14,594 research outputs found
Nuclear suppression of meson yields with large at the RHIC and the LHC
We calculate meson transverse momentum spectra in p+p collisions as
well as their nuclear suppressions in central A+A collisions both at the RHIC
and the LHC in LO and NLO with the QCD-improved parton model. We have included
the parton energy loss effect in hot/dense QCD medium with the effectively
medium-modified fragmentation functions in the higher-twist approach of
jet quenching. The nuclear modification factors of meson in central
Au+Au collisions at the RHIC and central Pb+Pb collisions at the LHC are
provided, and a nice agreement of our numerical results at NLO with the ALICE
measurement is observed. Predictions of yield ratios of neutral mesons such as
, and at large in relativistic
heavy-ion collisions are also presented for the first time.Comment: 7 pages, 8 figure
Optimization and Analysis of Wireless Powered Multi-antenna Cooperative Systems
In this paper, we consider a three-node cooperative wireless powered
communication system consisting of a multi-antenna hybrid access point (H-AP)
and a single-antenna relay and a single-antenna user. The energy constrained
relay and user first harvest energy in the downlink and then the relay assists
the user using the harvested power for information transmission in the uplink.
The optimal energy beamforming vector and the time split between harvest and
cooperation are investigated. To reduce the computational complexity,
suboptimal designs are also studied, where closed-form expressions are derived
for the energy beamforming vector and the time split. For comparison purposes,
we also present a detailed performance analysis in terms of the achievable
outage probability and the average throughput of an intuitive energy
beamforming scheme, where the H-AP directs all the energy towards the user. The
findings of the paper suggest that implementing multiple antennas at the H-AP
can significantly improve the system performance, and the closed-form
suboptimal energy beamforming vector and time split yields near optimal
performance. Also, for the intuitive beamforming scheme, a diversity order of
(N+1)/2 can be achieved, where N is the number of antennas at the H-AP
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
Offline reinforcement learning (RL) presents a promising approach for
learning reinforced policies from offline datasets without the need for costly
or unsafe interactions with the environment. However, datasets collected by
humans in real-world environments are often noisy and may even be maliciously
corrupted, which can significantly degrade the performance of offline RL. In
this work, we first investigate the performance of current offline RL
algorithms under comprehensive data corruption, including states, actions,
rewards, and dynamics. Our extensive experiments reveal that implicit
Q-learning (IQL) demonstrates remarkable resilience to data corruption among
various offline RL algorithms. Furthermore, we conduct both empirical and
theoretical analyses to understand IQL's robust performance, identifying its
supervised policy learning scheme as the key factor. Despite its relative
robustness, IQL still suffers from heavy-tail targets of Q functions under
dynamics corruption. To tackle this challenge, we draw inspiration from robust
statistics to employ the Huber loss to handle the heavy-tailedness and utilize
quantile estimators to balance penalization for corrupted data and learning
stability. By incorporating these simple yet effective modifications into IQL,
we propose a more robust offline RL approach named Robust IQL (RIQL). Extensive
experiments demonstrate that RIQL exhibits highly robust performance when
subjected to diverse data corruption scenarios.Comment: 31 pages, 17 figure
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