Maximum Principle for Control System driven by Mixed Fractional Brownian Motion

In this paper, we study the optimal control problem for system driven by mixed fractional Brownian motion (including a fractional Brownian motion with Hurst parameter $H>1/2$ and the underlying standard Brownian motion). By using Malliavin calculus, we obtain the necessary condition the optimal control should satisfy. Through martingale representation theorem and the properties of the transforms operator, we give out the adjoint backward stochastic differential equation in a natural way. As a straightforward consequence, the maximum principle for control system driven by fractional Brownian motion and an independent Brownian motion is also deduced, which is different to the underlying case. As an application, the linear quadratic case is investigated to illustrate the main results

Can Far-field Beam Training Be Deployed for Cross-field Beam Alignment in Terahertz UM-MIMO Communications?

Ultra-massive multiple-input multiple-output (UM-MIMO) is the enabler of Terahertz (THz) communications in next-generation wireless networks. In THz UM-MIMO systems, a new paradigm of cross-field communications spanning from near-field to far-field is emerging, since the near-field range expands with higher frequencies and larger array apertures. Precise beam alignment in cross-field is critical but challenging. Specifically, unlike far-field beams that rely only on the angle domain, the incorporation of dual-domain (angle and distance) training significantly increases overhead. A natural question arises of whether far-field beam training can be deployed for cross-field beam alignment. In this paper, this question is answered, by demonstrating that the far-field training enables sufficient signal-to-noise ratio (SNR) in both far- and near-field scenarios, while exciting all channel dimensions. Based on that, we propose a subarray-coordinated hierarchical (SCH) training with greatly reduced overhead. To further obtain high-precision beam designs, we propose a two-phase angle and distance beam estimator (TPBE). Extensive simulations demonstrate the effectiveness of the proposed methods. Compared to near-field exhaustive search, the SCH possesses 0.2\% training overhead. The TPBE achieves 0.01~degrees and 0.02~m estimation root-mean-squared errors for angle and distance. Furthermore, with the estimated beam directions, a near-optimal SNR with 0.11~dB deviation is attained after beam alignment

RESEARCH ON THE MOTION RESPONSE OF AQUACULTURE SHIP AND TANK SLOSHING UNDER ROLLING RESONANCE

The double-row and double-chamfered aquaculture tank is a special tank structure of the aquaculture ship. The tank sloshing of this structure is coupled with the hull motion, which has an important impact on the safety of the hull motion. In the present study, research on the tank sloshing and hull motion response of aquaculture ships was conducted based on the model seakeeping and tank sloshing tests in regular waves. The test results were compared with the numerical simulation results of solid loading without sloshing. The results showed that the numerical simulation of the pitch motion was consistent with the amplitude-frequency response curve of the experimental results. Under certain transverse wave conditions, a large discrepancy existed between the amplitude-frequency response curve of the heave motion by the numerical simulation and the test results, and the roll motion differed most from the experimental result. Severe roll resonance occurred when the wave length-ship length ratio was 0.6. The roll motion amplitude was increased by 183.2%. Therefore, compared with aquaculture ships without sloshing, the sloshing of the tank has little effect on the pitch but has a great impact on the roll and heave motions, with the most significant effect on the roll motion

Ergodic Achievable Rate Analysis and Optimization of RIS-assisted Millimeter-Wave MIMO Communication Systems

Reconfigurable intelligent surfaces (RISs) have emerged as a prospective technology for next-generation wireless networks due to their potential in coverage and capacity enhancement. Previous works on achievable rate analysis of RIS-assisted communication systems have mainly focused on the rich-scattering environment where Rayleigh and Rician channel models can be applied. This work studies the ergodic achievable rate of RIS-assisted multiple-input multiple-output communication systems in millimeter-wave band with limited scattering under the Saleh-Valenzuela channel model. Firstly, we derive an upper bound of the ergodic achievable rate by means of majorization theory and Jensen's inequality. The upper bound shows that the ergodic achievable rate increases logarithmically with the number of antennas at the base station (BS) and user, the number of the reflection units at the RIS, and the eigenvalues of the steering matrices associated with the BS, user and RIS. Then, we aim to maximize the ergodic achievable rate by jointly optimizing the transmit covariance matrix at the BS and the reflection coefficients at the RIS. Specifically, the transmit covariance matrix is optimized by the water-filling algorithm and the reflection coefficients are optimized using the Riemannian conjugate gradient algorithm. Simulation results validate the effectiveness of the proposed optimization algorithms.Comment: 30 pages, 11 figure

Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations

With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences. Although deep neural networks (DNNs) have made significant progress in improving recommendation systems by simulating the interaction between users and items and incorporating their textual information, these DNN-based approaches still have some limitations, such as the difficulty of effectively understanding users' interests and capturing textual information. It is not possible to generalize to different seen/unseen recommendation scenarios and reason about their predictions. At the same time, the emergence of large language models (LLMs), represented by ChatGPT and GPT-4, has revolutionized the fields of natural language processing (NLP) and artificial intelligence (AI) due to their superior capabilities in the basic tasks of language understanding and generation, and their impressive generalization and reasoning capabilities. As a result, recent research has sought to harness the power of LLM to improve recommendation systems. Given the rapid development of this research direction in the field of recommendation systems, there is an urgent need for a systematic review of existing LLM-driven recommendation systems for researchers and practitioners in related fields to gain insight into. More specifically, we first introduced a representative approach to learning user and item representations using LLM as a feature encoder. We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems from the three paradigms of pre-training, fine-tuning, and prompting. Finally, we had a comprehensive discussion on the future direction of this emerging field

