To meet the demands of high-capacity and low-delay services, Fifth Generation (5G) Base Stations (BSs) are typically deployed in ultra-dense configurations, especially in urban areas. While this densification enhances coverage and service quality, it also leads to substantially increased energy consumption. However, the dense deployment pattern makes BS workloads more responsive to the spatiotemporal variations in user behavior, offering opportunities for energy-saving strategies that dynamically adjust BS operation states. In this context, we propose a Delay-aware and Energy-efficient Integrated Optimization System (DEIS) based on Deep Reinforcement Learning (DRL), which jointly optimizes energy consumption and network delay while maintaining user satisfaction. DEIS leverages a real-world dataset collected from operational 5G BSs provided by partner network operators, containing both BS deployment data and high-volume user request logs. Extensive simulations demonstrate that DEIS can achieve a 41% reduction in energy consumption while ensuring reliable delay performance.

With the rapid proliferation of streaming services, network load exhibits highly time-varying and bursty behavior, posing serious challenges for maintaining Quality of Service (QoS) in Crowdsourced Cloud-Edge Platforms (CCPs). While CCPs leverage Predict-then-Schedule architecture to improve QoS and profitability, accurate load forecasting remains challenging under traffic surges. Existing methods either minimize mean absolute error, resulting in underprovisioning and potential Service Level Agreement (SLA) violations during peak periods, or adopt conservative overprovisioning strategies, which mitigate SLA risks at the expense of increased resource expenditure. To address this dilemma, we propose HRS, a Hybrid Representation framework with Scheduling awareness that integrates numerical and image-based representations to better capture extreme load dynamics. We further introduce a Scheduling-Aware Loss (SAL) that captures the asymmetric impact of prediction errors, guiding predictions that better support scheduling decisions. Extensive experiments on four real-world datasets demonstrate that HRS consistently outperforms ten baselines and achieves state-of-the-art performance, reducing SLA violation rates by 63.1% and total profit loss by 32.3%. Our code is available at [29].

Abstract Despite the significant success of transformer models and their successors in various natural language processing (NLP) applications, their internal workings are still not fully understood. Much of the current interpretability research has focused primarily on numerical components, often missing the complex semantic layers within these models. To fill this gap, this study explores the interpretability of the transformer model, a cornerstone of modern NLP, by addressing the semantic complexities of its multi-layer architecture. We identify three key questions: (i) the influence of the multi-layer structure on semantic processing, (ii) the unique contributions of each layer to model performance, and (iii) methodologies for determining optimal layer counts for the encoder and decoder. To tackle these issues, we introduce the semantic interpreter for transformer hierarchy, an innovative framework that employs convex hull metrics to visualize and assess semantic quality and quantity. Our contributions include novel methods for semantic assessment, a dual analytical framework that integrates cumulative and layer-to-layer perspectives, and insights into the dynamics of encoding and decoding. This comprehensive approach aims to enhance the understanding of Transformer models, ultimately guiding their refinement for improved interpretability and effectiveness in natural language tasks.

Responding to the escalating interest in long-term forecasting within the industry, we introduce EasyTS, a comprehensive toolkit engineered to streamline data collection, analysis, and model creation procedures. EasyTS acts as a unified solution, driving progress in long-term time series forecasting. The platform provides effortless access to various time series datasets, including a newly open-sourced multi-scenario dataset in the electricity domain. Integrated visualization and analysis tools help unveil inherent data features and relationships. EasyTS facilitates a user-friendly model validation approach with versatile evaluation criteria. This toolkit allows researchers to compare their models proficiently against renowned benchmarks. With our ongoing commitment to expanding our dataset collection and enhancing toolkit functionalities, we aspire to contribute significantly to the time series forecasting domain. Code is available at this repository: https://github.com/EdgeBigBang/EasyTS.git.

As a robust platform for mobile edge computing, 5G networks, while delivering high data rates and low latency, face a pressing concern with the escalating energy consumption of 5G Base Stations (BSs). To address this issue, we propose an algorithm called the Environmental Protection Prophet (EPP), based on the clustered and geographically inclined user request patterns. The EPP algorithm groups users according to their proximity to BSs and utilizes edge caching to reduce response times for user requests. This clustering strategy optimizes user allocation while minimizing BSs' energy consumption, all while meeting user Quality of Service (QoS) requirements. Simulation experiments illustrate the potential for energy savings and latency reduction, particularly in densely populated urban areas. The findings provide valuable insights for the design of energy-efficient 5G networks, concurrently addressing environmental concerns and meeting user performance expectations.

While Transformers and their derivatives have shown strong performance in various NLP tasks, understanding their internal mechanisms remains challenging. Mainstream interpretability research often focuses solely on numerical attributes, neglecting the complex semantic structure inherent in the model. We have developed the SITH(Semantic Interpreter for Transformer Hierarchy) framework to address this issue. We focus on creating universal text representation methods and uncovering the semantic principles of the Transformer's hierarchical structure. We use the convex hull method to represent sequence semantics in an n-dimensional Semantic Euclidean space and define different evaluation indicators through convex hull to analyze semantic quality and quantity changes. Our analysis takes a dual perspective: a multi-layer cumulative perspective and an individual layer-to-layer shift perspective. When applied to machine translation, our results reveal potential semantic processes and emphasize the effectiveness of stacking and hierarchical differences. These insights are valuable for fine-tuning hyperparameters at the encoder and decoder layers.