pwc-archive/papers-with-abstracts huggingface dataset:
Contextual Bandits for Advertising Budget Allocation
Authors: Benjamin Han, Jared Gabor
Date: 2020-08-22 00:00:00
Proceeding: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2020 8
Tasks: Marketing, Multi-Armed Bandits, Thompson Sampling

When allocating budgets across different ad campaigns, advertisers confront the challenge that the payouts or returns are uncertain. In this paper, we describe a system for optimizing advertising campaign budgets to ensure long-term profitability in the face of this uncertainty. Our modified contextual bandit system 1) applies supervised learning to predict ad campaign payouts based on context features and historical performance; 2) extrapolates the payouts to out-of-sample budgets using a simple functional form for the distribution of payouts; then 3) uses Thompson Sampling from the predicted payout distributions to manage the explore-exploit trade-off when selecting budgets. Using our system, we measure an overall efficiency improvement of (22 ± 10)% in the mean Cost Per Acquisition over the previous budget allocation strategy using Markov Chain Monte-Carlo. This system is now responsible for managing hundreds of millions of dollars of annual marketing spend at Lyft.
Environmental chemical exposure dynamics and machine learning-based prediction of diabetes mellitus
Authors: Hongcheng Wei a, Jie Sun c, Wenqi Shan a, 1, Wenwen Xiao a, Bingqian Wang a, Xuan Ma a, Weiyue Hua, Xinru Wang a, Yankai Xia a, B, ⁎
Date: 2021-09-29 00:00:00
Proceeding: Science of the Total Environment 2021 9

Background: With dramatically increasing prevalence, diabetes mellitus has imposed a tremendous toll on indi-
vidual well-being. Humans are exposed to various environmental chemicals, which have been postulated as un-
derappreciated but potentially modifiable diabetes risk factors.
Objectives: To determine the utility of environmental chemical exposure in predicting diabetes mellitus.
Methods: A total of 8501 eligible participants from NHANES 2005–2016 were randomly assigned to a discovery
(N= 5953) set and a validation (N= 2548) set. We applied random forest (RF) and least absolute shrinkage
and selection operator (LASSO) regression with 10-fold cross-validation in the discovery set to select features,
and built an optimal model to predict diabetes mellitus, blood insulin, fasting plasma glucose (FPG) and 2-h
plasma glucose after oral glucose tolerance test (2-h PG after OGTT).
Results: The machine learning model using LASSO regression predicted diabetes with an area under the receiver
operating characteristics (AUROC) of0.80 and 0.78 in the discovery set and validation set, respectively. The linear
model predicted blood insulin level with an R2 of 0.42 and 0.40 in the discovery set and validation set, respec-
tively. For FPG, the discovery set and validation set yielded an R2 of 0.16 and 0.15, respectively. For 2-h PG
after OGTT, the discovery set and validation set yielded an R2 of 0.18 and 0.17, respectively.
Conclusion: We used environmental chemical exposure, constructedmachine learning models and achieved rel-
atively accurate prediction for diabetes, emphasizing the predictive value of widespread environmental
chemicals for complicated diseases.
Developing Question-Answering Models in Low-Resource Languages: A Case Study on Turkish Medical Texts Using Transformer-Based Approaches
Authors: Mert Incidelen, Murat Aydogan
Date: 2024-10-16 00:00:00
Proceeding: International Artificial Intelligence and Data Processing Symposium 2024 10
Tasks: Language Modeling, Language Modelling, Question Answering

