Deep Reinforcement Learning (DRL) is a key machine learning technology driving progress across various scientific and engineering fields, including wireless communication. However, its limited interpretability and generalizability remain major challenges. In supervised learning, generalizability is commonly evaluated through the generalization error using information-theoretic methods. In DRL, the training data is sequential and not independent and identically distributed (i.i.d.), rendering traditional information-theoretic methods unsuitable for generalizability analysis. To address this challenge, this paper proposes a novel analytical method for evaluating the generalizability of DRL. Specifically, we first model the evolution of states and actions in trained DRL algorithms as unknown discrete, stochastic, and nonlinear dynamical functions. Then, we employ a data-driven identification method, the Koopman operator, to approximate these functions, and propose two interpretable representations. Based on these interpretable representations, we develop a rigorous mathematical approach to evaluate the generalizability of DRL algorithms. This approach is formulated using the spectral feature analysis of the Koopman operator, leveraging the H_\infty norm. Finally, we apply this generalization analysis to compare the soft actor-critic method, widely recognized as a robust DRL approach, against the proximal policy optimization algorithm for an unmanned aerial vehicle-assisted mmWave wireless communication scenario.

More recently, (Fu et al., 2023) natural language processing (NLP) tasks using creates a meta-model responsible for predicting the latest generative pretrained models such as the accuracy of the LLM model using the model's GPT (OpenAI, 2023; Ouyang et al., 2022), PaLM confidence scores as features. Methods from the (Chowdhery et al., 2022), and many others (Touvron computer vision (CV) domain to assess unlabeled et al., 2023; Bai et al., 2022; Penedo et al., data more generally have, for example, proposed 2023; Taori et al., 2023). This new generation of the average threshold confidence method that learns models opens up many new possibilities including a threshold over the model's confidence, predicting competitive performance in zero-shot and few-shot accuracy as the fraction of unlabeled examples settings for tasks that have typically been modeled exceeding that threshold (Garg et al., 2022), or iteratively using a supervised setting (OpenAI, 2023). More learn an ensemble of models to identify established language models (BERT (Devlin et al., misclassified data points and perform self-training 2019), RoBERTa (Liu et al., 2019), XLM-Roberta to improve the ensemble with the identified points (Conneau et al., 2020b), etc.) provide a strong balance (Chen et al., 2021). However, the metrics and hyperparameters of inference cost and task performance for in previous works are specifically for such systems. This broad class of large language classification tasks and cannot be easily extended models (LLMs) used for complex supervised NLP to more complex tasks.

As the journey of 5G standardization is coming to an end, academia and industry have already begun to consider the sixth-generation (6G) wireless networks, with an aim to meet the service demands for the next decade. Deep learning-based RF fingerprinting (DL-RFFP) has recently been recognized as a potential solution for enabling key wireless network applications and services, such as spectrum policy enforcement and network access control. The state-of-the-art DL-RFFP frameworks suffer from a significant performance drop when tested with data drawn from a domain that is different from that used for training data. In this paper, we propose ADL-ID, an unsupervised domain adaption framework that is based on adversarial disentanglement representation to address the temporal domain adaptation for the RFFP task. Our framework has been evaluated on real LoRa and WiFi datasets and showed about 24% improvement in accuracy when compared to the baseline CNN network on short-term temporal adaptation. It also improves the classification accuracy by up to 9% on long-term temporal adaptation. Furthermore, we release a 5-day, 2.1TB, large-scale WiFi 802.11b dataset collected from 50 Pycom devices to support the research community efforts in developing and validating robust RFFP methods.

Abstract: Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue. In this paper, we introduce a fully Visual Attention-based Depth (VADepth) network, where spatial attention and channel attention are applied to all stages. By continuously extracting the dependencies of features along the spatial and channel dimensions over a long distance, VADepth network can effectively preserve important details and suppress interfering features to better perceive the scene structure for more accurate depth estimates. In addition, we utilize geometric priors to form scale constraints for scale-aware model training. Specifically, we construct a novel scale-aware loss using the distance between the camera and a plane fitted by the ground points corresponding to the pixels of the rectangular area in the bottom middle of the image.

Backtracking search combined with constraint solving is the main approach to solve problems in Constraint Programming (CP). The key to effective search is having a good variable search heuristic to select a variable to branch as the size of the search tree is strongly dependent on the selected variables. In CP, many general purpose variable ordering search heuristics have been proposed and implemented in many CP solvers, such as the conflict-driven heuristic dom/wdeg [1], impactbased search (IBS) heuristic [2], and activity-based search (ABS) heuristic [3]. Search heuristics by their nature are not designed to be optimal search strategies but merely good ones. Thus, our goal in this paper is a new search heuristic which can outperform existing heuristics on some instances across a range of problems. We propose a new idea which is correlation-based search (CRBS), the search heuristic employs correlations between variables.