Optical ReLU-like Activation Function Based on a Semiconductor Laser with Optical Injection

Artificial neural networks usually consist of successive linear multiply-accumulate operations and nonlinear activation functions. However, most optical neural networks only achieve the linear operation in the optical domain, while the optical implementation of activation function remains challenging. Here we present an optical ReLU-like activation function based on a semiconductor laser subject to the optical injection in experiment. The ReLU-like function is achieved in a broad regime above the Hopf bifurcation of the injection-locking diagram. In particular, the slope of the activation function is reconfigurable by tuning the frequency difference between the master laser and the slave laser

Diagnosis after Zooming in: A Multi-label Classification Model by Imitating Doctor Reading Habits to Diagnose Brain Diseases

International audiencePurpose: Computed tomography (CT) has the advantages of being low cost and noninvasive and is a primary diagnostic method for brain diseases. However, it is a challenge for junior radiologists to diagnose CT images accurately and comprehensively. It is necessary to build a system that can help doctors diagnose and provide an explanation of the predictions. Despite the success of deep learning algorithms in the field of medical image analysis, the task of brain disease classification still faces challenges: Researchers lack attention to complex manual labeling requirements and the incompleteness of prediction explanations. More importantly, most studies only measure the performance of the algorithm, but do not measure the effectiveness of the algorithm in the actual diagnosis of doctors. Methods: In this paper, we propose a model called DrCT2 that can detect brain diseases without using image-level labels and provide a more comprehensive explanation at both the slice and sequence levels. This model achieves reliable performance by imitating human expert reading habits: targeted scaling of primary images from the full slice scans and observation of suspicious lesions for diagnosis. We evaluated our model on two open-access data sets: CQ500 and the RSNA Intracranial Hemorrhage Detection Challenge. In addition, we defined three tasks to comprehensively evaluate model interpretability by measuring whether the algorithm can select key images with lesions. To verify the algorithm from the perspective of practical application, three junior radiologists were invited to participate in the experiments, comparing the effects before and after human-computer cooperation in different aspects. Results: The method achieved F1-scores of 0.9370 on CQ500 and 0.8700 on the RSNA data set. The results show that our model has good interpretability under the premise of good performance. Human radiologist evaluation experiments have proven that our model can effectively improve the accuracy of the diagnosis and improve efficiency. Conclusions: We proposed a model that can simultaneously detect multiple brain diseases.The report generated by the model can assist doctors in avoiding missed diagnoses, and it has good clinical application value

Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction

We introduce Omni-LOS, a neural computational imaging method for conducting holistic shape reconstruction (HSR) of complex objects utilizing a Single-Photon Avalanche Diode (SPAD)-based time-of-flight sensor. As illustrated in Fig. 1, our method enables new capabilities to reconstruct near-$360^\circ$ surrounding geometry of an object from a single scan spot. In such a scenario, traditional line-of-sight (LOS) imaging methods only see the front part of the object and typically fail to recover the occluded back regions. Inspired by recent advances of non-line-of-sight (NLOS) imaging techniques which have demonstrated great power to reconstruct occluded objects, Omni-LOS marries LOS and NLOS together, leveraging their complementary advantages to jointly recover the holistic shape of the object from a single scan position. The core of our method is to put the object nearby diffuse walls and augment the LOS scan in the front view with the NLOS scans from the surrounding walls, which serve as virtual ``mirrors'' to trap lights toward the object. Instead of separately recovering the LOS and NLOS signals, we adopt an implicit neural network to represent the object, analogous to NeRF and NeTF. While transients are measured along straight rays in LOS but over the spherical wavefronts in NLOS, we derive differentiable ray propagation models to simultaneously model both types of transient measurements so that the NLOS reconstruction also takes into account the direct LOS measurements and vice versa. We further develop a proof-of-concept Omni-LOS hardware prototype for real-world validation. Comprehensive experiments on various wall settings demonstrate that Omni-LOS successfully resolves shape ambiguities caused by occlusions, achieves high-fidelity 3D scan quality, and manages to recover objects of various scales and complexity

Application and Development Progress of Cr-Based Surface Coatings in Nuclear Fuel Element: I. Selection, Preparation, and Characteristics of Coating Materials

To cope with the shortcomings of nuclear fuel design exposed during the Fukushima Nuclear Accident, researchers around the world have been directing their studies towards accident-tolerant fuel (ATF), which can improve the safety of fuel elements. Among the several ATF cladding concepts, surface coatings comprise the most promising strategy to be specifically applied in engineering applications in a short period. This review presents a comprehensive introduction to the latest progress in the development of Cr-based surface coatings based on zirconium alloys. Part I of the review is a retrospective look at the application status of zirconium alloy cladding, as well as the development of ATF cladding. Following this, the review focuses on the selection process of ATF coating materials, along with the advantages and disadvantages of the current mainstream preparation methods of Cr-based coatings worldwide. Finally, the characteristics of the coatings obtained through each method are summarized according to some conventional performance evaluations or investigations of the claddings. Overall, this review can help assist readers in getting a thorough understanding of the selection principle of ATF coating materials and their preparation processes

