Efficiency and power of minimally nonlinear irreversible heat engines with broken time-reversal symmetry

We study the minimally nonlinear irreversible heat engines in which the time-reversal symmetry for the systems may b e broken. The expressions for the power and the efficiency are derived, in which the effects of the nonlinear terms due to dissipations are included. We show that, as within the linear responses, the minimally nonlinear irreversible heat engines enable attainment of Carnot efficiency at positive power. We also find that the Curzon-Ahlborn limit imposed on the efficiency at maximum power can be overcomed if the time-reversal symmetry is broken

Efficiency at maximum power output of an irreversible Carnot-like cycle with internally dissipative friction

We investigate the efficiency at maximum power of an irreversible Carnot engine performing finite-time cycles between two reservoirs at temperatures $T_h$ and $T_c$ $(T_c<T_h)$, taking into account of internally dissipative friction in two "adiabatic" processes. In the frictionless case, the efficiencies at maximum power output are retrieved to be situated between $\eta_{_C}/$ and $\eta_{_C}/(2-\eta_{_C})$, with $\eta_{_C}=1-T_c/{T_h}$ being the Carnot efficiency. The strong limits of the dissipations in the hot and cold isothermal processes lead to the result that the efficiency at maximum power output approaches the values of $\eta_{_C}/$ and $\eta_{_C}/(2-\eta_{_C})$, respectively. When dissipations of two isothermal and two adiabatic processes are symmetric, respectively, the efficiency at maximum power output is founded to be bounded between 0 and the Curzon-Ahlborn (CA) efficiency $1-\sqrt{1-\eta{_C}}$, and the the CA efficiency is achieved in the absence of internally dissipative friction

Robust retrieval of material chemical states in X-ray microspectroscopy

X-ray microspectroscopic techniques are essential for studying morphological and chemical changes in materials, providing high-resolution structural and spectroscopic information. However, its practical data analysis for reliably retrieving the chemical states remains a major obstacle to accelerating the fundamental understanding of materials in many research fields. In this work, we propose a novel data formulation model for X-ray microspectroscopy and develop a dedicated unmixing framework to solve this problem, which is robust to noise and spectral variability. Moreover, this framework is not limited to the analysis of two-state material chemistry, making it an effective alternative to conventional and widely-used methods. In addition, an alternative directional multiplier method with provable convergence is applied to obtain the solution efficiently. Our framework can accurately identify and characterize chemical states in complex and heterogeneous samples, even under challenging conditions such as low signal-to-noise ratios and overlapping spectral features. Extensive experimental results on simulated and real datasets demonstrate its effectiveness and reliability.Comment: 12 page

Electro-acupuncture at Jianshi (PC5) and Neiguan (PC6) alters heart rate variability (HRV) in frightened volunteers

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Dynamic Loss For Robust Learning

Label noise and class imbalance commonly coexist in real-world data. Previous works for robust learning, however, usually address either one type of the data biases and underperform when facing them both. To mitigate this gap, this work presents a novel meta-learning based dynamic loss that automatically adjusts the objective functions with the training process to robustly learn a classifier from long-tailed noisy data. Concretely, our dynamic loss comprises a label corrector and a margin generator, which respectively correct noisy labels and generate additive per-class classification margins by perceiving the underlying data distribution as well as the learning state of the classifier. Equipped with a new hierarchical sampling strategy that enriches a small amount of unbiased metadata with diverse and hard samples, the two components in the dynamic loss are optimized jointly through meta-learning and cultivate the classifier to well adapt to clean and balanced test data. Extensive experiments show our method achieves state-of-the-art accuracy on multiple real-world and synthetic datasets with various types of data biases, including CIFAR-10/100, Animal-10N, ImageNet-LT, and Webvision. Code will soon be publicly available