pwc-archive/papers-with-abstracts huggingface dataset:
From Classification to Optimization: Slicing and Resource Management with TRACTOR
Authors: Joshua Groen, Zixian Yang, Divyadharshini Muruganandham, Mauro Belgiovine, Lei Ying, Kaushik Chowdhury
Date: 2023-12-13 00:00:00
Tasks: Management, Traffic Classification

5G and beyond networks promise advancements in bandwidth, latency, and connectivity. The Open Radio Access Network (O-RAN) framework enhances flexibility through network slicing and closed-loop RAN control. Central to this evolution is integrating machine learning (ML) for dynamic network control. This paper presents a framework to optimize O-RAN operation. First, we build and share a robust O-RAN dataset from real-world traffic captured across diverse locations and mobility scenarios, replicated within a full-stack srsRAN-based O-RAN system using the Colosseum RF emulator. This dataset supports ML training and deployment. We then introduce a traffic classification approach leveraging various ML models, demonstrating rapid training, testing, and refinement to improve accuracy. With up to 99% offline accuracy and 92% online accuracy for specific slices, our framework adapts efficiently to different models and network conditions. Finally, we present a physical resource block (PRB) assignment optimization strategy using reinforcement learning to refine resource allocation. Our learned policy achieves a mean performance score (0.631), surpassing a manually configured expert policy (0.609) and a random baseline (0.588), demonstrating improved PRB utilization. More importantly, our approach exhibits lower variability, with the Coefficient of Variation (CV) reduced by up to an order of magnitude in three out of four cases, ensuring more consistent performance. Our contributions, including open-source tools and datasets, accelerate O-RAN and ML-driven network control research.
Optimization of Power Control for Autonomous Hybrid Electric Vehicles with Flexible Power Demand
Authors: Mohammadali Kargar, Xingyong Song
Date: 2023-12-13 00:00:00
Tasks: Autonomous Vehicles

Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degree of freedom to the powertrain control. The present research investigates the use of an Approximate Dynamic Programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The formulation is based on an autonomous hybrid electric vehicle (HEV), while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of the investigated method.
Non-myopic Beam Scheduling for Multiple Smart Target Tracking in Phased Array Radar Network
Authors: Yuhang Hao, Zengfu Wang, José Niño-Mora, Jing Fu, Min Yang, Quan Pan
Date: 2023-12-13 00:00:00
Tasks: Scheduling

A smart target, also referred to as a reactive target, can take maneuvering motions to hinder radar tracking. We address beam scheduling for tracking multiple smart targets in phased array radar networks. We aim to mitigate the performance degradation in previous myopic tracking methods and enhance the system performance, which is measured by a discounted cost objective related to the tracking error covariance (TEC) of the targets. The scheduling problem is formulated as a restless multi-armed bandit problem (RMABP) with state variables, following the Markov decision process. In particular, the problem consists of parallel bandit processes. Each bandit process is associated with a target and evolves with different transition rules for different actions, i.e., either the target is tracked or not. We propose a non-myopic, scalable policy based on Whittle indices for selecting the targets to be tracked at each time. The proposed policy has a linear computational complexity in the number of targets and the truncated time horizon in the index computation, and is hence applicable to large networks with a realistic number of targets. We present numerical evidence that the model satisfies sufficient conditions for indexability (existence of the Whittle index) based upon partial conservation laws, and, through extensive simulations, we validate the effectiveness of the proposed policy in different scenarios.
Selecting the Number of Sensor Scans in Surveillance Operations
Authors: Graham V. Weinberg
Date: 2023-12-13 00:00:00

Searching for concealed threats within a surveillance region is an important role for military sensors. One prominent case is the search for a submarine by a helicopter deploying a dipping sonar in anti-submarine warfare. Another is the utilisation of an uncrewed aerial vehicle for remote detection of land mines. These platforms will deploy an appropriate sensor to search within a given region for a potential threat. The sensors employed may not provide continuous surveillance, but may have active periods where the inherent sensor is scanning for threats. The reasons for disjoint activation periods may be both functional as well as tactical. Therefore, given a specified search pattern under which the surveillance platform operates, the question of the number of sensor scans required becomes relevant for search mission pre-planning. Hence it is not only important to develop a modelling framework to assess performance but it is of relevance to consider whether a minimum number of sensor scans can be determined to guarantee a desired level of performance. Consequently this paper introduces a framework for this and illustrates how it can be utilised in practice.
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations
Authors: Pedram Rabiee, Amirsaeid Safari
Date: 2023-12-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration, Safe Reinforcement Learning

