Lifting the Veil: Unlocking the Power of Depth in Q-learning

With the help of massive data and rich computational resources, deep Q-learning has been widely used in operations research and management science and has contributed to great success in numerous applications, including recommender systems, supply chains, games, and robotic manipulation. However, the success of deep Q-learning lacks solid theoretical verification and interpretability. The aim of this paper is to theoretically verify the power of depth in deep Q-learning. Within the framework of statistical learning theory, we rigorously prove that deep Q-learning outperforms its traditional version by demonstrating its good generalization error bound. Our results reveal that the main reason for the success of deep Q-learning is the excellent performance of deep neural networks (deep nets) in capturing the special properties of rewards namely, spatial sparseness and piecewise constancy, rather than their large capacities. In this paper, we make fundamental contributions to the field of reinforcement learning by answering to the following three questions: Why does deep Q-learning perform so well? When does deep Q-learning perform better than traditional Q-learning? How many samples are required to achieve a specific prediction accuracy for deep Q-learning? Our theoretical assertions are verified by applying deep Q-learning in the well-known beer game in supply chain management and a simulated recommender system

Statistical Origin of Constituent-Quark Scaling in the QGP hadronization

Nonextensive statistics in a Blast-Wave model (TBW) is implemented to describe the identified hadron production in relativistic p+p and nucleus-nucleus collisions. Incorporating the core and corona components within the TBW formalism allows us to describe simultaneously some of the major observations in hadronic observables at the Relativistic Heavy-Ion Collider (RHIC): the Number of Constituent Quark Scaling (NCQ), the large radial and elliptic flow, the effect of gluon saturation and the suppression of hadron production at high transverse momentum (pT) due to jet quenching. In this formalism, the NCQ scaling at RHIC appears as a consequence of non-equilibrium process. Our study also provides concise reference distributions with a least chi2 fit of the available experimental data for future experiments and models.Comment: 4 pages, 3 figures; added two tables, explained a little bit more on TBW_p

Detection of lymphangiogenesis in non-small cell lung cancer and its prognostic value

<p>Abstract</p> <p>Background</p> <p>Our aim was to detect lymphatic endothelial marker podoplanin, lymphatic vessel endothelial hyaluronan receptor-1 (LYVE-1) and vascular endothelial growth factor receptor-3 (VEGFR)-3 and study the prognostic relevance of lymphangiogenesis in non-small cell lung cancer (NSCLC).</p> <p>Materials</p> <p>82 paraffin-embedded tissues and 40 fresh frozen tissues from patients with NSCLC were studied. Tumor samples were immunostained for the lymphatic endothelial markers. Lymphangiogenesis was assessed by immunohistochemical double stains for Podoplanin and Ki-67. The prognostic relevance of lymphangiogenesis-related clinicopathological parameters in NSCLC was evaluated.</p> <p>Results</p> <p>We found that the number of podoplanin positive vessels was correlated positively with the number of LYVE-1 positive vessels. Most of VEGFR-3 positive, few of LYVE-1 positive and none of podoplanin positive vessels were blood vessels. Peritumoral lymphatic vessel density (ptLVD), pathologic stage, lymph node status, lymphatic vessel invasion (LVI), vascular endothelial growth factor-C (VEGF-C) expression and Ki-67 index of the endothelium cells of the micro lymphatic vessels (Ki67%) were associated significantly with a higher risk of tumor progress. ptLVD, pathologic stage, lymph-node metastasis and Ki67% were independent prognostic parameters for overall survival.</p> <p>Conclusion</p> <p>Podoplanin positive ptLVD might play important roles in the lymphangiogenesis and progression of NSCLC. Patients with high podoplanin+ ptLVD have a poor prognosis.</p

Aurora Kinase Inhibitors in Head and Neck Cancer

Aurora kinases are a group of serine/threonine kinases responsible for the regulation of mitosis. In recent years, with the increase in Aurora kinase-related research, the important role of Aurora kinases in tumorigenesis has been gradually recognized. Aurora kinases have been regarded as a new target for cancer therapy, resulting in the development of Aurora kinase inhibitors. The study and application of these small-molecule inhibitors, especially in combination with chemotherapy drugs, represents a new direction in cancer treatment. This paper reviews studies on Aurora kinases from recent years, including studies of their biological function, their relationship with tumor progression, and their inhibitors

Enhancement of Quantum Sensing in a Cavity Optomechanical System around Quantum Critical Point

The precision of quantum sensing could be improved by exploiting quantum phase transitions, where the physical quantity tends to diverge when the system is approaching the quantum critical point. This critical enhancement phenomenon has been applied to the quantum Rabi model in a dynamic framework, showing a promising sensing enhancement without the complex initial state preparation. In this work, we find a quantum phase transition in the coupling cavity-mechanical oscillator system when the coupling strength crosses a critical point, determined by the effective detuning of cavity and frequency of mechanical mode. By utilizing this critical phenomenon, we obtain a prominent enhancement of quantum sensing, such as the position and momentum of the mechanical oscillator. This result provides an alternative method to enhance the quantum sensing of some physical quantities, such as mass, charge, and weak force, in a large mass system