The pinching-antenna system (PASS) reconfigures wireless links through pinching beamforming, in which the activated locations of pinching antennas (PAs) along dielectric waveguides are optimized. This article investigates the application of PASS in multicast communication systems, where pinching beamforming is designed to maximize the multicast rate. i) In the single-waveguide scenario, a closed-form solution for the optimal activated location is derived under the assumption of a single PA and linearly distributed users. Based on this, a closed-form expression for the achievable multicast rate is obtained and proven to be larger than that of conventional fixed-location antenna systems. For the general multiple-PA case with arbitrary user distributions, an element-wise alternating optimization (AO) algorithm is proposed to design the pinching beamformer. ii) In the multiple-waveguide scenario, an AO-based method is developed to jointly optimize the transmit and pinching beamformers. Specifically, the transmit beamformer is updated using a majorization-minimization (MM) framework together with second-order cone programming (SOCP), while the pinching beamformer is optimized via element-wise sequential refinement. Numerical results are provided to demonstrate that: i) PASS achieves significantly higher multicast rates than conventional fixed-location antenna systems, particularly when the number of users and spatial coverage increase; ii) increasing the number of PAs further improves the multicast performance of PASS.

This article addresses the design of multigroup multicast communications in the pinching-antenna system (PASS). A PASS-enabled multigroup transmission framework is proposed to maximize multicast rates under a couple of transmission architectures: waveguide-division (WD) and waveguide-multiplexing (WM). 1) For WD, an element-wise sequential optimization strategy is proposed forpinching beamforming, i.e., optimizing the activated positions of pinching antennas along dielectric waveguides. Meanwhile, a log-sum-exp projected gradient descent algorithm is proposed for transmit power allocation across waveguides. 2) For WM, a majorization-minimization (MM)-based framework is proposed to tackle the problem’s non-smoothness and non-convexity. On this basis, a low-complexity element-wise sequential optimization method is developed for pinching beamforming using the MM surrogate objective. Furthermore, the optimal transmit beamformer structure is derived from the MM surrogate objective using the Lagrange duality, with an efficient transmit beamforming algorithm proposed using projected adaptive gradient descent. Numerical results demonstrate that: i) both WD and WM architectures in PASS achieve significant multicast rate improvements over conventional MIMO techniques, especially for systems with large service areas; ii) WM is more robust than WD in dense deployments, while WD excels when user groups are spatially separated.

This article investigates secure multicast communications in pinching-antenna systems (PASS), where pinching beamforming is enabled by adaptively adjusting pinching antenna (PAs) positions along waveguides to improve multicast security. Specifically, a PASS-based secure multicast framework is proposed, in which joint optimization of transmit and pinching beamforming is conducted to maximize the secrecy multicast rate. i) For the single-group multicast scenario, an alternating optimization (AO) framework is employed, where the pinching beamformer is updated via an element-wise sequential optimization method. The transmit beamformer is designed via a semidefinite relaxation (SDR) formulation for an upper-bound solution, while a Dinkelbach-alternating direction method of multipliers (ADMM) offers a low-complexity alternative. ii) For the multi-group multicast scenario, transmit and pinching beamformers are alternately optimized under a majorization-minimization (MM) framework. The transmit beamformer is obtained via SDR or an efficient second-order cone programming (SOCP) method, while the pinching beamformer is updated through MM-based element-wise sequential update strategy. Numerical results are provided to demonstrate that: (i) PASS consistently outperform conventional fixed-location antenna architectures in terms of secrecy performance across various configurations; and (ii) the performance advantage of PASS over fixed-location architectures becomes more significant with increased service region, larger antenna arrays, and higher user and eavesdropper densities.

This paper studies the application of pinching-antenna systems (PASS) in multicast communications. The minimum-rate maximization problem is formulated, and both single-pinching antenna (PA) and multiple-PA deployment over a single waveguide are considered. 1) For the single-PA scenario, a closed-form solution is derived for one-dimensional user distributions, and a candidate-point search strategy is proposed for arbitrary user placements. 2) For the multiple-PA scenario, the pinching beamforming is optimized through a low-complexity greedy search approach. Numerical results demonstrate that: i) PASS achieves a higher multicast rate compared to conventional fixed-antenna systems; ii) the proposed approaches substantially reduce computational complexity compared to traditional exhaustive search algorithms.

