Ultrafast Opto-Electronic and Thermal Tuning of Third-Harmonic Generation in a Graphene Field Effect Transistor.

Graphene is a unique platform for tunable opto-electronic applications thanks to its linear band dispersion, which allows electrical control of resonant light-matter interactions. Tuning the nonlinear optical response of graphene is possible both electrically and in an all-optical fashion, but each approach involves a trade-off between speed and modulation depth. Here, lattice temperature, electron doping, and all-optical tuning of third-harmonic generation are combined in a hexagonal boron nitride-encapsulated graphene opto-electronic device and demonstrate up to 85% modulation depth along with gate-tunable ultrafast dynamics. These results arise from the dynamic changes in the transient electronic temperature combined with Pauli blocking induced by the out-of-equilibrium chemical potential. The work provides a detailed description of the transient nonlinear optical and electronic response of graphene, which is crucial for the design of nanoscale and ultrafast optical modulators, detectors, and frequency converters.

Pump-Driven Opto-Magnetic Properties in Semiconducting Transition-Metal Dichalcogenides: An Analytical Model.

Single-layer transition-metal dichalcogenides provide an unique intrinsic entanglement between the spin/valley/orbital degrees of freedom and the polarization of scattered photons. This scenario gives rise to the well-assessed optical dichroism observed by using both steady and time-resolved probes. In this paper, we provide compact analytical modeling of the onset of a finite Faraday/Kerr optical rotation upon shining with circularly polarized light. We identify different optical features displaying optical rotation at different characteristic energies, and we describe in an analytical framework the time-dependence of their intensities as a consequence of the main spin-conserving and spin-flip processes.

A convolutional neural network-based system for fully automatic segmentation of whole-body [68Ga]Ga-PSMA PET images in prostate cancer.

PURPOSE: The aim of this study was development and evaluation of a fully automated tool for the detection and segmentation of mPCa lesions in whole-body [68Ga]Ga-PSMA-11 PET scans by using a nnU-Net framework. METHODS: In this multicenter study, a cohort of 412 patients from three different center with all indication of PCa who underwent [68Ga]Ga-PSMA-11 PET/CT were enrolled. Two hundred cases of center 1 dataset were used for training the model. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework. A subset of center 1 dataset and cases of center 2 and center 3 were used for testing of model. The performance of the segmentation pipeline that was developed was evaluated by comparing the fully automatic segmentation mask with the manual segmentation of the corresponding internal and external test sets in three levels including patient-level scan classification, lesion-level detection, and voxel-level segmentation. In addition, for comparison of PET-derived quantitative biomarkers between automated and manual segmentation, whole-body PSMA tumor volume (PSMA-TV) and total lesions PSMA uptake (TL-PSMA) were calculated. RESULTS: In terms of patient-level classification, the model achieved an accuracy of 83%, sensitivity of 92%, PPV of 77%, and NPV of 91% for the internal testing set. For lesion-level detection, the model achieved an accuracy of 87-94%, sensitivity of 88-95%, PPV of 98-100%, and F1-score of 93-97% for all testing sets. For voxel-level segmentation, the automated method achieved average values of 65-70% for DSC, 72-79% for PPV, 53-58% for IoU, and 62-73% for sensitivity in all testing sets. In the evaluation of volumetric parameters, there was a strong correlation between the manual and automated measurements of PSMA-TV and TL-PSMA for all centers. CONCLUSIONS: The deep learning networks presented here offer promising solutions for automatically segmenting malignant lesions in prostate cancer patients using [68Ga]Ga-PSMA PET. These networks achieve a high level of accuracy in whole-body segmentation, as measured by the DSC and PPV at the voxel level. The resulting segmentations can be used for extraction of PET-derived quantitative biomarkers and utilized for treatment response assessment and radiomic studies.

Highlights from Faraday Discussion: From optical to THz control of materials, London, UK, May 2022.

The Faraday Discussion 'From optical to THz control of materials' was held in London, UK, and online on the 23rd, 24th, and 25th May 2022. The meeting brought together established and early-career scientists, postgraduate students, scientific editors, and industrial researchers in the field of spectroscopy and materials science from over ten different countries interested in exploring the physical properties of materials at ultrafast timescales driven by optical and THz excitations. This conference report provides highlights of this meeting and we give summaries of the presentations including the introductory lecture, all the papers discussed, and the concluding remarks.

