Monolithically integrated photonic crystal surface emitters on silicon with a vortex beam by using bound states in the continuum.

Optical resonant cavities with high quality factor (Q-factor) are widely used in science and technology for their capabilities of strong confinement of light and enhanced light-matter interaction. The 2D photonic crystal structure with bound states in the continuum (BICs) is a novel concept for resonators with ultra-compact device size, which can be used to generate surface emitting vortex beams based on symmetry-protected BICs at the Γ point. Here, to the best of our knowledge, we demonstrate the first photonic crystal surface emitter with a vortex beam by using BICs monolithically grown on CMOS-compatible silicon substrate. The fabricated quantum-dot BICs-based surface emitter operates at 1.3 µm under room temperature (RT) with a low continuous wave (CW) optically pumped condition. We also reveal the BIC's amplified spontaneous emission with the property of a polarization vortex beam, which is promising to provide a novel degree of freedom in classical and quantum realms.

Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features.

Accurate and robust camera pose estimation is essential for high-level applications such as augmented reality and autonomous driving. Despite the development of global feature-based camera pose regression methods and local feature-based matching guided pose estimation methods, challenging conditions, such as illumination changes and viewpoint changes, as well as inaccurate keypoint localization, continue to affect the performance of camera pose estimation. In this paper, we propose a novel relative camera pose regression framework that uses global features with rotation consistency and local features with rotation invariance. First, we apply a multi-level deformable network to detect and describe local features, which can learn appearances and gradient information sensitive to rotation variants. Second, we process the detection and description processes using the results from pixel correspondences of the input image pairs. Finally, we propose a novel loss that combines relative regression loss and absolute regression loss, incorporating global features with geometric constraints to optimize the pose estimation model. Our extensive experiments report satisfactory accuracy on the 7Scenes dataset with an average mean translation error of 0.18 m and a rotation error of 7.44° using image pairs as input. Ablation studies were also conducted to verify the effectiveness of the proposed method in the tasks of pose estimation and image matching using the 7Scenes and HPatches datasets.

Glycan sulfation patterns define autophagy flux at axon tip via PTPRσ-cortactin axis.

Chondroitin sulfate (CS) and heparan sulfate (HS) are glycosaminoglycans that both bind the receptor-type protein tyrosine phosphatase PTPRσ, affecting axonal regeneration. CS inhibits axonal growth, while HS promotes it. Here, we have prepared a library of HS octasaccharides and, together with synthetic CS oligomers, we found that PTPRσ preferentially interacts with CS-E-a rare sulfation pattern in natural CS-and most HS oligomers bearing sulfate and sulfamate groups. Consequently, short and long stretches of natural CS and HS, respectively, bind to PTPRσ. CS activates PTPRσ, which dephosphorylates cortactin-herein identified as a new PTPRσ substrate-and disrupts autophagy flux at the autophagosome-lysosome fusion step. Such disruption is required and sufficient for dystrophic endball formation and inhibition of axonal regeneration. Therefore, sulfation patterns determine the length of the glycosaminoglycan segment that bind to PTPRσ and define the fate of axonal regeneration through a mechanism involving PTPRσ, cortactin and autophagy.

Polarization-independent directional coupler and polarization beam splitter based on asymmetric cross-slot waveguides.

The polarization dependence of a directional coupler (DC) based on asymmetric cross-slot waveguides is investigated. Due to structural birefringence, the coupling behaviors of the quasi-TE and quasi-TM modes in the DC vary with the waveguide geometry. A polarization-independent directional coupler (PIDC) and polarization beam splitter (PBS) are proposed by tailoring the ratio of the coupling length for quasi-TE and quasi-TM modes. The simulated results show that the coupling lengths of the designed PIDC and PBS are 8 and 28.25 µm, respectively. Both the PIDC and PBS show an insertion loss (IL) <0.7 dB on a bandwidth over 100 nm. The extinction ratios are ∼20 dB for PIDC and ∼14 dB for PBS. The fabrication-error tolerance of the practical devices is also discussed. In this study, we employ a commercial software tool for finite difference eigenmode and three-dimensional finite difference time domain methods to perform the numerical simulations.

Objective comparison of particle tracking methods.

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities.

In the dynamic landscape of Artificial Intelligence (AI), two notable phenomena are becoming predominant: the exponential growth of large AI model sizes and the explosion of massive amount of data. Meanwhile, scientific research such as quantum computing and protein synthesis increasingly demand higher computing capacities. As the Moore's Law approaches its terminus, there is an urgent need for alternative computing paradigms that satisfy this growing computing demand and break through the barrier of the von Neumann model. Neuromorphic computing, inspired by the mechanism and functionality of human brains, uses physical artificial neurons to do computations and is drawing widespread attention. This review studies the expansion of optoelectronic devices on photonic integration platforms that has led to significant growth in photonic computing, where photonic integrated circuits (PICs) have enabled ultrafast artificial neural networks (ANN) with sub-nanosecond latencies, low heat dissipation, and high parallelism. In particular, various technologies and devices employed in neuromorphic photonic AI accelerators, spanning from traditional optics to PCSEL lasers are examined. Lastly, it is recognized that existing neuromorphic technologies encounter obstacles in meeting the peta-level computing speed and energy efficiency threshold, and potential approaches in new devices, fabrication, materials, and integration to drive innovation are also explored. As the current challenges and barriers in cost, scalability, footprint, and computing capacity are resolved one-by-one, photonic neuromorphic systems are bound to co-exist with, if not replace, conventional electronic computers and transform the landscape of AI and scientific computing in the foreseeable future.

