In this work, we predict the spectroscopy behavior of these light unflavor vector mesons with masses at the range of $2.4sim 3$ GeV, which are still missing in experiment. By presenting their mass spectrum and studying their two-body Okubo-Zweig-lizu
ka allowed decay widths, we discuss the possible experimental evidences of these discussed states combing with the present experimental data. Especially, we strongly suggest our experimental colleague to carry out the exploration of these higher states via the $e^+e^-$ annihilation into light mesons. It is obvious that BESIII and Belle II will be potential experiment to achieve this target.
Myopia is an eye condition that makes it difficult for people to focus on faraway objects. It has become one of the most serious eye conditions worldwide and negatively impacts the quality of life of those who suffer from it. Although myopia is preva
lent, many non-myopic people have misconceptions about it and encounter challenges empathizing with myopia situations and those who suffer from it. In this research, we developed two virtual reality (VR) games, (1) Myopic Bike and (2) Say Hi, to provide a means for the non-myopic population to experience the frustration and difficulties of myopic people. Our two games simulate two inconvenient daily life scenarios (riding a bicycle and greeting someone on the street) that myopic people encounter when not wearing glasses. We evaluated four participants game experiences through questionnaires and semi-structured interviews. Overall, our two VR games can create an engaging and non-judgmental experience for the non-myopic population to better understand and empathize with those who suffer from myopia.
Cross-speaker style transfer (CSST) in text-to-speech (TTS) synthesis aims at transferring a speaking style to the synthesised speech in a target speakers voice. Most previous CSST approaches rely on expensive high-quality data carrying desired speak
ing style during training and require a reference utterance to obtain speaking style descriptors as conditioning on the generation of a new sentence. This work presents Referee, a robust reference-free CSST approach for expressive TTS, which fully leverages low-quality data to learn speaking styles from text. Referee is built by cascading a text-to-style (T2S) model with a style-to-wave (S2W) model. Phonetic PosteriorGram (PPG), phoneme-level pitch and energy contours are adopted as fine-grained speaking style descriptors, which are predicted from text using the T2S model. A novel pretrain-refinement method is adopted to learn a robust T2S model by only using readily accessible low-quality data. The S2W model is trained with high-quality target data, which is adopted to effectively aggregate style descriptors and generate high-fidelity speech in the target speakers voice. Experimental results are presented, showing that Referee outperforms a global-style-token (GST)-based baseline approach in CSST.
Federated Learning (FL) makes a large amount of edge computing devices (e.g., mobile phones) jointly learn a global model without data sharing. In FL, data are generated in a decentralized manner with high heterogeneity. This paper studies how to per
form statistical estimation and inference in the federated setting. We analyze the so-called Local SGD, a multi-round estimation procedure that uses intermittent communication to improve communication efficiency. We first establish a {it functional central limit theorem} that shows the averaged iterates of Local SGD weakly converge to a rescaled Brownian motion. We next provide two iterative inference methods: the {it plug-in} and the {it random scaling}. Random scaling constructs an asymptotically pivotal statistic for inference by using the information along the whole Local SGD path. Both the methods are communication efficient and applicable to online data. Our theoretical and empirical results show that Local SGD simultaneously achieves both statistical efficiency and communication efficiency.
In this paper, we consider the design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In addition to a power constraint, we require the co
variance of the transmit waveform be equal to a given optimal covariance for MIMO radar, to guarantee the radar performance. With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization. Considering that the interference cannot be completely eliminated with this constraint, we introduce dirty paper coding (DPC) to further cancel the interference, and formulate the SINR balancing and sum rate maximization problem in the DPC regime. Although both of the two problems are non-convex, we show that they can be reformulated to convex optimizations via the Lagrange and downlink-uplink duality. In addition, we propose gradient projection based algorithms to solve the equivalent dual problem of SINR balancing, in both transmit beamforming and DPC regimes. The simulation results demonstrate significant performance improvement of DPC over transmit beamforming, and also indicate that the degrees of freedom for the communication transmitter is restricted by the rank of the covariance.