In this study, transformer-based pre-trained language models were fine-tuned using medical texts for question-answering (QA) tasks in Turkish, a low-resource language. Variations of the BERTurk pre-trained language model created using large Turkish corpus were used for QA tasks. The study presents a medical Turkish QA dataset created using Turkish Wikipedia and medical theses located in the Thesis Center of the Council of Higher Education in Turkey. This dataset, containing a total of 8200 question-answer pairs, is used to fine-tune the BERTurk model. The performance of the models was evaluated by Exact Match (EM) and F1 score. The BERTurk (cased, 32k) model achieved an EM of 51.097 and an F1 score of 74.148, while the BERTurk (cased, 128 k) model achieved an EM of 55.121 and an F1 score of 77.187. The results show that pre-trained language models can be successfully used for question-answer tasks in low-resource languages such as Turkish. This study lays an important foundation for Turkish medical text processing and automatic QA tasks and sheds light on future research in this field.
Efficient Channel Estimation With Shorter Pilots in RIS-Aided Communications: Using Array Geometries and Interference Statistics
Authors: Özlem Tuğfe Demir, Emil Björnson, Luca Sanguinetti
Date: 2024-11-07 00:00:00

Accurate estimation of the cascaded channel from a user equipment (UE) to a base station (BS) via each reconfigurable intelligent surface (RIS) element is critical to realizing the full potential of the RIS's ability to control the overall channel. The number of parameters to be estimated is equal to the number of RIS elements, requiring an equal number of pilots unless an underlying structure can be identified. In this paper, we show how the spatial correlation inherent in the different RIS channels provides this desired structure. We first optimize the RIS phase-shift pattern using a much-reduced pilot length (determined by the rank of the spatial correlation matrices) to minimize the mean square error (MSE) in the channel estimation under electromagnetic interference. In addition to considering the linear minimum MSE (LMMSE) channel estimator, we propose a novel channel estimator that requires only knowledge of the array geometry while not requiring any user-specific statistical information. We call this the reduced-subspace least squares (RS-LS) estimator and optimize the RIS phase-shift pattern for it. This novel estimator significantly outperforms the conventional LS estimator. For both the LMMSE and RS-LS estimators, the proposed optimized RIS configurations result in significant channel estimation improvements over the benchmarks.
Large Intelligent Surfaces with Low-End Receivers: From Scaling to Antenna and Panel Selection
Authors: Ashkan Sheikhi, Juan Vidal Alegría, Ove Edfors
Date: 2024-11-07 00:00:00

We analyze the performance of large intelligent surface (LIS) with hardware distortion at its RX-chains. In particular, we consider the memory-less polynomial model for non-ideal hardware and derive analytical expressions for the signal to noise plus distortion ratio after applying maximum ratio combining (MRC) at the LIS. We also study the effect of back-off and automatic gain control on the RX-chains. The derived expressions enable us to evaluate the scalability of LIS when hardware impairments are present. We also study the cost of assuming ideal hardware by analyzing the minimum scaling required to achieve the same performance with a non-ideal hardware. Then, we exploit the analytical expressions to propose optimized antenna selection schemes for LIS and we show that such schemes can improve the performance significantly. In particular, the antenna selection schemes allow the LIS to have lower number of non-ideal RX-chains for signal reception while maintaining a good performance. We also consider a more practical case where the LIS is deployed as a grid of multi-antenna panels, and we propose panel selection schemes to optimize the complexity-performance trade-offs and improve the system overall efficiency.
Over-the-Air DPD and Reciprocity Calibration in Massive MIMO and Beyond
Authors: Ashkan Sheikhi, Ove Edfors, Juan Vidal Alegría
Date: 2024-11-07 00:00:00

In this paper we study an over-the-air (OTA) approach for digital pre-distortion (DPD) and reciprocity calibration in massive multiple-input-multiple-output systems. In particular, we consider a memory-less non-linearity model for the base station (BS) transmitters and propose a methodology to linearize the transmitters and perform the calibration by using mutual coupling OTA measurements between BS antennas. We show that by only using the OTA-based data, we can linearize the transmitters and design the calibration to compensate for both the non-linearity and non-reciprocity of BS transceivers effectively. This allows to alleviate the requirement to have dedicated hardware modules for transceiver characterization. Moreover, exploiting the results of the DPD linearization step, our calibration method may be formulated in terms of closed-form transformations, achieving a significant complexity reduction over state-of-the-art methods, which usually rely on costly iterative computations. Simulation results showcase the potential of our approach in terms of the calibration matrix estimation error and downlink data-rates when applying zero-forcing precoding after using our OTA-based DPD and calibration method.
Advancing Multi-Connectivity in Satellite-Terrestrial Integrated Networks: Architectures, Challenges, and Applications
Authors: Xiangyu Li, Bodong Shang
Date: 2024-11-07 00:00:00

