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Electrically induced $p-n$ junctions in graphene bilayer (GBL) have shown superior performance for detection of sub-THz radiation at cryogenic temperatures, especially upon electrical induction of the band gap $E_g$. Still, the upper limits of responsivity and noise equivalent power (NEP) at very large $E_g$ remained unknown. Here, we study the cryogenic performance of GBL detectors at $f=0.13$ THz by inducing gaps up to $E_g \approx 90$ meV, a value close to the limits observed in recent transport experiments. High value of the gap is achieved by using high-$\kappa$ bottom hafnium dioxide gate dielectric. The voltage responsivity, current responsivity and NEP optimized with respect to doping do not demonstrate saturation with gap induction up to its maximum values. The NEP demonstrates an order-of-magnitude drop from $\sim450$ fW/Hz$^{1/2}$ in the gapless state to $\sim30$ fW/Hz$^{1/2}$ at the largest gap. At largest induced band gaps, plasmonic oscillations of responsivity become visible and important for optimization of sub-THz response.

Graphene/hBN/graphene tunnel devices offer promise as sensitive mid-infrared photodetectors but the microscopic origin underlying the photoresponse in them remains elusive. In this work, we investigated the photocurrent generation in graphene/hBN/graphene tunnel structures with localized defect states under mid-IR illumination. We demonstrate that the photocurrent in these devices is proportional to the second derivative of the tunnel current with respect to the bias voltage, peaking during tunneling through the hBN impurity level. We revealed that the origin of the photocurrent generation lies in the change of the tunneling probability upon radiation-induced electron heating in graphene layers, in agreement with the theoretical model that we developed. Finally, we show that at a finite bias voltage, the photocurrent is proportional to the either of the graphene layers heating under the illumination, while at zero bias, it is proportional to the heating difference. Thus, the photocurrent in such devices can be used for accurate measurements of the electronic temperature providing a convenient alternative to Johnson noise thermometry.

We present the results of a new experimental study of the quantum entanglement of photon pairs produced in positron-electron annihilation at rest. The experimental setup includes a system of Compton polarimeters to measure the Compton scattering of annihilation photons in entangled and decoherent states. Decoherent states are prepared by pre-scattering of one of the initial photons prior to measurements in polarimeters. For the first time, a direct comparison of the polarization correlations of annihilation photons in the entangled and thus prepared separable states has been carried out. The angular distributions of scattered photons turned out to be the same in both quantum states, which is an unexpected discovery for the quantum-entangled positron emission tomography. Moreover, the correlation function in the Bell's inequality is also the same for entangled and separable states. It follows that, despite numerous measurements in a series of experiments, there is still no experimental proof of the entanglement of annihilation photons. These results are in line with recent theoretical predictions of an identical Compton scattering cross-section for entangled and specific mixed separable quantum states and cast doubt on the universality of the Bell's theorem for testing the entanglement and nonlocality in quantum theory., Comment: 12 pages, 8 figures

We propose an electrically pumped laser diode based on multiple HgTe quantum wells with band structure engineered for Auger recombination suppression. A model for accounting for hot phonons is developed for calculating the nonequilibrium temperature of electrons and holes. Using a comprehensive model accounting for carrier drift and diffusion, Auger recombination and hotphonon effects, we predict of lasing at $\lambda \sim 3 $ $\mu$m at room temperature in 2.2 nm HgTe/Cd$_{0.85}$Hg$_{0.15}$Te quantum well heterostructure. The output power in the pulse can reach up to 600 mW for 100 nanosecond-duration pulses., Comment: 26 pages, 7 figures, preprint

Vertebral body compression fractures are early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologists in clinical settings. Prior research on automatic methods of vertebral fracture classification proves its reliable quality; however, existing methods provide hard-to-interpret outputs and sometimes fail to process cases with severe abnormalities such as highly pathological vertebrae or scoliosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then detect individual vertebrae and quantify fractures in 2D simultaneously. We train neural networks for both steps using a simple 6-keypoints based annotation scheme, which corresponds precisely to the current clinical recommendation. Our algorithm has no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and provides an interpretable and verifiable output. The method approaches expert-level performance and demonstrates state-of-the-art results in vertebrae 3D localization (the average error is 1 mm), vertebrae 2D detection (precision and recall are 0.99), and fracture identification (ROC AUC at the patient level is up to 0.96). Our anchor-free vertebra detection network shows excellent generalizability on a new domain by achieving ROC AUC 0.95, sensitivity 0.85, specificity 0.9 on a challenging VerSe dataset with many unseen vertebra types., Comment: arXiv admin note: text overlap with arXiv:2005.11960

One of the main tools used to understand both qualitative and quantitative spectral behaviour of periodic and almost periodic Schr\"odinger operators is the method of gauge transform. In this paper, we extend this method to an abstract setting, thus allowing for greater flexibility in its applications that include, among others, matrix-valued operators. In particular, we obtain asymptotic expansions for the density of states of certain almost periodic systems of elliptic operators, including systems of Dirac type. We also prove that a range of periodic systems including the two-dimensional Dirac operators satisfy the Bethe--Sommerfeld property, that the spectrum contains a semi-axis -- or indeed two semi-axes in the case of operators that are not semi-bounded., Comment: 86 pages, comment welcome

Operation of semiconductor lasers in the 20--50 $\mu$m wavelength range is hindered by strong non-radiative recombination in the interband laser diodes, and strong lattice absorption in GaAs-based quantum cascade structures. Here, we propose an electrically pumped laser diode based on multiple HgTe quantum wells with band structure engineered for Auger recombination suppression. Using a comprehensive model accounting for carrier drift and diffusion, electron and hole capture in quantum wells, Auger recombination, and heating effects, we show the feasibility of lasing at $\lambda = 26...30$ $\mu$m at temperatures up to 90 K. The output power in the pulse can reach up to 8 mW for microsecond-duration pulses.