This paper introduces the reinforcement learning backup shield (RLBUS), an algorithm that guarantees safe exploration in reinforcement learning (RL) by incorporating backup control barrier functions (BCBFs). RLBUS constructs an implicit control forward invariant subset of the safe set using multiple backup policies, ensuring safety in the presence of input constraints. While traditional BCBFs often result in conservative control forward-invariant sets due to the design of backup controllers, RLBUS addresses this limitation by leveraging model-free RL to train an additional backup policy, which enlarges the identified control forward invariant subset of the safe set. This approach enables the exploration of larger regions in the state space with zero safety violations during training. The effectiveness of RLBUS is demonstrated on an inverted pendulum example, where the expanded invariant set allows for safe exploration over a broader state space, enhancing performance without compromising safety.
Brain Computer Interface Technology for Future Battlefield
Authors: Guodong Xiong, Xinyan Ma, Wei Li, Jiaqi Cao, Jian Zhong, Yicong Su
Date: 2023-12-13 00:00:00
Tasks: Brain Computer Interface, Decision Making

With the development of artificial intelligence and unmanned equipment, human-machine hybrid formations will be the main focus in future combat formations. With the development of big data and various situational awareness technologies, while enhancing the breadth and depth of information, decision-making has also become more complex. The operation mode of existing unmanned equipment often requires complex manual input, which is not conducive to the battlefield environment. How to reduce the cognitive load of information exchange between soldiers and various unmanned equipment is an important issue in future intelligent warfare. This paper proposes a brain computer interface communication system for soldier combat, which takes into account the characteristics of soldier combat scenarios in design. The stimulation paradigm is combined with helmets, portable computers, and firearms, and brain computer interface technology is used to achieve fast, barrier free, and hands-free communication between humans and machines. Intelligent algorithms are combined to assist decision-making in fully perceiving and fusing situational information on the battlefield, and a large amount of data is processed quickly, understanding and integrating a large amount of data from human and machine networks, achieving real-time perception of battlefield information, making intelligent decisions, and achieving the effect of direct control of drone swarms and other equipment by the human brain to assist in soldier scenarios.
Coordination of Damping Controllers: A Novel Data-Informed Approach for Adaptability
Authors: Francisco Zelaya-Arrazabal, Hector Pulgar-Painemal, Jingzi Liu, Horacio Silva-Saravia, Fangxing Li
Date: 2023-12-12 00:00:00
Tasks: Learning Theory

This paper explores the novel concept of damping controller coordination, which aims to minimize the Total Action metric by identifying an optimal switching combination (on/off) of these controllers. The metric is rooted in power system physics, capturing oscillation energy associated with all synchronous generators in the grid. While coordination has shown promising results, it has relied on computing linear sensitivities based on the grid model. This paper proposes a data-informed framework to accurately estimate total action and subsequently determine an optimal switching combination. The estimation is provided by a multivariate function approximator that captures the nonlinear relationship between system-wide area measurements, the status of damping controllers, and the conditions of the disturbance. By enabling real-time coordination, electromechanical oscillations are reduced, enhancing power system stability. The concept is tested in the Western North America Power System (wNAPS) and compared with the model-based approach for coordination. The proposed coordination outperforms the model-based approach, demonstrating effective adaptability and performance in handling multi-mode events. Additionally, the results show significant reductions in low-frequency electromechanical oscillations even under various operating conditions, fault locations, and time delay considerations.
Learning for System Identification of NDAE-modeled Power Systems
Authors: Wenjie Mei, Muhammad Nadeem, MirSaleh Bahavarnia, Ahmad F. Taha
Date: 2023-12-12 00:00:00

System identification through learning approaches is emerging as a promising strategy for understanding and simulating dynamical systems, which nevertheless faces considerable difficulty when confronted with power systems modeled by differential-algebraic equations (DAEs). This paper introduces a neural network (NN) framework for effectively learning and simulating solution trajectories of DAEs. The proposed framework leverages the synergy between Implicit Runge-Kutta (IRK) time-stepping schemes tailored for DAEs and NNs (including a differential NN (DNN)). The framework enforces an NN to cooperate with the algebraic equation of DAEs as hard constraints and is suitable for the identification of the ordinary differential equation (ODE)-modeled dynamic equation of DAEs using an existing penalty-based algorithm. Finally, the paper demonstrates the efficacy and precision of the proposed NN through the identification and simulation of solution trajectories for the considered DAE-modeled power system.
Smart Energy Management with Optimized Prosumerism for Achieving Dynamic Net-Zero Balance in Electrified Road Transport Networks
Authors: Ferheen Ayaz, Maziar Nekovee
Date: 2023-12-12 00:00:00
Tasks: energy management, Management, Scheduling

