pwc-archive/papers-with-abstracts huggingface dataset:
Consumer-Optimal Segmentation in Multi-Product Markets
Authors: Dirk Bergemann, Tibor Heumann, Michael C. Wang
Date: 2024-01-22 00:00:00
Tasks: Segmentation

We analyze how market segmentation affects consumer welfare when a monopolist can engage in both second-degree price discrimination (through product differentiation) and third-degree price discrimination (through market segmentation). We characterize the consumer-optimal market segmentation and show that it has several striking properties: (1) the market segmentation displays monotonicity$\unicode{x2014}$higher-value customers always receive higher quality product than lower-value regardless of their segment and across any segment; and (2) when aggregate demand elasticity exceeds a threshold determined by marginal costs, no segmentation maximizes consumer surplus. Our results demonstrate that strategic market segmentation can benefit consumers even when it enables price discrimination, but these benefits depend critically on demand elasticities and cost structures. The findings have implications for regulatory policy regarding price discrimination and market segmentation practices.
Enhancements for 5G NR PRACH Reception: An AI/ML Approach
Authors: Rohit Singh, Anil Kumar Yerrapragada, Jeeva Keshav S, Radha Krishna Ganti
Date: 2024-01-12 00:00:00

Random Access is an important step in enabling the initial attachment of a User Equipment (UE) to a Base Station (gNB). The UE identifies itself by embedding a Preamble Index (RAPID) in the phase rotation of a known base sequence, which it transmits on the Physical Random Access Channel (PRACH). The signal on the PRACH also enables the estimation of propagation delay, often known as Timing Advance (TA), which is induced by virtue of the UE's position. Traditional receivers estimate the RAPID and TA using correlation-based techniques. This paper presents an alternative receiver approach that uses AI/ML models, wherein two neural networks are proposed, one for the RAPID and one for the TA. Different from other works, these two models can run in parallel as opposed to sequentially. Experiments with both simulated data and over-the-air hardware captures highlight the improved performance of the proposed AI/ML-based techniques compared to conventional correlation methods.
CoAVT: A Cognition-Inspired Unified Audio-Visual-Text Pre-Training Model for Multimodal Processing
Authors: Xianghu Yue, Xiaohai Tian, Lu Lu, Malu Zhang, Zhizheng Wu, Haizhou Li
Date: 2024-01-22 00:00:00
Tasks: AudioCaps, Audio-Visual Synchronization, Language Modeling, Language Modelling, Representation Learning, Retrieval, Video Retrieval

There has been a long-standing quest for a unified audio-visual-text model to enable various multimodal understanding tasks, which mimics the listening, seeing and reading process of human beings. Humans tends to represent knowledge using two separate systems: one for representing verbal (textual) information and one for representing non-verbal (visual and auditory) information. These two systems can operate independently but can also interact with each other. Motivated by this understanding of human cognition, in this paper, we introduce CoAVT -- a novel cognition-inspired Correlated Audio-Visual-Text pre-training model to connect the three modalities. It contains a joint audio-visual encoder that learns to encode audio-visual synchronization information together with the audio and visual content for non-verbal information, and a text encoder to handle textual input for verbal information. To bridge the gap between modalities, CoAVT employs a query encoder, which contains a set of learnable query embeddings, and extracts the most informative audiovisual features of the corresponding text. Additionally, to leverage the correspondences between audio and vision with language respectively, we also establish the audio-text and visual-text bi-modal alignments upon the foundational audiovisual-text tri-modal alignment to enhance the multimodal representation learning. Finally, we jointly optimize CoAVT model with three multimodal objectives: contrastive loss, matching loss and language modeling loss. Extensive experiments show that CoAVT can learn strong multimodal correlations and be generalized to various downstream tasks. CoAVT establishes new state-of-the-art performance on text-video retrieval task on AudioCaps for both zero-shot and fine-tuning settings, audio-visual event classification and audio-visual retrieval tasks on AudioSet and VGGSound.
Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems
Authors: Lorenzo Cazzella, Marouan Mizmizi, Dario Tagliaferri, Damiano Badini, Matteo Matteucci, Umberto Spagnolini
Date: 2024-01-11 00:00:00
Tasks: Beam Prediction, Integrated sensing and communication, ISAC

