Enhancing Control Performance through ESN-Based Model Compensation in MPC for Dynamic Systems

Deriving precise system dynamic models through traditional numerical methods is often a challenging endeavor. The performance of Model Predictive Control is heavily contingent on the accuracy of the system dynamic model. Consequently, this study employs Echo State Networks to acquire knowledge of the unmodeled dynamic characteristics inherent in the system. This information is then integrated with the nominal model, functioning as a form of model compensation. The present paper introduces a control framework that combines ESN with MPC. By perpetually assimilating the disparities between the nominal and real models, control performance experiences augmentation. In a demonstrative example, a second order dynamic system is subjected to simulation. The outcomes conclusively evince that ESNbased MPC adeptly assimilates unmodeled dynamic attributes, thereby elevating the system control proficiency.Comment: 5 pages,3 figures,conferenc

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page

Improving Image Captioning with Conditional Generative Adversarial Nets

In this paper, we propose a novel conditional-generative-adversarial-nets-based image captioning framework as an extension of traditional reinforcement-learning (RL)-based encoder-decoder architecture. To deal with the inconsistent evaluation problem among different objective language metrics, we are motivated to design some "discriminator" networks to automatically and progressively determine whether generated caption is human described or machine generated. Two kinds of discriminator architectures (CNN and RNN-based structures) are introduced since each has its own advantages. The proposed algorithm is generic so that it can enhance any existing RL-based image captioning framework and we show that the conventional RL training method is just a special case of our approach. Empirically, we show consistent improvements over all language evaluation metrics for different state-of-the-art image captioning models. In addition, the well-trained discriminators can also be viewed as objective image captioning evaluatorsComment: 12 pages; 33 figures; 36 refenences; Accepted by AAAI201

Image to Multi-Modal Retrieval for Industrial Scenarios

We formally define a novel valuable information retrieval task: image-to-multi-modal-retrieval (IMMR), where the query is an image and the doc is an entity with both image and textual description. IMMR task is valuable in various industrial application. We analyze three key challenges for IMMR: 1) skewed data and noisy label in metric learning, 2) multi-modality fusion, 3) effective and efficient training in large-scale industrial scenario. To tackle the above challenges, we propose a novel framework for IMMR task. Our framework consists of three components: 1) a novel data governance scheme coupled with a large-scale classification-based learning paradigm. 2) model architecture specially designed for multimodal learning, where the proposed concept-aware modality fusion module adaptively fuse image and text modality. 3. a hybrid parallel training approach for tackling large-scale training in industrial scenario. The proposed framework achieves SOTA performance on public datasets and has been deployed in a real-world industrial search system, leading to significant improvements in click-through rate and deal number. Code and data will be made publicly available

Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models

Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and delay, impede potentials in real-world scenarios. To achieve acceleration for VLMs, most existing methods focus on the model perspective: pruning, distillation, quantification, but completely overlook the data-perspective redundancy. To fill the overlook, this paper pioneers the severity of data redundancy, and designs one plug-and-play Turbo module guided by information degree to prune inefficient tokens from visual or textual data. In pursuit of efficiency-performance trade-offs, information degree takes two key factors into consideration: mutual redundancy and semantic value. Concretely, the former evaluates the data duplication between sequential tokens; while the latter evaluates each token by its contribution to the overall semantics. As a result, tokens with high information degree carry less redundancy and stronger semantics. For VLMs' calculation, Turbo works as a user-friendly plug-in that sorts data referring to information degree, utilizing only top-level ones to save costs. Its advantages are multifaceted, e.g., being generally compatible to various VLMs across understanding and generation, simple use without retraining and trivial engineering efforts. On multiple public VLMs benchmarks, we conduct extensive experiments to reveal the gratifying acceleration of Turbo, under negligible performance drop

Identification of Blue Horizontal-Branch Stars From LAMOST DR5

We construct a new catalog of the blue horizontal-branch (BHB) stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR5 dataset, which contains 5355+81 BHB stars at high Galactic latitude (($|Glat|>20^{\circ}$). We combine the spectral line indices with a set of Balmer line profile selection criteria to identify the BHB stars. During the selection process, we use the line index of \ion{Ca}{2}\,K to exclude the metal-rich A-type dwarfs. We obtain their atmospheric parameters by cross-matching our BHB stars with the catalog provided by \citet{Xiang2022}. The results show that our sample is consistent with the theoretical $T_{\rm eff}$-log\,$g$ evolutionary tracks of the BHB stars, indicating that our method is robust for identifying BHB stars from the LAMOST spectra. Their spatial distribution indicates that most of our BHB stars are located in the inner halo or the disk of the Milky Way. Combined with other BHB samples from the literature, the BHB stars can cover a large Galactic volume, which makes it a better probe for studying the kinematics, dynamics, and structural characteristics of the Milky Way.Comment: accepted by ApJS.15 pages, 18 figure

The Clumpy Structure Of Five Star-bursting Dwarf Galaxies In The MaNGA Survey

The star-forming clumps in star-bursting dwarf galaxies provide valuable insights into the understanding of the evolution of dwarf galaxies. In this paper, we focus on five star-bursting dwarf galaxies featuring off-centered clumps in the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Using the stellar population synthesis software FADO, we obtain the spatially-resolved distribution of the star formation history, which allows us to construct the $g$-band images of the five galaxies at different ages. These images can help us to probe the evolution of the morphological structures of these galaxies. While images of stellar population older than 1 Gyr are typically smooth, images of stellar population younger than 1 Gyr reveal significant clumps, including multiple clumps which appear at different locations and even different ages. To study the evolutionary connections of these five galaxies to other dwarf galaxies before their star-forming clumps appear, we construct the images of the stellar populations older than three age nodes, and define them to be the images of the "host" galaxies. We find that the properties such as the central surface brightness and the effective radii of the hosts of the five galaxies are in between those of dwarf ellipticals (dEs) and dwarf irregulars (dIrrs), with two clearly more similar to dEs and one more similar to dIrrs. Among the five galaxies, 8257-3704 is particularly interesting, as it shows a previous starburst event that is not quite visible from its $gri$ image, but only visible from images of the stellar population at a few hundred million years. The star-forming clump associated with this event may have appeared at around 600 Myr and disappeared at around 40 Myr.Comment: 21 pages, 16 figures, accepted for publication in RA