4,934 research outputs found
Probing the topcolor-assisted technicolor model via the single t-quark production at Hadron colliders
In this paper, we systematically study the contribution of the TC2 model to
the single t-quark production at the Hadron colliders, specially at the LHC.
The TC2 model can contribute to the cross section of the single t-quark
production in two different ways. First, the existence of the top-pions and
top-higgs can modify the coupling via their loop contributions, and such
modification can cause the correction to the cross sections of all three
production modes. Our study shows that this kind of correction is negative and
very small in all cases. Thus it is difficult to observe such correction even
at the LHC. On the other hand, there exist the tree-level FC couplings in the
TC2 model which can also contribute to the cross sections of the and
production processes. The resonant effect can greatly enhance the
cross sections of the and productions. The first evidence of
the single t-quark production has been reported by the collaboration and
the measured cross section for the single t-quark production of
is compatible at the 10% level with the
standard model prediction. Because the light top-pion can make great
contribution to the production, the top-pion mass should be very
large in order to make the predicted cross section in the TC2 model be
consistent with the Tevatron experiments. More detailed information about the
top-pion mass and the FC couplings in the TC2 model should be obtained with the
running of the LHC.Comment: 30 pages, 3 tables, 10 figure
Deep Baseline Network for Time Series Modeling and Anomaly Detection
Deep learning has seen increasing applications in time series in recent
years. For time series anomaly detection scenarios, such as in finance,
Internet of Things, data center operations, etc., time series usually show very
flexible baselines depending on various external factors. Anomalies unveil
themselves by lying far away from the baseline. However, the detection is not
always easy due to some challenges including baseline shifting, lacking of
labels, noise interference, real time detection in streaming data, result
interpretability, etc. In this paper, we develop a novel deep architecture to
properly extract the baseline from time series, namely Deep Baseline Network
(DBLN). By using this deep network, we can easily locate the baseline position
and then provide reliable and interpretable anomaly detection result. Empirical
evaluation on both synthetic and public real-world datasets shows that our
purely unsupervised algorithm achieves superior performance compared with
state-of-art methods and has good practical applications
An Approximate Dynamic Programming Approach to Vehicle Platooning Coordination in Networks
Platooning connected and autonomous vehicles (CAVs) provide significant
benefits in terms of traffic efficiency and fuel economy. However, most
existing platooning systems assume the availability of pre-determined plans,
which is not feasible in real-time scenarios. In this paper, we address this
issue in time-dependent networks by formulating a Markov decision process at
each junction, aiming to minimize travel time and fuel consumption. Initially,
we analyze coordinated platooning without routing to explore the cooperation
among controllers on an identical path. We propose two novel approaches based
on approximate dynamic programming, offering suboptimal control in the context
of a stochastic finite horizon problem. The results demonstrate the superiority
of the approximation in the policy space. Furthermore, we investigate
platooning in a network setting, where speed profiles and routes are determined
simultaneously. To simplify the problem, we decouple the action space by
prioritizing routing decisions based on travel time estimation. We subsequently
employ the aforementioned policy approximation to determine speed profiles,
considering essential parameters such as travel times. Our simulation results
in SUMO indicate that our method yields better performance than conventional
approaches, leading to potential travel cost savings of up to 40%.
Additionally, we evaluate the resilience of our approach in dynamically
changing networks, affirming its ability to maintain efficient platooning
operations
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