In Integrated Sensing and Communication (ISAC) systems, matching the radar targets with communication user equipments (UEs) is functional to several communication tasks, such as proactive handover and beam prediction. In this paper, we consider a radar-assisted communication system where a base station (BS) is equipped with a multiple-input-multiple-output (MIMO) radar that has a double aim: (i) associate vehicular radar targets to vehicular equipments (VEs) in the communication beamspace and (ii) predict the beamforming vector for each VE from radar data. The proposed target-to-user (T2U) association consists of two stages. First, vehicular radar targets are detected from range-angle images, and, for each, a beamforming vector is estimated. Then, the inferred per-target beamforming vectors are matched with the ones utilized at the BS for communication to perform target-to-user (T2U) association. Joint multi-target detection and beam inference is obtained by modifying the you only look once (YOLO) model, which is trained over simulated range-angle radar images. Simulation results over different urban vehicular mobility scenarios show that the proposed T2U method provides a probability of correct association that increases with the size of the BS antenna array, highlighting the respective increase of the separability of the VEs in the beamspace. Moreover, we show that the modified YOLO architecture can effectively perform both beam prediction and radar target detection, with similar performance in mean average precision on the latter over different antenna array sizes.
Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications
Authors: Nazmul Islam, Seokjoo Shin
Date: 2024-01-08 00:00:00
Tasks: Intelligent Communication, Representation Learning, Semantic Communication

Deep learning (DL) has revolutionized wireless communication systems by introducing datadriven end-to-end (E2E) learning, where the physical layer (PHY) is transformed into DL architectures to achieve peak optimization. Leveraging DL for E2E optimization in PHY significantly enhances its adaptability and performance in complex wireless environments, meeting the demands of advanced network systems such as 5G and beyond. Furthermore, this evolution of data-driven PHY optimization has also enabled advanced semantic applications across various modalities, including text, image, audio, video, and multimodal transmissions. These applications elevate communication from bit-level to semantic-level intelligence, making it capable of discerning context and intent. Although the PHY, as a DL architecture, plays a crucial role in enabling semantic communication (SemCom) systems, comprehensive studies that integrate both E2E communication and SemCom systems remain significantly underexplored. This highlights the novelty and potential of these integrative fields, marking them as a promising research domain. Therefore, this article provides a comprehensive review of the emerging field of data-driven PHY for E2E communication systems, emphasizing their role in enabling semantic applications across various modalities. It also identifies key challenges and potential research directions, serving as a crucial guide for future advancements in DL for E2E communication and SemCom systems.
Boosting Unknown-number Speaker Separation with Transformer Decoder-based Attractor
Authors: Younglo Lee, Shukjae Choi, Byeong-Yeol Kim, Zhong-Qiu Wang, Shinji Watanabe
Date: 2024-01-23 00:00:00
Tasks: Decoder, Speaker Separation, Speech Separation

We propose a novel speech separation model designed to separate mixtures with an unknown number of speakers. The proposed model stacks 1) a dual-path processing block that can model spectro-temporal patterns, 2) a transformer decoder-based attractor (TDA) calculation module that can deal with an unknown number of speakers, and 3) triple-path processing blocks that can model inter-speaker relations. Given a fixed, small set of learned speaker queries and the mixture embedding produced by the dual-path blocks, TDA infers the relations of these queries and generates an attractor vector for each speaker. The estimated attractors are then combined with the mixture embedding by feature-wise linear modulation conditioning, creating a speaker dimension. The mixture embedding, conditioned with speaker information produced by TDA, is fed to the final triple-path blocks, which augment the dual-path blocks with an additional pathway dedicated to inter-speaker processing. The proposed approach outperforms the previous best reported in the literature, achieving 24.0 and 23.7 dB SI-SDR improvement (SI-SDRi) on WSJ0-2 and 3mix respectively, with a single model trained to separate 2- and 3-speaker mixtures. The proposed model also exhibits strong performance and generalizability at counting sources and separating mixtures with up to 5 speakers.
COOCK project Smart Port 2025 D3.1: "To Twin Or Not To Twin"
Authors: Randy Paredis, Hans Vangheluwe, Pamela Adelino Ramos Albertins
Date: 2024-01-23 00:00:00