Multi-connectivity (MC) in satellite-terrestrial integrated networks (STINs), included in the Third-Generation Partnership Project (3GPP) standards, is regarded as a promising technology for future networks, especially the non-terrestrial network (NTN). The significant advantages of MC in improving coverage, communication, and sensing through satellite-terrestrial collaboration have sparked widespread interest. This article introduces three fundamental deployment architectures of MC systems in STINs, including multi-satellite, single-satellite single-base-station, and multi-satellite multi-base-station configurations. Considering the emerging but still evolving satellite networking, we explore system design challenges such as satellite networking schemes, such as cell-free and multi-tier satellite networks. Subsequently, key technical challenges severely influencing the quality of mutual communications, including beamforming, channel estimation, and synchronization, are discussed. Furthermore, typical applications such as coverage enhancement, traffic offloading, collaborative sensing, and low-altitude communication are demonstrated, followed by a case study comparing coverage performance in MC and single-connectivity (SC) configurations. Several essential future research directions for MC in STINs are presented to facilitate further exploration.
Diffusion-based Auction Mechanism for Efficient Resource Management in 6G-enabled Vehicular Metaverses
Authors: Jiawen Kang, Yongju Tong, Yue Zhong, Junlong Chen, Minrui Xu, Dusit Niyato, Runrong Deng, Shiwen Mao
Date: 2024-11-01 00:00:00
Tasks: Management

The rise of 6G-enable Vehicular Metaverses is transforming the automotive industry by integrating immersive, real-time vehicular services through ultra-low latency and high bandwidth connectivity. In 6G-enable Vehicular Metaverses, vehicles are represented by Vehicle Twins (VTs), which serve as digital replicas of physical vehicles to support real-time vehicular applications such as large Artificial Intelligence (AI) model-based Augmented Reality (AR) navigation, called VT tasks. VT tasks are resource-intensive and need to be offloaded to ground Base Stations (BSs) for fast processing. However, high demand for VT tasks and limited resources of ground BSs, pose significant resource allocation challenges, particularly in densely populated urban areas like intersections. As a promising solution, Unmanned Aerial Vehicles (UAVs) act as aerial edge servers to dynamically assist ground BSs in handling VT tasks, relieving resource pressure on ground BSs. However, due to high mobility of UAVs, there exists information asymmetry regarding VT task demands between UAVs and ground BSs, resulting in inefficient resource allocation of UAVs. To address these challenges, we propose a learning-based Modified Second-Bid (MSB) auction mechanism to optimize resource allocation between ground BSs and UAVs by accounting for VT task latency and accuracy. Moreover, we design a diffusion-based reinforcement learning algorithm to optimize the price scaling factor, maximizing the total surplus of resource providers and minimizing VT task latency. Finally, simulation results demonstrate that the proposed diffusion-based MSB auction outperforms traditional baselines, providing better resource distribution and enhanced service quality for vehicular users.
NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation
Authors: Momin Haider, Ming Yin, Menglei Zhang, Arpit Gupta, Jing Zhu, Yu-Xiang Wang
Date: 2024-10-30 00:00:00
Tasks: D4RL, Management, Offline RL

