A Universal Roadmap For Searching Repulsive Casimir Forces Between Magneto-Electric Materials

The Casimir effect, arising from vacuum quantum fluctuations, plays a fundamental role in the development of modern quantum electrodynamics. In parallel, the field of condensed matter has flourished through the discovery of various materials exhibiting broken symmetries, often connected to topology and characterized by magneto-electric coupling. Here, we calculate the Casimir forces between materials with time-reversal symmetry and/or parity symmetry breaking. Remarkably, we obtain a universal phase diagram governing the sign of symmetry-breaking-induced Casimir forces, contributing to a comprehensive understanding on the sign of Casimir force for linear optical materials. The discovered phase diagram serves as a roadmap for searching repulsive Casimir forces, a subject bearing both theoretical interest and practical significance

Pre-training with Synthetic Data Helps Offline Reinforcement Learning

Recently, it has been shown that for offline deep reinforcement learning (DRL), pre-training Decision Transformer with a large language corpus can improve downstream performance (Reid et al., 2022). A natural question to ask is whether this performance gain can only be achieved with language pre-training, or can be achieved with simpler pre-training schemes which do not involve language. In this paper, we first show that language is not essential for improved performance, and indeed pre-training with synthetic IID data for a small number of updates can match the performance gains from pre-training with a large language corpus; moreover, pre-training with data generated by a one-step Markov chain can further improve the performance. Inspired by these experimental results, we then consider pre-training Conservative Q-Learning (CQL), a popular offline DRL algorithm, which is Q-learning-based and typically employs a Multi-Layer Perceptron (MLP) backbone. Surprisingly, pre-training with simple synthetic data for a small number of updates can also improve CQL, providing consistent performance improvement on D4RL Gym locomotion datasets. The results of this paper not only illustrate the importance of pre-training for offline DRL but also show that the pre-training data can be synthetic and generated with remarkably simple mechanisms.Comment: 28 pages, 7 figure

Review of the role of pyroptosis in benign prostatic hyperplasia in old males

Pyroptosis, a new mode of programmed cell death, is primarily characterized by persistent cellular swelling that culminates in cell rupture. This process results in the release of large amounts of inflammatory factors, subsequently triggering an inflammatory response. Benign prostatic hyperplasia (BPH) is the most frequent urological disease in old males and is closely associated with changes in hormones and inflammation response. In recent years, the role of pyroptosis in the occurrence and development of BPH has also received increasing attention. This article summarizes the mechanisms of pyroptosis, concludes the pathogenesis associated with BPH in old males, and outlines the role of pyroptosis in BPH, to provide new ideas for finding more effective therapeutic measures for BPH through pyroptosis

Cross Entropy versus Label Smoothing: A Neural Collapse Perspective

Label smoothing loss is a widely adopted technique to mitigate overfitting in deep neural networks. This paper studies label smoothing from the perspective of Neural Collapse (NC), a powerful empirical and theoretical framework which characterizes model behavior during the terminal phase of training. We first show empirically that models trained with label smoothing converge faster to neural collapse solutions and attain a stronger level of neural collapse. Additionally, we show that at the same level of NC1, models under label smoothing loss exhibit intensified NC2. These findings provide valuable insights into the performance benefits and enhanced model calibration under label smoothing loss. We then leverage the unconstrained feature model to derive closed-form solutions for the global minimizers for both loss functions and further demonstrate that models under label smoothing have a lower conditioning number and, therefore, theoretically converge faster. Our study, combining empirical evidence and theoretical results, not only provides nuanced insights into the differences between label smoothing and cross-entropy losses, but also serves as an example of how the powerful neural collapse framework can be used to improve our understanding of DNNs

Quench of a Single-Layer ReBCO CORC Cable with Non-Uniform Terminal Contact Resistance

ReBCO conductor-on-round-core (CORC) cable has become a promising candidate for high temperature superconducting (HTS) power applications, due to its great mechanical strength, high current carrying capacity, high flexibility, and low ac losses. However, ReBCO coated conductors are at risk of quenching, which significantly affects the thermal stability and reliability of the CORC cable. Three-dimensional (3-D) numerical study on the quench behavior of the CORC cable remains a challenge, for its complex geometry is difficult to cope with. In this paper, a 3-D time-dependent multi-physics quench model based on the T-A formulation has been developed. Three modules are coupled in this model; the T-A formulation model, a heat transfer model, and an equivalent circuit model. The quench behavior of a single-layer ReBCO CORC cable with non-uniform terminal contact resistances has been studied, when a hotspot is imposed on one of the tapes to induce a local quench. Results show that, the CORC cable has the highest MQE; in other words, it is the most stable situation, when the hotspot-induced quench occurs on the tape with the middle value of terminal contact resistance

Overexpression of CARMA3 in Non-Small-Cell Lung Cancer Is Linked for Tumor Progression

We aimed to investigate the clinical significance of the expression of novel scaffold protein CARMA3 in non-small-cell lung cancer (NSCLC) and the biological function of CARMA3 in NSCLC cell lines. We observed moderate to high CARMA3 staining in 68.8% of 141 NSCLC specimens compared to corresponding normal tissues. The overexpression of CARMA3 was significantly correlated with TNM stage (P = 0.022) and tumor status (P = 0.013). CARMA3 upregulation also correlated with a shorter survival rate of patients of nodal status N0 (P = 0.042)as well as the expression of epidermal growth factor receptor (EGFR) (P = 0.009). In EGFR mutation positive cases, CARMA3 expression was much higher (87.5%) compared to non-mutation cases (66.1%). In addition, we observed that knockdown of CARMA3 inhibits tumor cell proliferation and invasion, and induces cell cycle arrest at the boundary between the G1 and S phase. We further demonstrated a direct link between CARMA3 and NF-κB activation. The change of biological behavior in CARMA3 knockdown cells may be NF-κB-related. Our findings demonstrated, for the first time, that CARMA3 was overexpressed in NSCLC and correlated with lung cancer progression, EGFR expression, and EGFR mutation. CARMA3 could serve as a potential companion drug target, along with NF-kB and EGFR in EGFR-mutant lung cancers

Experimental quantum adversarial learning with programmable superconducting qubits

Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning tasks. Yet, recent theoretical works show that, similar to traditional classifiers based on deep classical neural networks, quantum classifiers would suffer from the vulnerability problem: adding tiny carefully-crafted perturbations to the legitimate original data samples would facilitate incorrect predictions at a notably high confidence level. This will pose serious problems for future quantum machine learning applications in safety and security-critical scenarios. Here, we report the first experimental demonstration of quantum adversarial learning with programmable superconducting qubits. We train quantum classifiers, which are built upon variational quantum circuits consisting of ten transmon qubits featuring average lifetimes of 150 $\mu$s, and average fidelities of simultaneous single- and two-qubit gates above 99.94% and 99.4% respectively, with both real-life images (e.g., medical magnetic resonance imaging scans) and quantum data. We demonstrate that these well-trained classifiers (with testing accuracy up to 99%) can be practically deceived by small adversarial perturbations, whereas an adversarial training process would significantly enhance their robustness to such perturbations. Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.Comment: 26 pages, 17 figures, 8 algorithm