This document is a result of the COOCK project "Smart Port 2025: improving and accelerating the operational efficiency of a harbour eco-system through the application of intelligent technologies". It reports on the needs of companies for modelling and simulation and AI-based techniques, with twinning systems in particular. This document categorizes the purposes and Properties of Interest for the use of Digital Twins. It further illustrates some of the twinning usages, and touches on some of the potential architectural compositions for twins. This last topic will be further elaborated in a followup report.
On Simplified 3D Finite Element Simulations of Three-core Armored Power Cables
Authors: Juan Carlos del-Pino-López, Marius Hatlo, Pedro Cruz-Romero
Date: 2024-01-23 00:00:00

This paper analyzes different ways to simulate electromagnetically three-core armored cables in 3D by means of the finite element method. Full periodic models, as lengthy as 36 m, are developed to evaluate the accuracy when simulating only a small portion of the cable, as commonly employed in the literature. The adequate length and boundary conditions for having the same accuracy of full periodic models are also studied. To this aim, five medium voltage and high voltage armored cables are analyzed, obtaining the minimum length of the cable that may be simulated for having accurate results in shorter time and with less computational burden. This also results in the proposal of a new method comprising the advantages of short geometries and the applicability of periodic boundary conditions. Its accuracy is compared with experimental measurements and the IEC standard for 145 kV and 245 kV cables. The results show a very good agreement between simulations and measurements (errors below 4 %), obtaining a reduction in the computation time of about 90 %. This new method brings a more effective tool for saving time and computational resources in cable design and the development of new analytical expressions for improving the IEC standard.
Localized Data-driven Consensus Control
Authors: Zeze Chang, Junjie Jiao, Zhongkui Li
Date: 2024-01-23 00:00:00

This paper considers a localized data-driven consensus problem for leader-follower multi-agent systems with unknown discrete-time agent dynamics, where each follower computes its local control gain using only their locally collected state and input data. Both noiseless and noisy data-driven consensus protocols are presented, which can handle the challenge of the heterogeneity in control gains caused by the localized data sampling and achieve leader-follower consensus. The design of these data-driven consensus protocols involves low-dimensional linear matrix inequalities. In addition, the results are extended to the case where only the leader's data are collected and exploited. The effectiveness of the proposed methods is illustrated via simulation examples.
Learning the cost-to-go for mixed-integer nonlinear model predictive control
Authors: Christopher A. Orrico, W. P. M. H. Heemels, Dinesh Krishnamoorthy
Date: 2024-01-23 00:00:00
Tasks: Model Predictive Control, Prediction

Application of nonlinear model predictive control (NMPC) to problems with hybrid dynamical systems, disjoint constraints, or discrete controls often results in mixed-integer formulations with both continuous and discrete decision variables. However, solving mixed-integer nonlinear programming problems (MINLP) in real-time is challenging, which can be a limiting factor in many applications. To address the computational complexity of solving mixed integer nonlinear model predictive control problem in real-time, this paper proposes an approximate mixed integer NMPC formulation based on value function approximation. Leveraging Bellman's principle of optimality, the key idea here is to divide the prediction horizon into two parts, where the optimal value function of the latter part of the prediction horizon is approximated offline using expert demonstrations. Doing so allows us to solve the MINMPC problem with a considerably shorter prediction horizon online, thereby reducing the online computation cost. The paper uses an inverted pendulum example with discrete controls to illustrate this approach.
Minimum observability of probabilistic Boolean networks
Authors: Jiayi Xu, Shihua Fu, Liyuan Xia, Jianjun Wang
Date: 2024-01-23 00:00:00

