610 research outputs found
Prospects of Identifying Hierarchical Triple Mergers for the Third-generation Ground-based Detectors
A hierarchical triple merger (HTM) constitutes a type of event in which two
successive black hole (BH) mergers occur sequentially within the observational
window of gravitational wave (GW) detectors, which has important role in
testing general relativity and studying BH population. In this work, we conduct
an analysis to determine the feasibility of identifying HTMs from a large GW
event catalog using the third-generation ground-based GW detectors. By
comparing the Bhattacharyya coefficient that measures the overlap between the
posterior distributions of the remnant and progenitor BH parameters, we find
that the overlap between the event pair can serve as a preliminary filter,
which balances between computational demand and the probability of false
alarms. Following this initial, time-efficient, yet less accurate screening, a
subset of potential HTM candidates will be retained. These candidates will
subsequently be subjected to a more precise, albeit time-intensive, method of
joint parameter estimation for verification. Ultimately, this process will
enable us to robustly identify HTMs.Comment: 13 pages, 8 figures, ApJ accepte
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
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