The controversy of computed tomography (CT) use in COVID-19 screening is associated with ambiguous characteristics of chest CT as a diagnostic test. The reported values of CT sensitivity and specificity calculated using RT-PCR as a reference standard vary widely. The objective of this study was to reevaluate the diagnostic and prognostic value of CT using an alternative approach. This study included 973 symptomatic COVID-19 patients aged 42 $\pm$ 17 years, 56% females. We reviewed the disease dynamics between the initial and follow-up CT studies using a "CT0-4" grading system. Sensitivity and specificity were calculated as conditional probabilities that a patient's condition would improve or deteriorate relative to the initial CT study results. For the calculation of negative (NPV) and positive (PPV) predictive values, we estimated the COVID-19 prevalence in Moscow. We used several ARIMA and EST models with different parameters to fit the data on total cases of COVID-19 from March 6, 2020, to July 20, 2020, and forecast the incidence. The "CT0-4" grading scale demonstrated low sensitivity (28%) but high specificity (95%). The best statistical model for describing the pandemic in Moscow was ETS with multiplicative trend, error, and season type. According to our calculations, with the predicted prevalence of 2.1%, the values of NPV and PPV would be 98% and 10%, correspondingly. We associate the low sensitivity and PPV values with the small sample size of the patients with severe symptoms and non-optimal methodological setup for measuring these specific characteristics. The "CT0-4" grading scale was highly specific and predictive for identifying admissions to hospitals of COVID-19 patients. Despite the ambiguous accuracy, chest CT proved to be an effective practical tool for patient management during the pandemic, provided that the necessary infrastructure and human resources are available., Comment: 10 pages, 2 figures, 3 tables

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight studies of severe patients and direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to combine all available labels within a single model. In contrast with the most popular multitask approaches, we add classification layers to the most spatially detailed upper part of U-Net instead of the bottom, less detailed latent representation. We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in an out-patient hospital. The proposed multitask model outperforms the latent-based one and achieves ROC AUC scores ranging from 0.87+-01 (bacterial pneumonia) to 0.97+-01 (healthy controls) for Identification of COVID-19 and 0.97+-01 Spearman Correlation for Severity quantification. We release all the code and create a public leaderboard, where other community members can test their models on our test dataset.

Vertebral body compression fractures are reliable early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologists in clinical settings. Prior research on automatic methods of vertebral fracture classification proves its reliable quality; however, existing methods provide hard-to-interpret outputs and sometimes fail to process cases with severe abnormalities such as highly pathological vertebrae or scoliosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then to simultaneously detect individual vertebrae and quantify fractures in 2D. We train neural networks for both steps using a simple 6-keypoints based annotation scheme, which corresponds precisely to current medical recommendation. Our algorithm has no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and provides an intuitive and verifiable output. The method approaches expert-level performance and demonstrates state-of-the-art results in vertebrae 3D localization (the average error is 1 mm), vertebrae 2D detection (precision is 0.99, recall is 1), and fracture identification (ROC AUC at the patient level is 0.93)., Comment: Accepted to MICCAI-2020

Twinkling Artifact is a valuable tool in detecting dense objects such as kidney stones, calculi etc., especially when there is no acoustic shadowing and presence of hyperechogenic tissues obstructs visualization. This phenomenon is not completely understood. Different scientific groups have contradictory findings concerning its properties: some of them observed a decrease in Twinkling intensity at elevated pulse repetition frequencies (PRF), while others found Twinkling to be independent from PRF, etc. In this paper we hypothesize that this kind of contradictions can be partially resolved on an assumption that there are two types of Twinkling. The 1st type presumably is produced by random-phased reflections from microcavitation and can be registered even at PRF high enough to suppress blood flow. The 2nd is originated from elastic vibrations as the object under investigation swings like a pendulum in the field of ultrasound. These vibrations can be associated with an external source such as a muscles contraction, etc. or the acoustic radiation force from signals emitted by the transducer. The 2nd type of Twinkling disappears at high PRF and can be regularly observed in silicone and polyurethane phantoms where the occurrence of cavitation microbubbles is highly unlikely. Key Words: Ultrasound Imaging, Twinkling Artifact, Stone Detection, Doppler Effect, Color Frame Mapping, Acoustic Radiation Force, Elastic Vibration, Ultrasound Phantom, Cavitation, Microbubbles.

Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors. The importance of automatic estimation of MLS was recently highlighted by ACR Data Science Institute. In this paper we introduce a novel deep learning based approach for the problem of MLS detection, which exploits task-specific structural knowledge. We evaluate our method on a large dataset containing heterogeneous images with significant MLS and show that its mean error approaches the inter-expert variability. Finally, we show the robustness of our approach by validating it on an external dataset, acquired during routine clinical practice.

A challenge of bridging the terahertz gap with semiconductor lasers faces an inevitable problem of enhanced non-radiative Auger recombination with reduction of photon energy. We show that this problem can be mitigated in mercury-cadmium-telluride quantum wells (HgCdTe QWs) wherein the Auger process is suppressed due to formation of quasi-relativistic electron-hole dispersion imposing strong energy-momentum restrictions on recombining carriers. Such dispersion is formed upon interaction of topological states at the two QW interfaces. We characterize the lasing properties of HgCdTe QWs quantitatively by constructing a microscopic theory for recombination, absorption, and gain, and show the feasibility of lasing down to ~ 50 $\mu$m at liquid nitrogen temperature with threshold currents two orders of magnitude lower than in existing lasers. Our findings comply with recent experimental data on stimulated far-infrared emission from HgCdTe QWs and show the directions toward achievement of maximum possible lasing wavelength.