An energy-based model (EBM) is a popular generative framework that offers both explicit density and architectural flexibility, but training them is difficult since it is often unstable and time-consuming. In recent years, various training techniques have been developed, e.g., better divergence measures or stabilization in MCMC sampling, but there often exists a large gap between EBMs and other generative frameworks like GANs in terms of generation quality. In this paper, we propose a novel and effective framework for improving EBMs via contrastive representation learning (CRL). To be specific, we consider representations learned by contrastive methods as the true underlying latent variable. This contrastive latent variable could guide EBMs to understand the data structure better, so it can improve and accelerate EBM training significantly. To enable the joint training of EBM and CRL, we also design a new class of latent-variable EBMs for learning the joint density of data and the contrastive latent variable. Our experimental results demonstrate that our scheme achieves lower FID scores, compared to prior-art EBM methods (e.g., additionally using variational autoencoders or diffusion techniques), even with significantly faster and more memory-efficient training. We also show conditional and compositional generation abilities of our latent-variable EBMs as their additional benefits, even without explicit conditional training. The code is available at https://github.com/hankook/CLEL.
Aligning a robot's trajectory or map to the inertial frame is a critical capability that is often difficult to do accurately even though inertial measurement units (IMUs) can observe absolute roll and pitch with respect to gravity. Accelerometer biases and scale factor errors from the IMU's initial calibration are often the major source of inaccuracies when aligning the robot's odometry frame with the inertial frame, especially for low-grade IMUs. Practically, one would simultaneously estimate the true gravity vector, accelerometer biases, and scale factor to improve measurement quality but these quantities are not observable unless the IMU is sufficiently excited. While several methods estimate accelerometer bias and gravity, they do not explicitly address the observability issue nor do they estimate scale factor. We present a fixed-lag factor-graph-based estimator to address both of these issues. In addition to estimating accelerometer scale factor, our method mitigates limited observability by optimizing over a time window an order of magnitude larger than existing methods with significantly lower computational burden. The proposed method, which estimates accelerometer intrinsics and gravity separately from the other states, is enabled by a novel, velocity-agnostic measurement model for intrinsics and gravity, as well as a new method for gravity vector optimization on S2. Accurate IMU state prediction, gravity-alignment, and roll/pitch drift correction are experimentally demonstrated on public and self-collected datasets in diverse environments.
Severe class imbalance is one of the main conditions that make machine learning in cybersecurity difficult. A variety of dataset preprocessing methods have been introduced over the years. These methods modify the training dataset by oversampling, undersampling or a combination of both to improve the predictive performance of classifiers trained on this dataset. Although these methods are used in cybersecurity occasionally, a comprehensive, unbiased benchmark comparing their performance over a variety of cybersecurity problems is missing. This paper presents a benchmark of 16 preprocessing methods on six cybersecurity datasets together with 17 public imbalanced datasets from other domains. We test the methods under multiple hyperparameter configurations and use an AutoML system to train classifiers on the preprocessed datasets, which reduces potential bias from specific hyperparameter or classifier choices. Special consideration is also given to evaluating the methods using appropriate performance measures that are good proxies for practical performance in real-world cybersecurity systems. The main findings of our study are: 1) Most of the time, a data preprocessing method that improves classification performance exists. 2) Baseline approach of doing nothing outperformed a large portion of methods in the benchmark. 3) Oversampling methods generally outperform undersampling methods. 4) The most significant performance gains are brought by the standard SMOTE algorithm and more complicated methods provide mainly incremental improvements at the cost of often worse computational performance.
We consider a multi-user hybrid beamforming system, where the multiplexing gain is limited by the small number of RF chains employed at the base station (BS). To allow greater freedom for maximizing the multiplexing gain, it is better if the BS selects and serves some of the users at each scheduling instant, rather than serving all the users all the time. We adopt a two-timescale protocol that takes into account the mmWave characteristics, where at the long timescale an analog beam is chosen for each user, and at the short timescale users are selected for transmission based on the chosen analog beams. The goal of the user selection is to maximize the traditional Proportional Fair (PF) metric. However, this maximization is non-trivial due to interference between the analog beams for selected users. We first define a greedy algorithm and a "top-k" algorithm, and then propose a machine learning (ML)-based user selection algorithm to provide an efficient trade-off between the PF performance and the computation time. Throughout simulations, we analyze the performance of the ML-based algorithms under various metrics, and show that it gives an efficient trade-off in performance as compared to counterparts.
Uncertainty estimation of the trained deep learning network provides important information for improving the learning efficiency or evaluating the reliability of the network prediction. In this paper, we propose a method for the uncertainty estimation for multi-class image classification using test-time mixup augmentation (TTMA). To improve the discrimination ability between the correct and incorrect prediction of the existing aleatoric uncertainty, we propose the data uncertainty by applying the mixup augmentation on the test data and measuring the entropy of the histogram of predicted labels. In addition to the data uncertainty, we propose a class-specific uncertainty presenting the aleatoric uncertainty associated with the specific class, which can provide information on the class confusion and class similarity of the trained network. The proposed methods are validated on two public datasets, the ISIC-18 skin lesion diagnosis dataset, and the CIFAR-100 real-world image classification dataset. The experiments demonstrate that (1) the proposed data uncertainty better separates the correct and incorrect prediction than the existing uncertainty measures thanks to the mixup perturbation, and (2) the proposed class-specific uncertainty provides information on the class confusion and class similarity of the trained network for both datasets.
