This article studies the cell-free massive multi-input multi-output (MIMO) two-way relay systems with low-resolution analog-to-digital converters (ADCs). Primarily, we analyze the normalized mean square error (NMSE) and derive a closed-form expression for NMSE’s expectation${\mathrm {Exp}}_{\mathrm {nmse}}$. Asymptotic analysis showcases that, with an infinite number of access point (AP) antennas M or ideal ADCs,${\mathrm {Exp}}_{\mathrm {nmse}}$converges to a finite value. Particularly, as pilot power decreases with M in a power law, the channel estimation performance can be maintained at a desired level. Secondly, we derive two closed-form expressions of spectral efficiencies (SE) for multiple-access channel (MAC) phase and broadcasting (BC) phases, respectively. The corresponding analysis implies that, as AP number$L\rightarrow \infty $, the SE tends to infinity with MAC phase and approaches to constant with BC phase. Interestingly, all SE expressions can reduce to the conventional cases with high-resolution ADCs. Finally, based on geometric programming, an effective power successive approximation (PSA) scheme is provided to maximize the sum SE. Results prove that the proposed PSA scheme can significantly improve the SE compared to baseline benchmarks, particularly for median AP antenna regime. All the derived closed-form expressions are validated through Monte Carlo simulations.

Abstract Studying representation learning and generative modelling has been at the core of the 3D learning domain. By leveraging the generative adversarial networks and convolutional neural networks for point‐cloud representations, we propose a novel framework, which can directly generate 3D objects represented by point clouds. The novelties of the proposed method are threefold. First, the generative adversarial networks are applied to 3D object generation in the point‐cloud space, where the model learns object representation from point clouds independently. In this work, we propose a 3D spatial transformer network, and integrate it into a generation model, whose ability for extracting and reconstructing features for 3D objects can be improved. Second, a point‐wise approach is developed to reduce the computational complexity of the proposed network. Third, an evaluation system is proposed to measure the performance of our model by employing various categories and methods, and the error, considered as the difference between synthesized objects and raw objects are quantitatively compared, is less than 2.8%. Extensive experiments on benchmark dataset show that this method has a strong ability to generate 3D objects in the point‐cloud space, and the synthesized objects have slight differences with man‐made 3D objects.

The task of ground-to-aerial image geo-localization can be achieved by matching a ground view query image to a reference database of aerial/satellite images. It is highly challenging due to the dramatic viewpoint changes and unknown orientations. In this paper, we propose a novel in-batch reweighting triplet loss to emphasize the positive effect of hard exemplars during end-to-end training. We also integrate an attention mechanism into our model using feature-level contextual information. To analyze the difficulty level of each triplet, we first enforce a modified logistic regression to triplets with a distance rectifying factor. Then, the reference negative distances for corresponding anchors are set, and the relative weights of triplets are computed by comparing their difficulty to the corresponding references. To reduce the influence of extreme hard data and less useful simple exemplars, the final weights are pruned using upper and lower bound constraints. Experiments on two benchmark datasets show that the proposed approach significantly outperforms the state-of-the-art methods.

Image annotation is always an easy task for humans but a tough task for machines. Inspired by human's thinking mode, there is an assumption that the computer has double systems. Each of the systems can handle the task individually and in parallel. In this paper, we introduce a new hierarchical model for image annotation, based on constructing a novel, hierarchical tree, which consists of exploring the relationships between the labels and the features used, and dividing labels into several hierarchies for efficient and accurate labeling.

The Boosting algorithm has two main variants: the gradient Boosting and the totally-corrective column-generation Boosting. Recently, the latter has received increasing attention since it exhibits a better convergence property, thus resulting in more efficient strong learners. In this work, we point out that the totally-corrective column-generation Boosting is equivalent to the gradient-descent method for the gradient Boosting in the weak-learner selection criterion, but uses additional totally-corrective updates for the weak-learner weights. Therefore, other techniques for the gradient Boosting that produce continuous-valued weak learners, e.g. step-wise direct minimization and Newtons method, may also be used in combination with the totally-corrective procedure. In this work we take the well known AdaBoost algorithm as an example, and show that employing the continuous-valued weak learners improves the performance when used with the totally-corrective weak-learner weight update.

Working with a very large feature set is a challenge in the current machine learning research. In this paper, we address the feature-selection problem in the context of training AdaBoost classifiers. The AdaBoost algorithm embeds a feature selection mechanism based on training a classifier for each feature. Learning the single-feature classifiers is the most time consuming part of AdaBoost training, especially when large number of features are available. To solve this problem, we generate a working feature subset using a novel feature subset selection method based on the partial least square regression, and then train and select from this feature subset. The partial least square method is capable of selecting high-dimensional and highly redundant features. The experiments show that the proposed PLS-based feature-selection method generates sensible feature subsets for AdaBoost in a very efficient way.

This paper presents an efficient two-stage method for multi-class image labeling. We first propose a simple label-filtering algorithm (LFA), which can remove most of the irrelevant labels for a query image while the potential labels are maintained. With a small population of potential labels left, we then apply the Naive-Bayes Nearest-Neighbor (NBNN) classifier as the second stage of our algorithm to identify the labels for the query image. This approach has been evaluated on the Corel database, and compared to existing algorithms. Experiment results show that our proposed algorithm can achieve a promising result, as it outperforms existing algorithms.