Contextual Object Detection with Multimodal Large Language Models

Recent Multimodal Large Language Models (MLLMs) are remarkable in vision-language tasks, such as image captioning and question answering, but lack the essential perception ability, i.e., object detection. In this work, we address this limitation by introducing a novel research problem of contextual object detection -- understanding visible objects within different human-AI interactive contexts. Three representative scenarios are investigated, including the language cloze test, visual captioning, and question answering. Moreover, we present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts, so as to locate, identify, and associate visual objects with language inputs for human-AI interaction. Our ContextDET involves three key submodels: (i) a visual encoder for extracting visual representations, (ii) a pre-trained LLM for multimodal context decoding, and (iii) a visual decoder for predicting bounding boxes given contextual object words. The new generate-then-detect framework enables us to detect object words within human vocabulary. Extensive experiments show the advantages of ContextDET on our proposed CODE benchmark, open-vocabulary detection, and referring image segmentation. Github: https://github.com/yuhangzang/ContextDET.Comment: Github: https://github.com/yuhangzang/ContextDET, Project Page: https://www.mmlab-ntu.com/project/contextdet/index.htm

Multi-label prediction method for lithology, lithofacies and fluid classes based on data augmentation by cascade forest

Predicting the lithology, lithofacies and reservoir fluid classes of igneous rocks holds significant value in the domains of CO2 storage and reservoir evaluation. However, no precedent exists for research on the multi-label identification of igneous rocks. This study proposes a multi-label data augmented cascade forest method for the prediction of multilabel lithology, lithofacies and fluid using 9 conventional logging data features of cores collected from the eastern depression of the Liaohe Basin in northeastern China. Data augmentation is performed on an unbalanced multi-label training set using the multi-label synthetic minority over-sampling technique. Sample training is achieved by a multi-label cascade forest consisting of predictive clustering trees. These cascade structures possess adaptive feature selection and layer growth mechanisms. Given the necessity to focus on all possible outcomes and the generalization ability of the method, a simulated well model is built and then compared with 6 typical multi-label learning methods. The outperformance of this method in the evaluation metrics validates its superiority in terms of accuracy and generalization ability. The consistency of the predicted results and geological data of actual wells verifies the reliability of our method. Furthermore, the results show that it can be used as a reliable means of multi-label prediction of igneous lithology, lithofacies and reservoir fluids.Document Type: Original articleCited as: Han, R., Wang, Z., Guo, Y., Wang, X., A, R., Zhong, G. Multi-label prediction method for lithology, lithofacies and fluid classes based on data augmentation by cascade forest. Advances in Geo-Energy Research, 2023, 9(1): 25-37. https://doi.org/10.46690/ager.2023.07.0

Control water waves by metagratings

Metasurfaces and metagratings offers new platforms for electromagnetic wave control with significant responses. However, metasurfaces based on abrupt phase change and resonant structures suffer from the drawback of high loss and face challenges when applied in water waves. Therefore, the application of metasurfaces in water wave control is not ideal due to the limitations associated with high loss and other challenges. We have discovered that non-resonant metagratings exhibit promising effects in water wave control. Leveraging the similarity between bridges and metagratings, we have successfully developed a water wave metagrating model inspired by the Luoyang Bridge in ancient China. We conducted theoretical calculations and simulations on the metagrating and derived the equivalent anisotropic model of the metagrating. This model provides evidence that the metagrating has the capability to control water waves and achieve unidirectional surface water wave. The accuracy of our theory is strongly supported by the clear observation of the unidirectional propagation phenomenon during simulation and experiments conducted using a reduced version of the metagrating. It is the first time that the unidirectional propagation of water waves has been seen in water wave metagrating experiment. Above all, we realize the water wave metagrating experiment for the first time. By combining complex gratings with real bridges, we explore the physics embedded in the ancient building-Luoyang Bridge, which are of great significance for the water wave metagrating design, as well as the development and preservation of ancient bridges.Comment: 25 pages, 13 figure

FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System

Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated local models on the server may reveal sensitive personal information by inversion attacks. Privacy-preserving methods, such as homomorphic encryption (HE), then become necessary for FL training. Despite HE's privacy advantages, its applications suffer from impractical overheads, especially for foundation models. In this paper, we present FedML-HE, the first practical federated learning system with efficient HE-based secure model aggregation. FedML-HE proposes to selectively encrypt sensitive parameters, significantly reducing both computation and communication overheads during training while providing customizable privacy preservation. Our optimized system demonstrates considerable overhead reduction, particularly for large foundation models (e.g., ~10x reduction for ResNet-50, and up to ~40x reduction for BERT), demonstrating the potential for scalable HE-based FL deployment