X(3872) and its production at hadron colliders

We evaluate the production cross sections of $X(3872)$ at the LHC and Tevatron at NLO in $\alpha_s$ in NRQCD by assuming that the short-distance production proceeds dominantly through its $\chi_{c1}'$ component in our \chi_{c1}'\mbox{-}D^0\bar{D}^{*0} mixing model for $X(3872)$. The outcomes of the fits to the CMS $p_T$ distribution can well account for the recent ATLAS data in a much larger range of transverse momenta (10~\mbox{GeV}), and the CDF total cross section data, and are also consistent with the value of $k=Z_{c\bar c}\cdot Br(X\to J/\psi\pi^+\pi^-)$ constrained by the $B$-meson decay data. %It can also well describe the behavior of the CDF $\psi(2S)$ data, which show a strong %resemblance to that of the X(3872). For LHCb the predicted X(3872) total cross section is larger than the data by a factor of 2, which is due to the problem of the fixed-order NRQCD calculation that may not be applicable for the region with small $p_T$ (p_T\sim 5 ~\mbox{GeV}) and large forward rapidity $(2.5<y<4.5)$. In comparison, the prediction of molecule production mechanism for $X(3872)$ is inconsistent with both $p_T$ distributions and total cross sections of CMS and ATLAS, and the total cross section of CDF.Comment: Version published in PRD. More explanations added for the LHCb data. More references added for recent experimental and theoretical results: the ATLAS measurement on the X(3872) pT distribution in 10-70 GeV; the LHCb measurement on the X(3872) radiative decays; the lattice calculation on X(3872); the small $x$ resummation method, etc. No changes for the calculated result and the conclusio

On the nature of X(3960)

A near-threshold enhancement in the $D_s^+ D_s^-$ system, dubbed as $X(3960)$, is observed by the LHCb collaboration recently. A combined analysis on $\chi_{c0}(3930)~(\to D^+ D^-)$, $X(3960)~(\to D_s^+ D_s^-)$, and $X(3915)~(\to J/\psi\omega)$ is performed using both a $K$-matrix approach of $D_{(s)}\bar{D}_{(s)}$ four-point contact interactions and a model of Flatt\'e-like parameterizations. The use of the pole counting rule and spectral density function sum rule %provides consistent evidence indicate, under current statistics, that this $D_s^+ D_s^-$ near-threshold state has probably the mixed nature of a $c\bar{c}$ confining state and $D_s^+ D_s^-$ continuum.Comment: 8 pages, 4 figures, 5 table

Double-loop sliding mode control of reentry hypersonic vehicle with RCS

In order to solve the problem of insufficient aerodynamic moment caused by thin air in the re-entry stage of hypersonic vehicle, this paper establishes an attitude angle model of hypersonic vehicle with reaction control system (RCS), and derives its affine linear model to decoupled the internal and external loop. According to the dead zone and saturation characteristics of RCS thruster, a control method to convert continuous moment into discrete switching instruction using pulse width modulation (PWM) is proposed. Since the number of thrusters is usually redundant, the installation matrix of thrusters in the body coordinate is established, and the command moment is coordinately distributed to the individual thrusters. Then a double-loop sliding mode controller (DSMC) is designed to achieve attitude stability and trajectory tracking. Finally, the simulation results show that DSMC has higher maneuverability, fewer thruster switches and stronger robustness to interference

ηc\eta_c production at LHC and indications on the understanding of J/ψJ/\psi production

We present a complete evaluation for the prompt $\eta_c$ production at the LHC at next-to-leading order in $\alpha_s$ in nonrelativistic QCD. By assuming heavy quark spin symmetry, the recently observed $\eta_c$ production data by LHCb results in a very strong constraint on the upper bound of the color-octet long distance matrix element $1S0$ of $J/\psi$. We find this upper bound is consistent with our previous study of the $J/\psi$ yield and polarization and can give good descriptions for the measurements, but inconsistent with some other theoretical estimates. This may provide important information for understanding the nonrelativistic QCD factorization formulism.Comment: 5 pages, 2 figures, published version in PR

BMP signaling in the development and regeneration of tooth roots: from mechanisms to applications

Short root anomaly (SRA), along with caries, periodontitis, and trauma, can cause tooth loss, affecting the physical and mental health of patients. Dental implants have become widely utilized for tooth restoration; however, they exhibit certain limitations compared to natural tooth roots. Tissue engineering-mediated root regeneration offers a strategy to sustain a tooth with a physiologically more natural function by regenerating the bioengineered tooth root (bio-root) based on the bionic principle. While the process of tooth root development has been reported in previous studies, the specific molecular mechanisms remain unclear. The Bone Morphogenetic Proteins (BMPs) family is an essential factor regulating cellular activities and is involved in almost all tissue development. Recent studies have focused on exploring the mechanism of BMP signaling in tooth root development by using transgenic animal models and developing better tissue engineering strategies for bio-root regeneration. This article reviews the unique roles of BMP signaling in tooth root development and regeneration

Outracing Human Racers with Model-based Planning and Control for Time-trial Racing

Autonomous racing has become a popular sub-topic of autonomous driving in recent years. The goal of autonomous racing research is to develop software to control the vehicle at its limit of handling and achieve human-level racing performance. In this work, we investigate how to approach human expert-level racing performance with model-based planning and control methods using the high-fidelity racing simulator Gran Turismo Sport (GTS). GTS enables a unique opportunity for autonomous racing research, as many recordings of racing from highly skilled human players can served as expert emonstrations. By comparing the performance of the autonomous racing software with human experts, we better understand the performance gap of existing software and explore new methodologies in a principled manner. In particular, we focus on the commonly adopted model-based racing framework, consisting of an offline trajectory planner and an online Model Predictive Control-based (MPC) tracking controller. We thoroughly investigate the design challenges from three perspective, namely vehicle model, planning algorithm, and controller design, and propose novel solutions to improve the baseline approach toward human expert-level performance. We showed that the proposed control framework can achieve top 0.95% lap time among human-expert players in GTS. Furthermore, we conducted comprehensive ablation studies to validate the necessity of proposed modules, and pointed out potential future directions to reach human-best performance.Comment: 16 pages, 13 figures, 3 table

Federated Deep Multi-View Clustering with Global Self-Supervision

Federated multi-view clustering has the potential to learn a global clustering model from data distributed across multiple devices. In this setting, label information is unknown and data privacy must be preserved, leading to two major challenges. First, views on different clients often have feature heterogeneity, and mining their complementary cluster information is not trivial. Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data. To address these challenges, we propose a novel federated deep multi-view clustering method that can mine complementary cluster structures from multiple clients, while dealing with data incompleteness and privacy concerns. Specifically, in the server environment, we propose sample alignment and data extension techniques to explore the complementary cluster structures of multiple views. The server then distributes global prototypes and global pseudo-labels to each client as global self-supervised information. In the client environment, multiple clients use the global self-supervised information and deep autoencoders to learn view-specific cluster assignments and embedded features, which are then uploaded to the server for refining the global self-supervised information. Finally, the results of our extensive experiments demonstrate that our proposed method exhibits superior performance in addressing the challenges of incomplete multi-view data in distributed environments