Abstract--We introduce BERTO, a BERT -based framework for traffic prediction and energy optimization in cellular networks. Built on transformer architectures, BERTO delivers high prediction accuracy, while its Balancing Loss Function and prompt-based customization allow operators to adjust the trade-off between power savings and performance. Natural language prompts guide the model to manage underprediction and overprediction in accordance with the operator's intent. Experiments on real-world datasets show that BERTO improves upon existing models with a 4.13% reduction in MSE while introducing the feature of balancing competing objectives of power saving and performance through simple natural language inputs, operating over a flexible range of 1.4 kW in power and up to 9 variation in service quality, making it well suited for intelligent RAN deployments. Time series data is ubiquitous across all layers of modern communication networks.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Your morning catch-up: Mayor Lurie has SF feeling better, California's job market is taking a hit and more big stories Why outrage is erupting over Trump plan to exclude nursing from'professional' designation This is read by an automated voice. Please report any issues or inconsistencies here . Trump administration proposes excluding nursing and other fields from "professional" designation, capping graduate student loans. Nursing leaders warn the policy will worsen California's severe nurse shortage by discouraging graduate degrees required for teaching and specialized patient care.

For example, in the Pandemic Response Challenge experiment, the context consisted of data about the geographic region for which the predictions were made, e.g., historical data of COVID-19 cases and intervention policies; actions were future schedules of intervention policies for the region; and outcomes were predicted future cases of COVID-19 along with the stringency

These challenges are exacerbated by dynamics influenced not only by the environment but also by strategies of opponents.

Abstract-- Detecting anomalies in multivariate time series is essential for monitoring complex industrial systems, where high dimensionality, limited labeled data, and subtle dependencies between sensors cause significant challenges. This paper presents a deep reinforcement learning framework that combines a V ari-ational Autoencoder (V AE), an LSTM-based Deep Q-Network (DQN), dynamic reward shaping, and an active learning module to address these issues in a unified learning framework. The main contribution is the implementation of Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection (DRSMT), which demonstrates how each component enhances the detection process. The V AE captures compact latent representations and reduces noise. The DQN enables adaptive, sequential anomaly classification, and the dynamic reward shaping balances exploration and exploitation during training by adjusting the importance of reconstruction and classification signals. In addition, active learning identifies the most uncertain samples for labeling, reducing the need for extensive manual supervision. Experiments on two multivariate benchmarks, namely Server Machine Dataset (SMD) and Water Distribution T estbed (W ADI), show that the proposed method outperforms existing baselines in F1-score and AU-PR. In many of today's applications, identifying and removing anomalies (i.e., outliers) has become essential to ensure system reliability. In multivariate time series data, specifically, different factors can result in anomalies.

Training foundation models, such as GPT -3 and PaLM, can be extremely expensive, often involving tens of thousands of GPUs running continuously for months.

Training foundation models, such as GPT -3 and PaLM, can be extremely expensive, often involving tens of thousands of GPUs running continuously for months.

Algorithmic stability is a key characteristic of an algorithm regarding its sensitivity to perturbations of input samples. In this paper, we propose an innovative unsupervised feature selection algorithm attaining this stability with provable guarantees.