This paper studies the minimum observability of probabilistic Boolean networks (PBNs), the main objective of which is to add the fewest measurements to make an unobservable PBN become observable. First of all, the algebraic form of a PBN is established with the help of semi-tensor product (STP) of matrices. By combining the algebraic forms of two identical PBNs into a parallel system, a method to search the states that need to be H-distinguishable is proposed based on the robust set reachability technique. Secondly, a necessary and sufficient condition is given to find the minimum measurements such that a given set can be H-distinguishable. Moreover, by comparing the numbers of measurements for all the feasible H-distinguishable state sets, the least measurements that make the system observable are gained. Finally, an example is given to verify the validity of the obtained results.
Power System Resource Expansion Planning
Authors: Sohom Datta
Date: 2024-01-20 00:00:00
Tasks: Evolutionary Algorithms

Power System Resource Planning is the recurrent process of studying and determining what facilities and procedures should be provided to satisfy and promote appropriate future demands for electricity. The electric power system as planned should meet or balance societal goals. These include availability of electricity to all potential users at lowest possible cost, minimum environmental damage, high levels of safety and reliability, etc. Plans should be technically and financially feasible. Plans also should achieve the objectives the entity doing the planning, including minimizing risk. The emergence of meta-heuristics has given robustness to the non-analytical methods, because of the rationale behind them. Besides, evolutionary algorithms have provided a higher degree of confidence in a stochastic convergence to optimum and have supported this confidence with a mathematical background explaining not only how they achieve convergence but also how to improve the convergence rate. The present work of analyses and implementation can be divided into: i) Transmission Constrained Generation Expansion Planning (TC GEP), ii) Composite Generation Expansion and Transmission Network Expansion Planning (GEP TNEP), iii) Transmission Network Expansion (TNEP) Planning using AC model, iv) Composite Transmission Network Expansion Planning (TNEP) and Reactive Power Expansion Planning (RPP) and v) Transmission Network Planning using Interior-Point Method (IP TNEP).
Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning
Authors: Bairong Deng, Tao Yu, Zhenning Pan, Xuehan Zhang, Yufeng Wu, Qiaoyi Ding
Date: 2024-01-19 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Sequential Decision Making

Reinforcement learning is an emerging approaches to facilitate multi-stage sequential decision-making problems. This paper studies a real-time multi-stage stochastic power dispatch considering multivariate uncertainties. Current researches suffer from low generalization and practicality, that is, the learned dispatch policy can only handle a specific dispatch scenario, its performance degrades significantly if actual samples and training samples are inconsistent. To fill these gaps, a novel contextual meta graph reinforcement learning (Meta-GRL) for a highly generalized multi-stage optimal dispatch policy is proposed. Specifically, a more general contextual Markov decision process (MDP) and scalable graph representation are introduced to achieve a more generalized multi-stage stochastic power dispatch modeling. An upper meta-learner is proposed to encode context for different dispatch scenarios and learn how to achieve dispatch task identification while the lower policy learner learns context-specified dispatch policy. After sufficient offline learning, this approach can rapidly adapt to unseen and undefined scenarios with only a few updations of the hypothesis judgments generated by the meta-learner. Numerical comparisons with state-of-the-art policies and traditional reinforcement learning verify the optimality, efficiency, adaptability, and scalability of the proposed Meta-GRL.
Hypochaos prevents tragedy of the commons in discrete-time eco-evolutionary game dynamics
Authors: Samrat Sohel Mondal, Avishuman Ray, Sagar Chakraborty
Date: 2024-01-20 00:00:00