Despite advancements in generic object detection, there remains a performance gap in detecting small objects compared to normal-scale objects. We for the first time observe that existing bounding box regression methods tend to produce distorted gradients for small objects and result in less accurate localization. To address this issue, we present a novel Confidence-driven Bounding Box Localization (C-BBL) method to rectify the gradients. C-BBL quantizes continuous labels into grids and formulates two-hot ground truth labels. In prediction, the bounding box head generates a confidence distribution over the grids. Unlike the bounding box regression paradigms in conventional detectors, we introduce a classification-based localization objective through cross entropy between ground truth and predicted confidence distribution, generating confidence-driven gradients. Additionally, C-BBL describes a uncertainty loss based on distribution entropy in labels and predictions to further reduce the uncertainty in small object localization. The method is evaluated on multiple detectors using three object detection benchmarks and consistently improves baseline detectors, achieving state-of-the-art performance. We also demonstrate the generalizability of C-BBL to different label systems and effectiveness for high resolution detection, which validates its prospect as a general solution.
Single-user semantic communications have attracted extensive research recently, but multi-user semantic broadcast communication (BC) is still in its infancy. In this paper, we propose a practical robust features-disentangled multi-user semantic BC framework, where the transmitter includes a feature selection module and each user has a feature completion module. Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users' intended semantic features are selected for broadcasting, which can further improve the transmission efficiency. Within this framework, we further investigate two information-theoretic metrics, including the ultimate compression rate under both the distortion and perception constraints, and the achievable rate region of the semantic BC. Furthermore, to realize the proposed semantic BC framework, we design a lightweight robust semantic BC network by exploiting a supervised autoencoder (AE), which can controllably disentangle sematic features. Moreover, we design the first hardware proof-of-concept prototype of the semantic BC network, where the proposed semantic BC network can be implemented in real time. Simulations and experiments demonstrate that the proposed robust semantic BC network can significantly improve transmission efficiency.
Hybrid multiple-input multiple-output (MIMO) is an attractive technology for realizing extreme massive MIMO systems envisioned for future wireless communications in a scalable and power-efficient manner. However, the fact that hybrid MIMO systems implement part of their beamforming in analog and part in digital makes the optimization of their beampattern notably more challenging compared with conventional fully digital MIMO. Consequently, recent years have witnessed a growing interest in using data-aided artificial intelligence (AI) tools for hybrid beamforming design. This article reviews candidate strategies to leverage data to improve real-time hybrid beamforming design. We discuss the architectural constraints and characterize the core challenges associated with hybrid beamforming optimization. We then present how these challenges are treated via conventional optimization, and identify different AI-aided design approaches. These can be roughly divided into purely data-driven deep learning models and different forms of deep unfolding techniques for combining AI with classical optimization.We provide a systematic comparative study between existing approaches including both numerical evaluations and qualitative measures. We conclude by presenting future research opportunities associated with the incorporation of AI in hybrid MIMO systems.
Safety is one of the fundamental challenges in control theory. Recently, multi-step optimal control problems for discrete-time dynamical systems were formulated to enforce stability, while subject to input constraints as well as safety-critical requirements using discrete-time control barrier functions within a model predictive control (MPC) framework. Existing work usually focus on the feasibility or the safety for the optimization problem, and the majority of the existing work restrict the discussions to relative-degree one for control barrier function. Additionally, the real-time computation is challenging when a large horizon is considered in the MPC problem for relative-degree one or high-order control barrier functions. In this paper, we propose a framework that solves the safety-critical MPC problem in an iterative optimization, which is applicable for any relative-degree control barrier functions. In the proposed formulation, the nonlinear system dynamics as well as the safety constraints modeled as discrete-time high-order control barrier functions (DHOCBF) are linearized at each time step. Our formulation is generally valid for any control barrier function with an arbitrary relative-degree. The advantages of fast computational performance with safety guarantee are analyzed and validated with numerical results.
Long-form clinical summarization of hospital admissions has real-world significance because of its potential to help both clinicians and patients. The faithfulness of summaries is critical to their safe usage in clinical settings. To better understand the limitations of abstractive systems, as well as the suitability of existing evaluation metrics, we benchmark faithfulness metrics against fine-grained human annotations for model-generated summaries of a patient's Brief Hospital Course. We create a corpus of patient hospital admissions and summaries for a cohort of HIV patients, each with complex medical histories. Annotators are presented with summaries and source notes, and asked to categorize manually highlighted summary elements (clinical entities like conditions and medications as well as actions like "following up") into one of three categories: ``Incorrect,'' ``Missing,'' and ``Not in Notes.'' We meta-evaluate a broad set of proposed faithfulness metrics and, across metrics, explore the importance of domain adaptation (e.g. the impact of in-domain pre-training and metric fine-tuning), the use of source-summary alignments, and the effects of distilling a single metric from an ensemble of pre-existing metrics. Off-the-shelf metrics with no exposure to clinical text correlate well yet overly rely on summary extractiveness. As a practical guide to long-form clinical narrative summarization, we find that most metrics correlate best to human judgments when provided with one summary sentence at a time and a minimal set of relevant source context.