The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications. However, this technological leap comes with a substantial increase in energy consumption, presenting a significant challenge. To improve the energy efficiency of 5G networks, it is imperative to develop sophisticated models that accurately reflect the influence of base station (BS) attributes and operational conditions on energy usage.Importantly, addressing the complexity and interdependencies of these diverse features is particularly challenging, both in terms of data processing and model architecture design. This paper proposes a novel 5G base stations energy consumption modelling method by learning from a real-world dataset used in the ITU 5G Base Station Energy Consumption Modelling Challenge in which our model ranked second. Unlike existing methods that omit the Base Station Identifier (BSID) information and thus fail to capture the unique energy fingerprint in different base stations, we incorporate the BSID into the input features and encoding it with an embedding layer for precise representation. Additionally, we introduce a novel masked training method alongside an attention mechanism to further boost the model's generalization capabilities and accuracy. After evaluation, our method demonstrates significant improvements over existing models, reducing Mean Absolute Percentage Error (MAPE) from 12.75% to 4.98%, leading to a performance gain of more than 60%.

Abstract--Recurrent neural network-based sequence-tosequence models have been extensively applied for multi-stepahead time series forecasting. These models typically involve a decoder trained using either its previous forecasts or the actual observed values as the decoder inputs. However, relying on self-generated predictions can lead to the rapid accumulation of errors over multiple steps, while using the actual observations introduces exposure bias as these values are unavailable during the extrapolation stage. In this regard, this study proposes a novel training approach called reinforced decoder, which introduces auxiliary models to generate alternative decoder inputs that remain accessible when extrapolating. Additionally, a reinforcement learning algorithm is utilized to dynamically select the optimal inputs to improve accuracy. ULTI-STEP-AHEAD time series prediction, which involves extrapolating a sequence of future values based extrapolating process, i.e., feeding back the one-step-ahead on historical observations, plays a vital role in various realworld prediction to the decoder to predict the value at the next step. Accordingly, research efforts have been devoted to developing statistical some non-autoregressive architectures were proposed and machine learning techniques for multi-step-ahead time to obviate the error propagation issue [10], [16], [17].

We consider the problem of multi-objective optimization (MOO) of expensive black-box functions with the goal of discovering high-quality and diverse Pareto fronts where we are allowed to evaluate a batch of inputs. This problem arises in many real-world applications including penicillin production where diversity of solutions is critical. We solve this problem in the framework of Bayesian optimization (BO) and propose a novel approach referred to as Pareto front-Diverse Batch Multi-Objective BO (PDBO). PDBO tackles two important challenges: 1) How to automatically select the best acquisition function in each BO iteration, and 2) How to select a diverse batch of inputs by considering multiple objectives. We propose principled solutions to address these two challenges. First, PDBO employs a multi-armed bandit approach to select one acquisition function from a given library. We solve a cheap MOO problem by assigning the selected acquisition function for each expensive objective function to obtain a candidate set of inputs for evaluation. Second, it utilizes Determinantal Point Processes (DPPs) to choose a Pareto-front-diverse batch of inputs for evaluation from the candidate set obtained from the first step. The key parameters for the methods behind these two steps are updated after each round of function evaluations. Experiments on multiple MOO benchmarks demonstrate that PDBO outperforms prior methods in terms of both the quality and diversity of Pareto solutions.

Task and motion planning represents a powerful set of hybrid planning methods that combine reasoning over discrete task domains and continuous motion generation. Traditional reasoning necessitates task domain models and enough information to ground actions to motion planning queries. Gaps in this knowledge often arise from sources like occlusion or imprecise modeling. This work generates task and motion plans that include actions cannot be fully grounded at planning time. During execution, such an action is handled by a provided human-designed or learned closed-loop behavior. Execution combines offline planned motions and online behaviors till reaching the task goal. Failures of behaviors are fed back as constraints to find new plans. Forty real-robot trials and motivating demonstrations are performed to evaluate the proposed framework and compare against state-of-the-art. Results show faster execution time, less number of actions, and more success in problems where diverse gaps arise. The experiment data is shared for researchers to simulate these settings. The work shows promise in expanding the applicable class of realistic partially grounded problems that robots can address.

