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.
A distributed system keeps consistency by disallowing data anomalies. However, especially in the database, the definitions of data anomalies in the current ANSI standard are controversial. The standard does not include all anomalies and does not intr
oduce characters of anomalies. First, the definitions lack a mathematical formalization and cause ambiguous interpretations. Second, the definitions of anomalies are case-by-case, which could not have a comprehensive understanding of data anomalies. In this paper, we propose a ring anomalies detection method (the bingo model) in the distribution system and applying it to databases. The bingo model introduces anomalies construction and gives the base anomalies formalization method. Based on anomalies we propose consistency levels. We prove the simplified anomaly rings in the model to classified anomalies to give the independent consistency levels. We specify the bingo model to databases and find 22 anomalies in addition to existing anomalies.
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data. Despite its practical efficiency and
effectiveness, the iterative on-device learning process (e.g., local computations and global communications with the server) incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL in mobile edge networks that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. We establish the analytical relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different optimization metrics. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for different optimization metrics for various datasets and heterogeneous system and statistical settings.
For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during
training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration. Against these issues, we propose a novel interpretable dual domain network, termed as InDuDoNet, which combines the advantages of model-driven and data-driven methodologies. Specifically, we build a joint spatial and Radon domain reconstruction model and utilize the proximal gradient technique to design an iterative algorithm for solving it. The optimization algorithm only consists of simple computational operators, which facilitate us to correspondingly unfold iterative steps into network modules and thus improve the interpretablility of the framework. Extensive experiments on synthesized and clinical data show the superiority of our InDuDoNet. Code is available in url{https://github.com/hongwang01/InDuDoNet}.%method on the tasks of MAR and downstream multi-class pelvic fracture segmentation.
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.
The kagome superconductor AV$_3$Sb$_5$ (A=K, Rb, Cs) is a rare platform to explore the interplay between topology, geometrical frustration and symmetry-breaking orders. In addition to the charge density wave below $T_{CDW}sim94$~K and superconductivi
ty below $T_{SC}sim3$~K, recent surface sensitive studies of CsV$_3$Sb$_5$ find evidence of 1$times$4 superlattices below $T^{*}sim60$~K. Interestingly, this unidirectional $4a_0$ phase may intertwine with charge density wave and superconductivity and possibly responsible for spectroscopic and transport anomalies below $T^{*}$. Here, combining high-resolution X-ray diffraction and scanning tunneling microscopy, we demonstrate that the 1$times$4 superstructure emerges uniquely on the surface and hence exclude the $4a_0$ phase as the origin of $T^{*}$ anomaly in the bulk CsV$_3$Sb$_5$.
Formation and collision avoidance abilities are essential for multi-agent systems. Conventional methods usually require a central controller and global information to achieve collaboration, which is impractical in an unknown environment. In this pape
r, we propose a deep reinforcement learning (DRL) based distributed formation control scheme for autonomous vehicles. A modified stream-based obstacle avoidance method is applied to smoothen the optimal trajectory, and onboard sensors such as Lidar and antenna arrays are used to obtain local relative distance and angle information. The proposed scheme obtains a scalable distributed control policy which jointly optimizes formation tracking error and average collision rate with local observations. Simulation results demonstrate that our method outperforms two other state-of-the-art algorithms on maintaining formation and collision avoidance.
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.