Deep learning techniques have opened a new venue for electronic structure theory in recent years. In contrast to traditional methods, deep neural networks provide much more expressive and flexible wave function ansatz, resulting in better accuracy an
d time scaling behavior. In order to study larger systems while retaining sufficient accuracy, we integrate a powerful neural-network based model (FermiNet) with the effective core potential method, which helps to reduce the complexity of the problem by replacing inner core electrons with additional semi-local potential terms in Hamiltonian. In this work, we calculate the ground state energy of 3d transition metal atoms and their monoxide which are quite challenging for original FermiNet work, and the results are in good consistency with both experimental data and other state-of-the-art computational methods. Our development is an important step for a broader application of deep learning in the electronic structure calculation of molecules and materials.
Stimulated by the newly reported doubly-charmed tetraquark state $T_{cc}^+$ by LHCb, we carry out a systematic investigation of the $S$-wave interactions between the charmed meson ($D,,D^{*}$) in $H$-doublet and the charmed meson ($D_{1},,D_{2}^{*}$)
in $T$-doublet by adopting the one-boson-exchange model. Both the $S$-$D$ wave mixing effect and the coupled channel effect are taken into account. By performing a quantitative calculation, we suggest that the $S$-wave $D^{*} D_{1}$ states with $I(J^{P})=0(0^{-},,1^{-})$ and the $S$-wave $D^{*}D_{2}^{*}$ state with $I(J^{P})=0(1^{-})$ should be viewed as the most promising candidates of the doubly-charmed molecular tetraquark states, and the $S$-wave $DD_{1}$ state with $I(J^{P})=0(1^{-})$, the $S$-wave $DD_{2}^{*}$ state with $I(J^{P})=0(2^{-})$, and the $S$-wave $D^{*}D_{2}^{*}$ state with $I(J^{P})=0(2^{-})$ are the possible doubly-charmed molecular tetraquark candidates. With the accumulation of experimental data at Run III and after High-Luminosity-LHC upgrade, these predicted doubly-charmed molecular tetraquark states can be accessible at LHCb in the near future.
Extensions of Hubers finite-point conformal compactification theorem to higher dimensions with $L^frac{n}{2}$ bounded scalar curvature have been studied for many years. In this paper, we discuss the properties of conformal metrics with $|R|_{L^frac{n
}{2}}<+infty$ on a punctured ball of a Riemannian manifold to find some geometric obstacles for Hubers theorem. To our surprise, such metrics are rather more rigid than we have ever thought. For example, their volume densities at infinity are exact 1, which implies that Carron and Herzlichs Euclidean volume growth condition is also a necessary condition for Hubers Theorem. When the dimension is 4, we derive the $L^2$-integrability of Ricci curvature, which follows that the Pfaffian of the curvature is integrable and satisfies a Gauss-Bonnet-Chern formula. We also prove that the Gauss-Bonnet-Chern formula proved by Lu and Wang, under the assumption that the second fundamental form is in $L^4$, holds when $Rin L^2$.
How to hunt for higher $P$-wave states of charmonium is still an open topic when $2P$ charmonia were identified. In this work, we present an unquenched quark model calculation to illustrate the spectroscopy behavior of these discussed higher $P$-wave
charmonia. For the $3P$ charmonia, the predicted masses are around 4.2 GeV and their two-body open-charm decay behaviors were given, by which we propose that searching for these $3P$ states via their open-charm decay channels from $gammagamma$ fusion and $B$ decay can be accessible at future experiment like LHCb and Belle II. We continue to calculate the masses of these $4P$ and $5P$ charmonia. Combing with these calculated results of higher $P$-wave states of charmonium, we find that the coupled-channel effect becomes more obvious with increasing the radial quantum number, which can be understood well by the modified Godfrey-Isgur model with screened potential.
We investigate the long-time properties of a dynamic, out-of-equilibrium, network of individuals holding one of two opinions in a population consisting of two communities of different sizes. Here, while the agents opinions are fixed, they have a pref
erred degree which leads them to endlessly create and delete links. Our evolving network is shaped by homophily/heterophily, which is a form of social interaction by which individuals tend to establish links with others having similar/dissimilar opinions. Using Monte Carlo simulations and a detailed mean-field analysis, we study in detail how the sizes of the communities and the degree of homophily/heterophily affects the network structure. In particular, we show that when the network is subject to enough heterophily, an overwhelming transition occurs: individuals of the smaller community are overwhelmed by links from agents of the larger group, and their mean degree greatly exceeds the preferred degree. This and related phenomena are characterized by obtaining the networks total and joint degree distributions, as well as the fraction of links across both communities and that of agents having less edges than the preferred degree. We use our mean-field theory to discuss the networks polarization when the group sizes and level of homophily vary.
