, and UNet++. The performance results show that the proposed model outperforms the eight models on mIOU (0.8058/0.7792) and FSCR (0.8525/0.8328) for both tumor and tumor-infiltrating lymphocytes. 

Digital pathology has played a key role in replacing glass slides with digital images, enhancing various pathology workflows. Whole slide images are digitized pathological images improving the capabilities of digital pathology and contributing to the overall turnaround time for diagnoses. The digitized images have been successfully integrated with artificial intelligence algorithms assisting pathologists in many tasks, but there are still demands to develop a new algorithm for a better diagnosis process. In this paper, we propose a new deep convolutional neural network model integrating a feature pyramid network with a self-attention mechanism in three pathways: encoder, decoder, and self-attention nested for providing accurate tumor region segmentation on whole slide images. The encoder pathway adopts ResNet50 architecture for the bottom-up network. The decoder pathway adopts the feature pyramid network for the top-down network. The self-attention nested pathway forms the attention map represented by the distribution of attention scores focusing on localizing tumor regions and avoiding irrelevant information. The results of our experiment show that the proposed model outperforms the state-of-the-art deep convolutional neural network models in terms of tumor and stromal region segmentation. Moreover, various encoder networks were equipped with the proposed model and compared with each other. The results indicate that the ResNet series using the proposed model outperforms other encoder networks. 

Switching of magnetization by spin–orbit torque in the (Ga,Mn)(As,P) film was studied with currents along ⟨100⟩ crystal directions and an in-plane magnetic field bias. This geometry allowed us to identify the presence of two independent spin–orbit-induced magnetic fields: the Rashba field and the Dresselhaus field. Specifically, we observe that when the in-plane bias field is along the current (I[Formula: see text]H bias ), switching is dominated by the Rashba field, while the Dresselhaus field dominates magnetization reversal when the bias field is perpendicular to the current (I ⊥ H bias ). In our experiments, the magnitudes of the Rashba and Dresselhaus fields were determined to be 2.0 and 7.5 Oe, respectively, at a current density of 8.0 × 10 5 A/cm 2 . 

Interlayer exchange coupling (IEC) has been intensively investigated in magnetic multilayers, owing to its potential for magnetic memory and logic device applications. Although IEC can be reliably obtained in metallic ferromagnetic multilayer systems by adjusting structural parameters, it is difficult to achieve gate control of IEC in metallic systems due to their large carrier densities. Here, we demonstrate that IEC can be reliably controlled in ferromagnetic semiconductor (FMS) trilayer structures by means of an external gate voltage. We show that, by designing a quantum-well-type trilayer structure based on (Ga,Mn)(As,P) FMSs and adapting the ionic liquid gating technique, the carrier density in the nonmagnetic spacer of the system can be modulated with gate voltages of only a few volts. Due to this capability, we are able to vary the strength of IEC by as much as 49% in the FMS trilayer. These results provide important insights into design of spintronic devices and their energy-efficient operation.