相关论文: Machine learning delta-T noise for temperature bia…

In deep learning-based classification tasks, the softmax function's temperature parameter $T$ critically influences the output distribution and overall performance. This study presents a novel theoretical insight that the optimal…

This paper proposes a new methodological framework within which the heat conductance in 1D lattices can be studied. The total process of heat conductance is separated into two parts where the first one is the equilibrium process at equal…

We develop Johnson noise thermometry applicable to mesoscopic devices with variable source impedance with high bandwidth for fast data acquisition. By implementing differential noise measurement and two-stage impedance matching, we…

With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessly. Whenever critical temperatures cannot be measured…

The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional electron-phonon superconductors, including the retardation of the interaction and the effect of the Coulomb pseudopotential, to predict the…

It is now well known that neural networks can be wrong with high confidence in their predictions, leading to poor calibration. The most common post-hoc approach to compensate for this is to perform temperature scaling, which adjusts the…

We have proposed novel calorimetric tunneling (CT) experiment allowing exact determination of heat generation (or heat sinking) in individual tunnel junction (TJ) electrodes which opens new possibilities in the field of design and…

Parameterized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and…

We report the on-wafer characterization of $S$-parameters and microwave noise temperature ($T_{50}$) of discrete metamorphic InGaAs high electron mobility transistors (mHEMTs) at 40 K and 300 K and over a range of drain-source voltages…

Modern deep networks are trained with stochastic gradient descent (SGD) whose key hyperparameters are the number of data considered at each step or batch size $B$, and the step size or learning rate $\eta$. For small $B$ and large $\eta$,…

GaN/AlN interfaces are essential in advanced high-power and high-frequency electronic devices, where effective thermal management is crucial for optimal performance and reliability. This work investigates the thermal boundary conductance…

Coulomb blockade thermometers (CBTs) are versatile and, in principle, primary thermometers operating down to the micro-Kelvin range but bias heating spoils the thermometry and the primary mode. Here, we introduce a method to extract the CBT…

In this study we perform online sea ice bias correction within a GFDL global ice-ocean model. For this, we use a convolutional neural network (CNN) which was developed in a previous study (Gregory et al., 2023) for the purpose of predicting…

Nonequilibrium states driven by both electric bias voltages $V$ and temperature differences $\Delta T$ (or thermal voltages $eV_T\equiv k_B\Delta T$) are unique probes of various systems. Whereas average currents $I(V,V_T)$ are…

Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics. In particular, there has been significant interest in applying deep learning techniques to predicting the mechanical…

Research interests in the robustness of deep neural networks against domain shifts have been rapidly increasing in recent years. Most existing works, however, focus on improving the accuracy of the model, not the calibration performance…

Machine learning (ML) methods provide advanced means for understanding inherent patterns within large and complex datasets. Here, we employ the principal component analysis (PCA) and the diffusion map (DM) techniques to evaluate the glass…

Transition-edge sensor (TES) is a highly sensitive device that is capable of detecting extremely low levels of energy. It is characterised by low noise performance and high energy resolution. A mature method for reading out TES signal is…

Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are…

Routinely, experiments on tunneling molecular junctions report values of conductances ($G_{RT}$) and currents ($I_{RT}$) measured at room temperature. On the other side, theoretical approaches based on simplified models provide analytic…