相关论文: Machine learning delta-T noise for temperature bia…
The prediction reliability of neural networks is important in many applications. Specifically, in safety-critical domains, such as cancer prediction or autonomous driving, a reliable confidence of model's prediction is critical for the…
Machine learning techniques have emerged as powerful tools for tackling non-perturbative challenges in quantum chromodynamics. In this study, we introduce a data-driven framework employing deep neural networks to systematically predict the…
We have studied the origin of excess noise in superconducting transition-edge sensors (TES) with several different detector designs. We show that most of the observed noise and complex impedance features can be explained by a thermal model…
We apply noise thermometry to characterize charge and thermoelectric transport in single InAs nanowires (NWs) at a bath temperature of 4.2 K. Shot noise measurements identify elastic diffusive transport in our NWs with negligible…
Recently, Deep Neural Networks (DNNs) have been achieving impressive results on wide range of tasks. However, they suffer from being well-calibrated. In decision-making applications, such as autonomous driving or medical diagnosing, the…
Quantum noise or decoherence is a major factor impacting the performance of quantum technologies. On the qubit, an important quantum noise, often relevant in practice, is the thermal noise or generalized amplitude damping noise, describing…
Recently a relationship between the Debye temperature $\Theta_D$ and the superconducting transition temperature $T_c$ of conventional superconductors has been proposed [npj Quantum Materials $\mathbf{3}$, 59 (2018)]. The relationship…
Poisson statistics predicts that the shot noise in a tunnel junction has a temperature independent third cumulant e^2\I, determined solely by the mean current I. Experimental data, however, show a puzzling temperature dependence. We…
Continual Learning (CL) is recently gaining increasing attention for its ability to enable a single model to learn incrementally from a sequence of new classes. In this scenario, it is important to keep consistent predictive performance…
The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) effects often cause permanent damages…
Probes that measure the local thermal properties of systems out of equilibrium are emerging as new tools in the study of nanoscale systems. One can then measure the temperature of a probe that is weakly coupled to a bias-driven system. By…
We propose a new method to measure the system noise temperature, $T_{\rm sys}$, using a 2-bit analog-to-digital converter (ADC). The statistics of the digitized signal in a four-level quantization brings us information about the bias…
Emulating thermal observables on a digital quantum computer is essential for quantum simulation of many-body physics. However, thermalization typically requires a large system size due to incorporating a thermal bath, whilst limited…
We discuss an aspect of neural networks for the purpose of phase transition detection. To this end, we first train the neural network by feeding Ising/Potts configurations with labels of temperature so that it can predict the temperature of…
Microrefrigerators based on normal metal-insulator-superconductor (NIS) junctions represent a very attractive alternative to cool the microbolometers and calorimeters for astrophysical observations in space-borne experiments. The…
In this paper, we attempt to employ convolutional recurrent neural networks for weather temperature estimation using only image data. We study ambient temperature estimation based on deep neural networks in two scenarios a) estimating…
Reliable mechanical fault detection with limited data is crucial for the effective operation of induction machines, particularly given the real-world challenges present in industrial datasets, such as significant imbalances between healthy…
The number of electrified powertrains is ever increasing today towards a more sustainable future; thus, it is essential that unwanted failures are prevented, and a reliable operation is secured. Monitoring the internal temperatures of…
The operation of the primary Coulomb blockade thermometer (CBT) is based on a measurement of bias voltage dependent conductance of arrays of tunnel junctions between normal metal electrodes. Here we report on a comparison of a CBT with a…
Training data attribution (TDA) techniques find influential training data for the model's prediction on the test data of interest. They approximate the impact of down- or up-weighting a particular training sample. While conceptually useful,…