RESUMO
A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.
RESUMO
BACKGROUND: Local nonsurgical tumor ablation currently represents a further option for the treatment of patients with liver tumors or metastases. Electrochemotherapy (ECT) is a welcome addition to the portfolio of local therapies. A retrospective analysis of patients with liver tumors or metastases treated with ECT is reported. Attention is given to the safety and efficacy of the treatment over time. PATIENTS AND METHODS: Eighteen consecutive patients were recruited with measurable liver tumors of different histopatologic origins, mainly colorectal cancer, breast cancer, and hepatocellular cancer. They were treated with percutaneous ECT following the standard operating procedures (SOP) for ECT under general anaesthesia and muscle relaxation. Treatment planning was performed based on MRI preoperative images. The follow-up assessment included contrast-enhanced MR within at least 1-3 months after treatment and then after 5, 7, 9, 12, and 18 months until progression of the disease or death. RESULTS: Only mild or moderate side effects were observed after ECT. The objective response rate was 85.7% (complete response 61.9%, partial 23.8%), the mean progression-free survival (PFS) was 9.0 ± 8.2 months, and the overall survival (OS) was 11.3 ± 8.6 months. ECT performed best (PFS and OS) in lesions within 3 and 6 cm diameters (p = 0.0242, p = 0.0297) . The effectiveness of ECT was independent of the localization of the lesions: distant, close or adjacent to vital structures. Progression-free survival and overall survival were independent of the primary histology considered. CONCLUSIONS: Electrochemotherapy provides an effective valuable option for the treatment of unresectable liver metastases not amenable to other ablative techniques.