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 $Wtb$ 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 $tq$ and $t\bar{b}$ production processes. The resonant effect can greatly enhance the cross sections of the $tq$ and $t\bar{b}$ productions. The first evidence of the single t-quark production has been reported by the $D0$ collaboration and the measured cross section for the single t-quark production of $\sigma(p\bar{p}\to tb+X,tqb+X)$ is compatible at the 10% level with the standard model prediction. Because the light top-pion can make great contribution to the $t\bar{b}$ 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