Evaluation of Customs Supervision Competitiveness Using Principal Component Analysis

In order to improve the degree of security and facilitation of the business environment; customs administrations are constantly working to strengthen their own institutional innovation and governance in customs control. As such, this paper establishes an evaluation index of international customs supervision competitiveness based on the eight indexes extracted from the World Customs Organisation (WCO) Revised Kyoto Convention and selects 21 representative national customs using the principal component analysis (PCA) method to assess their competitiveness against SPSSAU quantitatively. Based on the data from the World Economic Forum, World Bank, OECD, WCO Annual Report, and Transparency International, the Dutch customs have relatively the best performance in the range of comprehensive competitiveness, and customs authorities in Germany, New Zealand, the United Kingdom, the United States, Mexico, Australia, the Netherlands, and Singapore also have relatively-best performance under different indexes. Taking China Customs as an example, the gaps between China Customs and the ones with the best performance are also analyzed. In response to the problems identified by the analysis, recommendations are made in the areas of process facilitation, technology application, international cooperation, economic development, taxation management, and capacity building to improve the competitiveness of customs control

Facile environment-friendly peptide-based humidity sensor for multifunctional applications

Humidity monitoring plays a key role in the human-machine interface (HMI) and health-related devices. Current inorganic sensing materials provide a stable and fast method to monitor relative humidity (RH). However, these materials require complicated preparation processes and even toxic reagents. Organic humidity-sensing materials were considered to be biocompatible, but they face slow response time and cannot be used in real-time systems. There is still a great challenge to seek biocompatible humidity-sensing materials with high performance. Micro-nano structures formed by self-assembled peptides have been proven to have good optical, mechanical, semiconductive properties and intrinsic biocompatibility, which can be used for monitoring human health and activities. Herein, we reported an environmentally friendly humidity sensor based on peptide self-assembled micro-nano fibers. This humidity sensor was fabricated by a drop-coating method, in which the peptide fiber served as the humidity-sensing material and the Au electrode was used as a substrate. Self-assembled fiber networks exhibit excellent absorption of water molecules, which result in an extraordinary humidity sensitivity of more than 30,000 as well as ultrafast response (66 ms). To demonstrate the multifunctional possibilities, peptide humidity sensors were applied to respiration monitoring, non-contact switch, and baby diaper wetting monitoring. Our results provide useful strategies for detecting humidity changes in physiological activities based on peptide self-assembled fibers. These results indicated that peptide could be a promising candidate for human-machine interaction and healthcare humidity monitoring

Me-NDT: Neural-backed Decision Tree for visual Explainability of deep Medical models

International audienceDespite the progress of deep learning on medical imaging, there is still not a true understanding of what networks learn and of how decisions are reached. Here, we address this by proposing a Visualized Neural-backed Decision Tree for Medical image analysis, Me-NDT. It is a CNN with a tree-based structure template that allows for both classification and visualization of firing neurons, thus offering interpretability. We also introduce node and path losses that allow Me-NDT to consider the entire path instead of isolated nodes. Our experiments on brain CT and chest radiographs outperform all baselines. Overall, Me-NDT is a lighter, comprehensively explanatory model, of great value for clinical practice

Recent Advances in Applications of Carbon Nanotubes for Desalination: A Review

As a sustainable, cost-effective and energy-efficient method, membranes are becoming a progressively vital technique to solve the problem of the scarcity of freshwater resources. With these critical advantages, carbon nanotubes (CNTs) have great potential for membrane desalination given their high aspect ratio, large surface area, high mechanical strength and chemical robustness. In recent years, the CNT membrane field has progressed enormously with applications in water desalination. The latest theoretical and experimental developments on the desalination of CNT membranes, including vertically aligned CNT (VACNT) membranes, composited CNT membranes, and their applications are timely and comprehensively reviewed in this manuscript. The mechanisms and effects of CNT membranes used in water desalination where they offer the advantages are also examined. Finally, a summary and outlook are further put forward on the scientific opportunities and major technological challenges in this field

Mechanical And Thermal Conductive Properties Of Fiber-Reinforced Silica-Alumina Aerogels

We report the formation of Al2O3-SiO2 fiber-reinforced Al2O3-SiO2 aerogels with the content of fibers in the range from 40 wt% to 55 wt% by sol-gel reaction, followed by supercritical drying. The structure and physical properties of fiber-reinforced Al2O3-SiO2 aerogels are studied. We find that the fiber-reinforced Al2O3-SiO2 aerogels can be resistant to the temperature of 1200°C. The integration of fibers significantly improves the mechanical properties of Al2O3-SiO2 aerogels. We find that the bending strength of fiber-reinforced Al2O3-SiO2 aerogels increases 0.431 MPa to 0.755 MPa and the elastic modulus increases from 0.679 MPa to 1.153 MPa, when the content of fibers increases from 40 wt% to 50 wt%. The thermal conductivity of the fiber-reinforced Al2O3-SiO2 aerogels is in the range from 0.0403 W/mK to 0.0545 W/mK, depending on the content of fibers