While quite a few recent papers have explored game-resource feedback using the framework of evolutionary game theory, almost all the studies are confined to using time-continuous dynamical equations. Moreover, in such literature, the effect of ubiquitous chaos in the resulting eco-evolutionary dynamics is rather missing. Here, we present a deterministic eco-evolutionary discrete-time dynamics in generation-wise non-overlapping population of two types of harvesters, one harvesting at a faster rate than the other, consuming a self-renewing resource capable of showing chaotic dynamics. In the light of our finding that sometimes chaos is confined exclusively to either the dynamics of the resource or that of the consumer fractions, an interesting scenario is realized: The resource state can keep oscillating chaotically, and hence, it does not vanish to result in the tragedy of the commons, extinction of the resource due to selfish indiscriminate exploitation, and yet the consumer population, whose dynamics depends directly on the state of the resource, may end up being composed exclusively of defectors, i.e., high harvesters. This appears non-intuitive because it is well known that prevention of tragedy of the commons usually requires substantial cooperation to be present.
Interpreting Event-Studies from Recent Difference-in-Differences Methods
Authors: Jonathan Roth
Date: 2024-01-22 00:00:00

This note discusses the interpretation of event-study plots produced by recent difference-in-differences methods. I show that even when specialized to the case of non-staggered treatment timing, the default plots produced by software for three of the most popular recent methods (de Chaisemartin and D'Haultfoeuille, 2020; Callaway and SantAnna, 2021; Borusyak, Jaravel and Spiess, 2024) do not match those of traditional two-way fixed effects (TWFE) event-studies: the new methods may show a kink or jump at the time of treatment even when the TWFE event-study shows a straight line. This difference stems from the fact that the new methods construct the pre-treatment coefficients asymmetrically from the post-treatment coefficients. As a result, visual heuristics for analyzing TWFE event-study plots should not be immediately applied to those from these methods. I conclude with practical recommendations for constructing and interpreting event-study plots when using these methods.
Generative AI Triggers Welfare-Reducing Decisions in Humans
Authors: Fabian Dvorak, Regina Stumpf, Sebastian Fehrler, Urs Fischbacher
Date: 2024-01-23 00:00:00
Tasks: Fairness

Generative artificial intelligence (AI) is poised to reshape the way individuals communicate and interact. While this form of AI has the potential to efficiently make numerous human decisions, there is limited understanding of how individuals respond to its use in social interaction. In particular, it remains unclear how individuals engage with algorithms when the interaction entails consequences for other people. Here, we report the results of a large-scale pre-registered online experiment (N = 3,552) indicating diminished fairness, trust, trustworthiness, cooperation, and coordination by human players in economic twoplayer games, when the decision of the interaction partner is taken over by ChatGPT. On the contrary, we observe no adverse welfare effects when individuals are uncertain about whether they are interacting with a human or generative AI. Therefore, the promotion of AI transparency, often suggested as a solution to mitigate the negative impacts of generative AI on society, shows a detrimental effect on welfare in our study. Concurrently, participants frequently delegate decisions to ChatGPT, particularly when the AI's involvement is undisclosed, and individuals struggle to discern between AI and human decisions.
Business Model Contributions to Bank Profit Performance: A Machine Learning Approach
Authors: F. Bolivar, Miguel A. Duran, A. Lozano-Vivas
Date: 2024-01-22 00:00:00

This paper analyzes the relation between bank profit performance and business models. Using a machine learning-based approach, we propose a methodological strategy in which balance sheet components' contributions to profitability are the identification instruments of business models. We apply this strategy to the European Union banking system from 1997 to 2021. Our main findings indicate that the standard retail-oriented business model is the profile that performs best in terms of profitability, whereas adopting a non-specialized business profile is a strategic decision that leads to poor profitability. Additionally, our findings suggest that the effect of high capital ratios on profitability depends on the business profile. The contributions of business models to profitability decreased during the Great Recession. Although the situation showed signs of improvement afterward, the European Union banking system's ability to yield returns is still problematic in the post-crisis period, even for the best-performing group.