Aircraft trajectory prediction and aviation safety in ADS-B failure conditions based on neural network.

With the rapid expansion of transportation demand, the number of global flights has rapidly increased, which also poses challenges to air traffic management (ATM). Considering that the radar system in ATM can no longer meet the requirements of flight safety, a very promising next-generation air traffic control technology-Automatic Dependent Surveillance Broadcast (ADS-B) technology has been introduced. However, in the event of on-board equipment failure and local area signal interference, the ADS-B's signal will disappear or be interrupted. This sudden situation can pose a danger to aviation safety. To solve this problem, this article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the trajectory of aircraft, which can improve aviation safety in busy airspace. Firstly, the problem of frequent dynamic modeling of different types of aircraft was solved by utilizing historical ADS-B data as the data source. Secondly, the data cleansing method is proposed for ADS-B raw data. Furthermore, considering that the spatial trajectory of the aircraft is a complex time series with continuity and interactivity, a bidirectional LSTM based aircraft trajectory prediction framework is proposed to further improve prediction accuracy. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft.

Identification of PTPRσ-interacting proteins by proximity-labelling assay.

Receptor protein tyrosine phosphatases (RPTPs) are type-I transmembrane proteins and involved in various biological and pathological processes. Their functions are supposed to be exerted through tyrosine dephosphorylation of their specific substrates. However, our comprehensive understanding of specific substrates or interacting proteins for RPTPs is poor. PTPRσ belongs to class 2a RPTP family, dephosphorylates cortactin, and leads to autophagy flux disruption and axonal regeneration inhibition in response to its ligand chondroitin sulphate. Here, we applied proximity-dependent biotin identification (BioID) assay, a proximity-labelling assay, to PTPRσ and reproducibly identified the 99 candidates as interactors for PTPRσ including already-known interactors such as Liprin-α and Trio. Of note, cortactin was also listed up in our assay. Our results suggest that the BioID assay is a powerful and reliable tool to identify RPTP-interacting proteins including its specific substrate.

Dermatan sulphate is an activating ligand of anaplastic lymphoma kinase.

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase (RTK) that harbours a tyrosine kinase domain in its intracellular region and is expressed in both central and peripheral nervous systems. RTKs are activated upon ligand binding and receptor clustering; however, ALK remains an orphan receptor despite its pathological significance, especially in malignancy. Recent biochemical work showed that heparan sulphate (HS), an unbranched sulphated glycan, acts as a ligand for and activates ALK. Here, we show that dermatan sulphate (DS, chondroitin sulphate B) directly interacts with the extracellular N-terminal region of ALK as well as HS. The tetrasaccharide of DS was required and was sufficient for inducing autophosphorylation of ALK at tyrosine 1604, a marker for activated ALK. Interestingly, longer oligosaccharides caused enhanced activation of ALK, as was the case for HS. Our results provide a novel example of glycans as signalling molecules and shed light on the pathophysiological roles of ALK.

First-principles study the single-layer transition metal trihalide CrXSe3 (X = Sn, Ge, Si) as monolayer ferromagnetic semiconductor.

Layered transition metal trihalides ABX3 are promising candidate materials for monolayer magnets. In this paper, we investigated single-layer CrXSe3 (X = Sn, Ge, Si) as monolayer ferromagnetic semiconductors (FMS). Firstly, our calculated interlayer binding energies and mechanical properties demonstrate the feasibility of obtaining the free-standing monolayer CrXSe3 from the layered van der Waals crystal CrXSe3 via mechanical exfoliating method. Plus, we find that the ferromagnetic (FM) super-exchange interaction dominates over the anti-ferromagnetic (AFM) direct-exchange interactions, making CrSnSe3 and CrGeSe3 monolayers FM with the magnetic moments of 6.0 µ B per unit cell. Particularly, the FM configurations of CrSnSe3 and CrGeSe3 monolayers become more stable under the increasing tensile strain, and CrSiSe3 converts to FM from AFM under biaxial tensile strain larger than 2%. Additionally, the three monolayers CrXSe3 are all semiconducting with energy band gaps of 0.76, 0.87 and 1.1 eV for X being Sn, Ge and Si, respectively. Our results suggest CrXSe3 as monolayer FMS hold promising potential in spintronics.

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA.

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time-consuming, imprecise, and subjective process, which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system, which directly predicts the H-Score automatically. Our system imitates the pathologists' decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumor and non-tumor), a second FCN to extract tumor nuclei region, and a multi-column convolutional neural network, which takes the outputs of the first two FCNs and the stain intensity description image as an input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as the input and directly outputs a clinical score. We will present experimental results, which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists' scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.

Curvature Filters Efficiently Reduce Certain Variational Energies.

In image processing, the rapid approximate solution of variational problems involving generic data-fitting terms is often of practical relevance, for example in real-time applications. Variational solvers based on diffusion schemes or the Euler-Lagrange equations are too slow and restricted in the types of data-fitting terms. Here, we present a filter-based approach to reduce variational energies that contain generic data-fitting terms, but are restricted to specific regularizations. Our approach is based on reducing the regularization part of the variational energy, while guaranteeing non-increasing total energy. This is applicable to regularization-dominated models, where the data-fitting energy initially increases, while the regularization energy initially decreases. We present fast discrete filters for regularizers based on Gaussian curvature, mean curvature, and total variation. These pixel-local filters can be used to rapidly reduce the energy of the full model. We prove the convergence of the resulting iterative scheme in a greedy sense, and we show several experiments to demonstrate applications in image-processing problems involving regularization-dominated variational models.