Centralized Coded Caching with User Cooperation

In this paper, we consider the coded-caching broadcast network with user cooperation, where a server connects with multiple users and the users can cooperate with each other through a cooperation network. We propose a centralized coded caching scheme based on a new deterministic placement strategy and a parallel delivery strategy. It is shown that the new scheme optimally allocate the communication loads on the server and users, obtaining cooperation gain and parallel gain that greatly reduces the transmission delay. Furthermore, we show that the number of users who parallelly send information should decrease when the users' caching size increases. In other words, letting more users parallelly send information could be harmful. Finally, we derive a constant multiplicative gap between the lower bound and upper bound on the transmission delay, which proves that our scheme is order optimal.Comment: 9 pages, submitted to ITW201

Fluorescence Accompanying Photoionization in Polyatomic Molecules and the Relationship to Photoelectron Motion: A Global View.

Measurements of dispersed fluorescence from electronically excited photoions are used to study fundamental molecular scattering phenomena. Our experimental advances make it possible to obtain vibrationally resolved data that offer qualitative insights into photoionization dynamics in polyatomics. In particular, vibrationally resolved data are measured to probe the correlation of electronic and nuclear degrees of freedom via studying the vibrational distributions of photoions and ionic fragments. Vibrational branching ratios are mapped out over an extremely broad energy range (\rm3\leq E\sb{k}\leq 200 eV) for N\sb2O (7\sigma\sp{-1}) ionization channel. The origin of the non-Franck-Condon behavior is tentatively interpreted as shape resonances and Cooper minima. The alternative vibrational mode, the bending degree of freedom, is studied for two isoelectronic molecules N\sb2O and CO\sb2. The results reveal very interesting information about the trapping site of shape resonant photoionization in a polyatomic molecule: bending motion may change the width of the box which traps the photoelectron. The multichannel aspect of shape resonances reflected in continuum channel coupling is investigated for CO\sb2 (\rm3\sigma\sb{u}\sp{-1}) channel. Current results are in very good agreements with calculations by Lucchese and McKoy. The dispersed fluorescence spectra of the fragments \rm N\sb2\sp+(B\sp2\Sigma\sb\mu\sp+,\ v\sp+ = 0) following dissociative photoionization of N\sb2O are measured in \rm20\leq h\nu\sb{exc}\leq150 eV. Characteristic features observed in the excitation spectrum of \rm N\sb2\sp+(B\sp2\Sigma\sb\mu\sp+) are discussed with a focus on (pre)dissociation of (1) singly ionized parent states; (2) multielectron excitation states commonly observed in the inner-valence regions; (3) doubly ionized parent states. The multielectron excitation states and doubly ionized parent states are responsible for most of the dissociative ionization that occurs in N\sb2O above $\sim$22 eV. Strongly dissociative nature of doubly ionized states correlating with the single-hole 4$\sigma$ shape resonance in inner-valence region is probably responsible for the broad enhancement observed in the CIS curve. The dispersed fluorescence provides information about the internal energy distributions of the fragments and, as a consequence, leads to insights into the dissociation dynamics

Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems

Schr\"odinger's equation serves as a fundamental component in characterizing quantum systems, wherein both quantum state tomography and Hamiltonian learning are instrumental in comprehending and interpreting quantum systems. While numerous techniques exist for carrying out state tomography and learning Hamiltonians individually, no method has been developed to combine these two aspects. In this study, we introduce a new approach that employs the attention mechanism in transformer models to effectively merge quantum state tomography and Hamiltonian learning. By carefully choosing and preparing the training data, our method integrates both tasks without altering the model's architecture, allowing the model to effectively learn the intricate relationships between quantum states and Hamiltonian. We also demonstrate the effectiveness of our approach across various quantum systems, ranging from simple 2-qubit cases to more involved 2D antiferromagnetic Heisenberg structures. The data collection process is streamlined, as it only necessitates a one-way generation process beginning with state tomography. Furthermore, the scalability and few-shot learning capabilities of our method could potentially minimize the resources required for characterizing and optimizing quantum systems. Our research provides valuable insights into the relationship between Hamiltonian structure and quantum system behavior, fostering opportunities for additional studies on quantum systems and the advancement of quantum computation and associated technologies.Comment: 15 pages, 10 figure

Coordinated optimal control of secondary cooling and final electromagnetic stirring for continuous casting billets

Secondary cooling and final electromagnetic stirring (F-EMS) are both key technologies for continuous casting. These parameters are usually optimized and controlled separately which caused internal quality fluctuations in unsteady conditions. In this paper, a coordinated optimal control strategy based on a multiobjective particle swarm optimization (MOPSO) algorithm is proposed for the parameter optimization of secondary cooling and F-EMS, which is solved based on multiobjective particle swarm optimization (MOPSO) algorithm. The solidification and heat transfer model are developed for the computation of billet temperature and the solidification, and the adaptive grid method is used to improve the diversity and robustness of optimal solutions. The secondary cooling water and F-EMS’ stirring current are dynamically controlled based on the optimization results. The results of field trials showed that the maximum carbon segregation and other quality indexes of billets can be improved significantly

MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds

3D semantic segmentation on multi-scan large-scale point clouds plays an important role in autonomous systems. Unlike the single-scan-based semantic segmentation task, this task requires distinguishing the motion states of points in addition to their semantic categories. However, methods designed for single-scan-based segmentation tasks perform poorly on the multi-scan task due to the lacking of an effective way to integrate temporal information. We propose MarS3D, a plug-and-play motion-aware module for semantic segmentation on multi-scan 3D point clouds. This module can be flexibly combined with single-scan models to allow them to have multi-scan perception abilities. The model encompasses two key designs: the Cross-Frame Feature Embedding module for enriching representation learning and the Motion-Aware Feature Learning module for enhancing motion awareness. Extensive experiments show that MarS3D can improve the performance of the baseline model by a large margin. The code is available at https://github.com/CVMI-Lab/MarS3D

Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations

Audio-driven talking face generation, which aims to synthesize talking faces with realistic facial animations (including accurate lip movements, vivid facial expression details and natural head poses) corresponding to the audio, has achieved rapid progress in recent years. However, most existing work focuses on generating lip movements only without handling the closely correlated facial expressions, which degrades the realism of the generated faces greatly. This paper presents DIRFA, a novel method that can generate talking faces with diverse yet realistic facial animations from the same driving audio. To accommodate fair variation of plausible facial animations for the same audio, we design a transformer-based probabilistic mapping network that can model the variational facial animation distribution conditioned upon the input audio and autoregressively convert the audio signals into a facial animation sequence. In addition, we introduce a temporally-biased mask into the mapping network, which allows to model the temporal dependency of facial animations and produce temporally smooth facial animation sequence. With the generated facial animation sequence and a source image, photo-realistic talking faces can be synthesized with a generic generation network. Extensive experiments show that DIRFA can generate talking faces with realistic facial animations effectively