Efficient and current roadway geometry data collection is a critical task for transportation agencies to undertake effective road planning, maintenance, design, and rehabilitation efforts. The methods for gathering such data can be broadly classified into two categories: a) land-based methods, which encompass field inventory, mobile mapping, and image logging, and b) aerial-based methods, which involve satellite imagery, drones, and laser scanning. However, employing land-based techniques for extensive highway networks covering thousands of miles proves arduous and costly, and poses safety risks for crew members. Consequently, there exists a pressing need to develop more efficient methodologies for acquiring this data promptly, safely, and economically. Fortunately, with the increasing availability of high-resolution images and recent strides in computer vision and object detection technologies, automated extraction of roadway geometry features has become feasible.

We investigate learning the eigenfunctions of evolution operators for time-reversal invariant stochastic processes, a prime example being the Langevin equation used in molecular dynamics. Many physical or chemical processes described by this equation involve transitions between metastable states separated by high potential barriers that can hardly be crossed during a simulation. To overcome this bottleneck, data are collected via biased simulations that explore the state space more rapidly. We propose a framework for learning from biased simulations rooted in the infinitesimal generator of the process and the associated resolvent operator. We contrast our approach to more common ones based on the transfer operator, showing that it can provably learn the spectral properties of the unbiased system from biased data. In experiments, we highlight the advantages of our method over transfer operator approaches and recent developments based on generator learning, demonstrating its effectiveness in estimating eigenfunctions and eigenvalues. Importantly, we show that even with datasets containing only a few relevant transitions due to sub-optimal biasing, our approach recovers relevant information about the transition mechanism.

A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility, i.e., reducing carbon emissions to tackle climate change. Cap and trade stands as a critical principle based on allocating and trading carbon allowances (carbon emission credit), enabling economic agents to follow planned emissions and penalizing excess emissions. A central authority is responsible for introducing and allocating those allowances in cap and trade. However, the complexity of carbon market dynamics makes accurate simulation intractable, which in turn hinders the design of effective allocation strategies. To address this, we propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL). Government agents allocate carbon credits, while enterprises engage in economic activities and carbon trading. This framework illustrates agents' behavior comprehensively. Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions. Our project is available at https://github.com/xwanghan/Carbon-Simulator.

Today robots must be safe, versatile, and user-friendly to operate in unstructured and human-populated environments. Dynamical system-based imitation learning enables robots to perform complex tasks stably and without explicit programming, greatly simplifying their real-world deployment. To exploit the full potential of these systems it is crucial to implement closed loops that use visual feedback. Vision permits to cope with environmental changes, but is complex to handle due to the high dimension of the image space. This study introduces a dynamical system-based imitation learning for direct visual servoing. It leverages off-the-shelf deep learning-based perception backbones to extract robust features from the raw input image, and an imitation learning strategy to execute sophisticated robot motions. The learning blocks are integrated using the large projection task priority formulation. As demonstrated through extensive experimental analysis, the proposed method realizes complex tasks with a robotic manipulator.

Much of the present-day Artificial Intelligence (AI) utilizes artificial neural networks, which are sophisticated computational models designed to recognize patterns and solve complex problems by learning from data. However, a major bottleneck occurs during a device's calculation of weighted sums for forward propagation and optimization procedure for backpropagation, especially for deep neural networks, or networks with numerous layers. Exploration into different methods of implementing neural networks is necessary for further advancement of the area. While a great deal of research into AI hardware in both directions, analog and digital implementation widely exists, much of the existing survey works lacks discussion on the progress of analog deep learning. To this end, we attempt to evaluate and specify the advantages and disadvantages, along with the current progress with regards to deep learning, for analog implementations. In this paper, our focus lies on the comprehensive examination of eight distinct analog deep learning methodologies across multiple key parameters. These parameters include attained accuracy levels, application domains, algorithmic advancements, computational speed, and considerations of energy efficiency and power consumption. We also identify the neural network-based experiments implemented using these hardware devices and discuss comparative performance achieved by the different analog deep learning methods along with an analysis of their current limitations. Overall, we find that Analog Deep Learning has great potential for future consumer-level applications, but there is still a long road ahead in terms of scalability. Most of the current implementations are more proof of concept and are not yet practically deployable for large-scale models.

While AI could change the world in many unforeseen ways, it's already having one massive impact: a voracious consumption of energy. Generative AI does not simply float upon ephemeral intuition. Rather, it gathers strength via thousands of computers in data centers across the world, which operate constantly on full blast. In January, the International Energy Agency (IEA) forecast that global data center electricity demand will more than double from 2022 to 2026, with AI playing a major role in that increase. AI industry insiders say the world has plenty of energy capacity to absorb this increased demand, and that technological efficiency improvements could offset these increases.