Ultra-massive multiple-input multiple-output (UM-MIMO) systems with hybrid beamforming architectures are extensively studied to enhance the capacity of future wireless networks. As the number of antennas increases, the near-field property of UM-MIMO systems becomes significant. Unlike the classical far-field line-of-sight (LoS) channel with single available data stream, the near-field LoS channel offers a significantly increased spatial degrees-of-freedom (DoFs) to enable the LoS spatial multiplexing. However, most of the existing literature considered single-user transmission scenarios. Hence, we focus our attention on the more challenging design of hybrid beam-forming for multi-user UM-MIMO systems by leveraging the extra spatial DoFs available in the near-field region. Specifically, the available spatial DoFs and the capacity upper bound of the near-field LoS channel are theoretically analyzed at first. Then, to exploit the near-field property as a new possibility for capacity improvement, a block diagonal geometric mean decomposition (BD-GMD) multi-user near-field hybrid beamforming algorithm is proposed. The key idea of our approach is to allocate specific subarrays for different users to optimize their channel eigenvalue distribution, thereby maximizing multi-user spatial multiplexing performance as well as minimizing inter-user interference. Simulation results demonstrate that the proposed BD-GMD scheme achieves a significant capacity improvement compared to conventional approaches.

Apparent first impression prediction has made great progress with deep neural networks. There is a trend for multimodal fusion where features from different sources are fused together to improve the accuracy of the prediction. However, in a real-life scenario, it is often hard to gather features from different sources such as audio and background information. It is desirable to develop a method that could improve the prediction accuracy from a single source rather than multiple sources. This study developed a method to predict personality traits from a single source of information, i.e., facial information. Specifically, a pre-trained Deep Convolutional Neural Network was employed to extract emotional expression frame by frame in the video clip, which was then fed into a Long Short Term Memory model to predict the “Big Five” personality traits score. In Parallel, the model based on the static apparent facial features was trained, and finally, the facial feature and facial expression were fused with demographic data (age and gender). The proposed system is tested on the CharLearn Dataset and achieved an accuracy score of 90.67% ranked just below the top 5 CharLearn Competition. The result also showed that the dynamic emotional pattern has a positive impact on first impression prediction, especially on extraversion.

Ultra-massive multiple-input multiple-output (UM-MIMO) has been considered as one of the promising technologies for terahertz (THz) wireless communications to compensate for the severe path loss. However, the widely acknowledged hybrid beamforming approaches in massive MIMO cannot deal with the beam split effect caused by the increased scale of array dimension and system bandwidth in THz UM-MIMO systems. In this paper, the hybrid beamforming specifically designed for wideband THz UM-MIMO systems with the beam split effect is proposed. Firstly, a novel technique of hybrid beamforming is formulated as a sub-beam coherent combination method to cover the dispersed spatial directions. Subsequently, the dynamic hybrid hardware architecture is considered to flexibly adapt to various beam splits on different path directions, in which the optimal number of the activated subarray is elaborately designed to alleviate beam split while reducing power consumption. Simulation results indicate that our scheme achieves desirable beamforming gain distribution across the entire bandwidth, as well as achieving higher energy efficiency than other fixed hybrid hardware architectures designed for alleviating the beam split effect.

BACKGROUND: The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. RESULTS: Prediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can't classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. CONCLUSIONS: Our study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Channel state information (CSI) is crucial to achieve the promising gain in millimeter-wave (mmWave) wideband system. Several existing wideband channel estimation schemes only address the frequency-wideband effect, assuming common sparsity supports across subcarriers yet ignoring the spatial-wideband effect, where beams are split into different physical directions at different frequencies. Considering spatial-wideband effect, the beam split pattern (BSP), a set which contains different spatial directions for different subcarriers, can be exploited to perform BSP detection (BSPD) based channel estimation scheme. However, the straightforward BSPD scheme shows poor accuracy of channel estimation due to the limited angle-domain resolution. In this paper, we propose two refined channel estimation schemes for more accurate CSI acquisition. Firstly, direct refinement of BSPD (DR-BSPD) based channel estimation is developed through explicit angle-domain resolution enhancement. Secondly, multiple-subcarrier aided BSPD (MSA-BSPD) based channel estimation is proposed to implicitly refine the physical channel direction through exploiting the property that multiple subcarriers with different array gains are matched to one angle-domain sample. Numerical results demonstrate the superior performance of the two proposed schemes against the existing BSPD based channel estimation scheme.