Probing Electronic States in Monolayer Semiconductors through Static and Transient Third-Harmonic Spectroscopies.

Electronic states and their dynamics are of critical importance for electronic and optoelectronic applications. Here, various relevant electronic states in monolayer MoS2 , such as multiple excitonic Rydberg states and free-particle energy bands are probed with a high relative contrast of up to ≥200 via broadband (from ≈1.79 to 3.10 eV) static third-harmonic spectroscopy (THS), which is further supported by theoretical calculations. Moreover, transient THS is introduced to demonstrate that third-harmonic generation can be all-optically modulated with a modulation depth exceeding ≈94% at ≈2.18 eV, providing direct evidence of dominant carrier relaxation processes associated with carrier-exciton and carrier-phonon interactions. The results indicate that static and transient THS are not only promising techniques for the characterization of monolayer semiconductors and their heterostructures, but also a potential platform for disruptive photonic and optoelectronic applications, including all-optical modulation and imaging.

Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images.

BACKGROUND AND OBJECTIVE: In most patients presenting with respiratory symptoms, the findings of chest radiography play a key role in the diagnosis, management, and follow-up of the disease. Consolidation is a common term in radiology, which indicates focally increased lung density. When the alveolar structures become filled with pus, fluid, blood cells or protein subsequent to a pulmonary pathological process, it may result in different types of lung opacity in chest radiograph. This study aims at detecting consolidations in chest x-ray radiographs, with a certain precision, using artificial intelligence and especially Deep Convolutional Neural Networks to assist radiologist for better diagnosis. METHODS: Medical image datasets usually are relatively small to be used for training a Deep Convolutional Neural Network (DCNN), so transfer learning technique with well-known DCNNs pre-trained with ImageNet dataset are used to improve the accuracy of the models. ImageNet feature space is different from medical images and in the other side, the well-known DCNNs are designed to achieve the best performance on ImageNet. Therefore, they cannot show their best performance on medical images. To overcome this problem, we designed a problem-based architecture which preserves the information of images for detecting consolidation in Pediatric Chest X-ray dataset. We proposed a three-step pre-processing approach to enhance generalization of the models. To demonstrate the correctness of numerical results, an occlusion test is applied to visualize outputs of the model and localize the detected appropriate area. A different dataset as an extra validation is used in order to investigate the generalization of the proposed model. RESULTS: The best accuracy to detect consolidation is 94.67% obtained by our problem based architecture for the understudy dataset which outperforms the previous works and the other architectures. CONCLUSIONS: The designed models can be employed as computer aided diagnosis tools in real practice. We critically discussed the datasets and the previous works based on them and show that without some considerations the results of them may be misleading. We believe, the output of AI should be only interpreted as focal consolidation. The clinical significance of the finding can not be interpreted without integration of clinical data.

Broadband, electrically tunable third-harmonic generation in graphene.

Optical harmonic generation occurs when high intensity light (>1010 W m-2) interacts with a nonlinear material. Electrical control of the nonlinear optical response enables applications such as gate-tunable switches and frequency converters. Graphene displays exceptionally strong light-matter interaction and electrically and broadband tunable third-order nonlinear susceptibility. Here, we show that the third-harmonic generation efficiency in graphene can be increased by almost two orders of magnitude by controlling the Fermi energy and the incident photon energy. This enhancement is due to logarithmic resonances in the imaginary part of the nonlinear conductivity arising from resonant multiphoton transitions. Thanks to the linear dispersion of the massless Dirac fermions, gate controllable third-harmonic enhancement can be achieved over an ultrabroad bandwidth, paving the way for electrically tunable broadband frequency converters for applications in optical communications and signal processing.

Ultra-strong nonlinear optical processes and trigonal warping in MoS2 layers.