The increasing number of Electric Vehicles (EVs) have led to rising energy demands which aggregates the burden on grid supply. A few solutions have been proposed to reduce grid load, for example, using storage systems for storing surplus energy from EVs or time-scheduling supply. These solutions are costly and limited to specific regions and times. This paper proposes a smart energy management solution for a massively electrified road transport network. It comprises of energy supplies from grid, charging stations, renewable sources and EVs connected by 5G-enabled aggregators. We propose EVs as prosumers, which are energy consumers but also supply back their surplus energy via Vehicle-to-Grid (V2G) technology. We use machine learning to forecast hourly energy output from renewable sources, surplus supply from EVs and their demands. A grid cost minimization using Mixed Integer Linear Programming solution is proposed to dynamically alter supply according to demand and energy provision from EVs. The upper bounds of surplus supply and demand of EVs are theoretically derived. An incentive distribution mechanism is presented to reward EVs offering their surplus supply and to discourage them to become selfish which is analyzed using Prisoner's dilemma game. The paper also presents an optimum number of charging stations on a road. Simulation results show that the proposed solution can effectively meet the demand requirements even if the supply from grid is limited, and can averagely reduce 38.21% of grid load. It results in 5.3% of average cost reduction compared with optimization without prosumers. The proposed penalty charge for CO2 emissions results in over 50% cost reduction by using renewable resources in the proposed solution as compared to fossil fuels. The communication and computation complexity of the proposed solution is reduced by 5G-enabled aggregator.
Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings
Authors: Deep Chatterjee, Philip C. Harris, Maanas Goel, Malina Desai, Michael W. Coughlin, Erik Katsavounidis
Date: 2023-12-11 00:00:00
Tasks: parameter estimation, Self-Supervised Learning, Translation

Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference to make it even faster by marginalizing symmetries in a physical problem. In this approach, physical symmetries, for example, time-translation are learned using joint-embedding via self-supervised learning with symmetry data augmentations. Subsequently, parameter inference is performed using a normalizing flow where the embedding network is used to summarize the data before conditioning the parameters. We present this approach on two simple physical problems and we show faster convergence in a smaller number of parameters compared to a normalizing flow that does not use a pre-trained symmetry-informed representation.
Kunyu: A High-Performing Global Weather Model Beyond Regression Losses
Authors: Zekun Ni
Date: 2023-12-04 00:00:00
Tasks: regression, Weather Forecasting

Over the past year, data-driven global weather forecasting has emerged as a new alternative to traditional numerical weather prediction. This innovative approach yields forecasts of comparable accuracy at a tiny fraction of computational costs. Regrettably, as far as I know, existing models exclusively rely on regression losses, producing forecasts with substantial blurring. Such blurring, although compromises practicality, enjoys an unfair advantage on evaluation metrics. In this paper, I present Kunyu, a global data-driven weather forecasting model which delivers accurate predictions across a comprehensive array of atmospheric variables at 0.35{\deg} resolution. With both regression and adversarial losses integrated in its training framework, Kunyu generates forecasts with enhanced clarity and realism. Its performance outpaces even ECMWF HRES in some aspects such as the estimation of anomaly extremes, while remaining competitive with ECMWF HRES on evaluation metrics such as RMSE and ACC. Kunyu is an important step forward in closing the utility gap between numerical and data-driven weather prediction.
Individual Updating of Subjective Probability of Homicide Victimization: a "Natural Experiment'' on Risk Communication
Authors: José Raimundo Carvalho, Diego de Maria André, Yuri Costa
Date: 2023-12-13 00:00:00

We investigate the dynamics of the update of subjective homicide victimization risk after an informational shock by developing two econometric models able to accommodate both optimal decisions of changing prior expectations which enable us to rationalize skeptical Bayesian agents with their disregard to new information. We apply our models to a unique household data (N = 4,030) that consists of socioeconomic and victimization expectation variables in Brazil, coupled with an informational ``natural experiment'' brought by the sample design methodology, which randomized interviewers to interviewees. The higher priors about their own subjective homicide victimization risk are set, the more likely individuals are to change their initial perceptions. In case of an update, we find that elders and females are more reluctant to change priors and choose the new response level. In addition, even though the respondents' level of education is not significant, the interviewers' level of education has a key role in changing and updating decisions. The results show that our econometric approach fits reasonable well the available empirical evidence, stressing the salient role heterogeneity represented by individual characteristics of interviewees and interviewers have on belief updating and lack of it, say, skepticism. Furthermore, we can rationalize skeptics through an informational quality/credibility argument.
Efficiency of QMLE for dynamic panel data models with interactive effects
Authors: Jushan Bai
Date: 2023-12-13 00:00:00

