We present a theoretical study of radiative heat transport in nonlinear solid-state quantum circuits. We give a detailed account of heat rectification effects, i.e. the asymmetry of heat current with respect to a reversal of the thermal gradient, in a system consisting of two reservoirs at finite temperatures coupled through a nonlinear resonator. We suggest an experimentally feasible superconducting circuit employing the Josephson nonlinearity to realize a controllable low temperature heat rectifier with a maximal asymmetry of the order of 10%. We also discover a parameter regime where the rectification changes sign as a function of temperature.Comment: 5 pages, 5 figures; v2: added discussion on rectification sig

Bosonic thermal transport through a two-level system is analyzed at temperatures below and comparable to the two-level energy splitting. It is shown that in the low-temperature regime transport is dominated by correlated two-boson processes analogous to electron cotunneling in quantum dots under Coulomb blockade. We present a detailed analysis of the sequential-cotunneling crossover and obtain essentially an analytic description of the transport problem. Perturbative analysis is complemented by employing scaling properties of the Ohmic spin-boson model, allowing us to extract an anomalous low temperature scaling of thermal conductance.

No abstract

We introduce a new class of mesoscopic heat engines consisting of a tunnel junction coupled to a linear thermal bath. Work is produced by transporting electrons up against a voltage bias like in ordinary thermoelectrics but heat is transferred by microwave photons, allowing the heat bath to be widely separated from the electron system. A simple and generic formalism capable of treating a variety of different types of junctions and environments is presented. We identify the systems and conditions required for maximal efficiency and maximal power. High efficiencies are possible with quantum dot arrays but high power can be achieved also with metallic systems.

Objective:In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid haemorrhage (SAH) on head computed tomography (CT) scans, and that the trained model performs satisfactorily when tested using external and real-world data.Methods:We used non-contrast head CT images of patients admitted Helsinki University Hospital between 2012 and 2017. We manually segmented (i.e. delineated) SAH on 90 head CT scans, and used the segmented CT scans together with 22 negative (no SAH) control CT scans in training an open-source convolutional neural network (U-Net) to identify and localize SAH. We then tested the performance of the trained algorithm by using external datasets (137 SAH and 1242 control cases) collected in two foreign countries, and also by creating a dataset of consecutive emergency head CT scans (8 SAH and 511 control cases) performed during on call hours in 5 different domestic hospitals in September 2021. We assessed the algorithm’s capability to identify SAH by calculating patient- and slice-level performance metrics, such as sensitivity and specificity.Results:In the external validation set of 1379 cases, the algorithm identified 136 out of 137 SAH cases correctly (sensitivity 99.3%, specificity 63.2%). Of the 49064 axial head CT slices, the algorithm identified and localized SAH in 1845 out of 2110 slices with SAH (sensitivity 87.4%, specificity 95.3%). Of 519 consecutive emergency head CT scans imaged in September 2021, the algorithm identified all 8 SAH cases correctly (sensitivity 100.0%, specificity 75.3%). The slice-level (27167 axial slices in total) sensitivity and specificity were 87.3% and 98.8%, as the algorithm identified and localized SAH in 58 out of 77 slices with SAH. The performance of the algorithm can be tested on through a webservice.Conclusions:We show that the shared algorithm identifies SAH cases with a high sensitivity, and that the slice-level specificity is high. In addition to openly sharing a high-performing deep learning algorithm, our work presents infrequently used approaches in designing, training, testing and reporting deep learning algorithms developed for medical imaging diagnostics.Classification of Evidence:This study provides Class III evidence a deep learning algorithm correctly identifies the presence of subarachnoid hemorrhage on CT scan.