Nonlinear optical processes, such as harmonic generation, are of great interest for various applications, e.g., microscopy, therapy, and frequency conversion. However, high-order harmonic conversion is typically much less efficient than low-order, due to the weak intrinsic response of the higher-order nonlinear processes. Here we report ultra-strong optical nonlinearities in monolayer MoS2 (1L-MoS2): the third harmonic is 30 times stronger than the second, and the fourth is comparable to the second. The third harmonic generation efficiency for 1L-MoS2 is approximately three times higher than that for graphene, which was reported to have a large χ (3). We explain this by calculating the nonlinear response functions of 1L-MoS2 with a continuum-model Hamiltonian and quantum mechanical diagrammatic perturbation theory, highlighting the role of trigonal warping. A similar effect is expected in all other transition-metal dichalcogenides. Our results pave the way for efficient harmonic generation based on layered materials for applications such as microscopy and imaging.Harmonic generation is a nonlinear optical process occurring in a variety of materials; the higher orders generation is generally less efficient than lower orders. Here, the authors report that the third-harmonic is thirty times stronger than the second-harmonic in monolayer MoS2.

Spin relaxation and the Kondo effect in transition metal dichalcogenide monolayers.

We investigate the spin relaxation and Kondo resistivity caused by magnetic impurities in doped transition metal dichalcogenide monolayers. We show that momentum and spin relaxation times, due to the exchange interaction by magnetic impurities, are much longer when the Fermi level is inside the spin-split region of the valence band. In contrast to the spin relaxation, we find that the dependence of Kondo temperature T K on the doping is not strongly affected by the spin-orbit induced splitting, although only one of the spin species are present at each valley. This result, which is obtained using both perturbation theory and the poor man's scaling methods, originates from the intervalley spin-flip scattering in the spin-split region. We further demonstrate the decline in the conductivity with temperatures close to T K, which can vary with the doping. Our findings reveal the qualitative difference with the Kondo physics in conventional metallic systems and other Dirac materials.

Edge modes in zigzag and armchair ribbons of monolayer MoS2.

We explore the electronic structure, orbital character and topological aspect of a monolayer MoS2 nanoribbon using tight-binding (TB) and low-energy ([Formula: see text]) models. We obtain a mid-gap edge mode in the zigzag ribbon of monolayer MoS2, which can be traced back to the topological properties of the bulk band structure. Monolayer MoS2 can be considered as a valley Hall insulator. The boundary conditions at armchair edges mix the valleys on the edges, and a gap is induced in the edge modes. The spin-orbit coupling in the valence band reduces the hybridization of the bulk states.

Pgu-Face: A dataset of partially covered facial images.

In this article we introduce a human face image dataset. Images were taken in close to real-world conditions using several cameras, often mobile phone׳s cameras. The dataset contains 224 subjects imaged under four different figures (a nearly clean-shaven countenance, a nearly clean-shaven countenance with sunglasses, an unshaven or stubble face countenance, an unshaven or stubble face countenance with sunglasses) in up to two recording sessions. Existence of partially covered face images in this dataset could reveal the robustness and efficiency of several facial image processing algorithms. In this work we present the dataset and explain the recording method.

High resolution melting technique for molecular epidemiological studies of cystic echinococcosis: differentiating G1, G3, and G6 genotypes of Echinococcus granulosus sensu lato.

Reliable and rapid genotyping of large number of Echinococcus granulosus sensu lato isolates is crucial for understanding the epidemiology and transmission of cystic echinococcosis. We have developed a method for distinguishing and discriminating common genotypes of E. granulosus s.l. (G1, G3, and G6) in Iran. This method is based on polymerase chain reaction coupled with high resolution melting curve (HRM), ramping from 70 to 86 °C with fluorescence data acquisition set at 0.1 °C increments and continuous fluorescence monitoring. Consistency of this technique was assessed by inter- and intra-assays. Assessment of intra- and inter-assay variability showed low and acceptable coefficient of variations ranging from 0.09 to 0.17 %. Two hundred and eighty E. granulosus s.l. isolates from sheep, cattle, and camel were used to evaluate the applicability and accuracy of the method. The isolates were categorized as G1 (93, 94, and 25%), G3 (7, 4, and 4%), and G6 (0, 2, and 71%) for sheep, cattle, and camel, respectively. HRM results were completely compatible with those obtained from sequencing and rostellar hook measurement. This method proved to be a valuable screening tool for large-scale molecular epidemiological studies.