This paper studies the problem of efficient estimation of panel data models in the presence of an increasing number of incidental parameters. We formulate the dynamic panel as a simultaneous equations system, and derive the efficiency bound under the normality assumption. We then show that the Gaussian quasi-maximum likelihood estimator (QMLE) applied to the system achieves the normality efficiency bound without the normality assumption. Comparison of QMLE with the fixed effects approach is made.
New Kids on the Block: On the impact of information retrieval on contextual resource integration patterns
Authors: Martin Semmann, Mahei Manhei Li
Date: 2023-12-13 00:00:00
Tasks: Information Retrieval, Retrieval

The rise of new modes of interaction with AI skyrocketed the popularity, applicability, and amount of use cases. Despite this evolution, conceptual integration is falling behind. Studies suggest that there is hardly a systematization in using AI in organizations. Thus, by taking a service-dominant logic perspective, specifically, the concept of resource integration patterns, the most potent application of AI for organizational use - namely information retrieval - is analyzed. In doing so, we propose a systematization that can be applied to deepen understanding of core technical concepts, further investigate AI in contexts, and help explore research directions guided by SDL.
Intergenerational Equitable Climate Change Mitigation: Negative Effects of Stochastic Interest Rates; Positive Effects of Financing
Authors: Christian P. Fries, Lennart Quante
Date: 2023-12-11 00:00:00

Today's decisions on climate change mitigation affect the damage that future generations will bear. Discounting future benefits and costs of climate change mitigation is one of the most critical components of assessing efficient climate mitigation pathways. We extend the DICE model with stochastic discount rates to reflect the uncertain nature of discount rates. Stochastic rates give rise to a stochastic mitigation strategy, resulting in all model quantities becoming stochastic. We show that the optimization procedure of the DICE model induces intergenerational inequality: lacking a mechanism to regulate burden, future generations have to bear higher costs from abatement and damage relative to GDP. Further, we show that considering uncertainty of discount rates and their feedback to abatement policies, which can be interpreted as successive re-calculation, increases intergenerational inequality (and adds additional risks). Motivated by this, we consider additional financing risks by investigating two modifications of DICE. We find that allowing financing of abatement costs and considering non-linear financing effects for large damages improves intergenerational effort sharing. To conclude our discussion of options to improve intergenerational equity in an IAM, we propose a modified optimization to keep costs below 3% of GDP, resulting in more equal distribution of efforts between generations.
Optimal Information Acquisition Under Intervention
Authors: Augusto Nieto-Barthaburu
Date: 2023-12-12 00:00:00

We present a model of a forecaster who must predict the future value of a variable that depends on an exogenous state and on the intervention of a policymaker. Our focus is on the incentives of the forecaster to acquire costly private information to use in his forecasting exercise. We show that the policy-making environment plays a crucial role in determining the incentives of the forecaster to acquire information. Key parameters are the expected strength of policy intervention, the precision of the policymaker's private information, and the precision of public information. We identify conditions, which are plausible in applications, under which the forecaster optimally acquires little or no private information, and instead bases his forecast exclusively on information publicly known at the time the forecast is made. Furthermore we show that, also under plausible conditions, stronger policy intervention and more precise policymaker's information crowd-out forecaster's information acquisition.
Audio Deepfake Detection with Self-Supervised WavLM and Multi-Fusion Attentive Classifier
Authors: Yinlin Guo, Haofan Huang, Xi Chen, He Zhao, Yuehai Wang
Date: 2023-12-13 00:00:00
Tasks: Audio Deepfake Detection, DeepFake Detection

With the rapid development of speech synthesis and voice conversion technologies, Audio Deepfake has become a serious threat to the Automatic Speaker Verification (ASV) system. Numerous countermeasures are proposed to detect this type of attack. In this paper, we report our efforts to combine the self-supervised WavLM model and Multi-Fusion Attentive classifier for audio deepfake detection. Our method exploits the WavLM model to extract features that are more conducive to spoofing detection for the first time. Then, we propose a novel Multi-Fusion Attentive (MFA) classifier based on the Attentive Statistics Pooling (ASP) layer. The MFA captures the complementary information of audio features at both time and layer levels. Experiments demonstrate that our methods achieve state-of-the-art results on the ASVspoof 2021 DF set and provide competitive results on the ASVspoof 2019 and 2021 LA set.