Mobile devices such as smartphones, laptops, and tablets can often connect to multiple access networks (e.g., Wi-Fi, LTE, and 5G) simultaneously. Recent advancements facilitate seamless integration of these connections below the transport layer, enhancing the experience for apps that lack inherent multi-path support. This optimization hinges on dynamically determining the traffic distribution across networks for each device, a process referred to as \textit{multi-access traffic splitting}. This paper introduces \textit{NetworkGym}, a high-fidelity network environment simulator that facilitates generating multiple network traffic flows and multi-access traffic splitting. This simulator facilitates training and evaluating different RL-based solutions for the multi-access traffic splitting problem. Our initial explorations demonstrate that the majority of existing state-of-the-art offline RL algorithms (e.g. CQL) fail to outperform certain hand-crafted heuristic policies on average. This illustrates the urgent need to evaluate offline RL algorithms against a broader range of benchmarks, rather than relying solely on popular ones such as D4RL. We also propose an extension to the TD3+BC algorithm, named Pessimistic TD3 (PTD3), and demonstrate that it outperforms many state-of-the-art offline RL algorithms. PTD3's behavioral constraint mechanism, which relies on value-function pessimism, is theoretically motivated and relatively simple to implement.
Generative AI Enabled Matching for 6G Multiple Access
Authors: Xudong Wang, Hongyang Du, Dusit Niyato, Lijie Zhou, Lei Feng, Zhixiang Yang, Fanqin Zhou, Wenjing Li
Date: 2024-10-29 00:00:00

In wireless networks, applying deep learning models to solve matching problems between different entities has become a mainstream and effective approach. However, the complex network topology in 6G multiple access presents significant challenges for the real-time performance and stability of matching generation. Generative artificial intelligence (GenAI) has demonstrated strong capabilities in graph feature extraction, exploration, and generation, offering potential for graph-structured matching generation. In this paper, we propose a GenAI-enabled matching generation framework to support 6G multiple access. Specifically, we first summarize the classical matching theory, discuss common GenAI models and applications from the perspective of matching generation. Then, we propose a framework based on generative diffusion models (GDMs) that iteratively denoises toward reward maximization to generate a matching strategy that meets specific requirements. Experimental results show that, compared to decision-based AI approaches, our framework can generate more effective matching strategies based on given conditions and predefined rewards, helping to solve complex problems in 6G multiple access, such as task allocation.
A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning
Authors: Antonin Gagnere, Geoffroy Peeters, Slim Essid
Date: 2024-11-06 00:00:00
Tasks: Beat Tracking, Few-Shot Learning, Rhythm, Self-Supervised Learning

In this paper, we propose a novel Self-Supervised-Learning scheme to train rhythm analysis systems and instantiate it for few-shot beat tracking. Taking inspiration from the Contrastive Predictive Coding paradigm, we propose to train a Log-Mel-Spectrogram Transformer encoder to contrast observations at times separated by hypothesized beat intervals from those that are not. We do this without the knowledge of ground-truth tempo or beat positions, as we rely on the local maxima of a Predominant Local Pulse function, considered as a proxy for Tatum positions, to define candidate anchors, candidate positives (located at a distance of a power of two from the anchor) and negatives (remaining time positions). We show that a model pre-trained using this approach on the unlabeled FMA, MTT and MTG-Jamendo datasets can successfully be fine-tuned in the few-shot regime, i.e. with just a few annotated examples to get a competitive beat-tracking performance.
Voltage Support Capability Analysis of Grid-Forming Inverters with Current-Limiting Control Under Asymmetrical Grid Faults
Authors: Han Zhang, Rui Liu, Yunwei, Li
Date: 2024-11-07 00:00:00

Voltage support capability is critical for grid-forming (GFM) inverters with current-limiting control (CLC) during grid faults. Despite the findings on the voltage support for symmetrical grid faults, its applicability to more common but complex asymmetrical grid faults has yet to be verified rigorously. This letter fills the gap in the voltage support capability analysis for asymmetrical grid faults by establishing and analyzing positive- and negative-sequence equivalent circuit models, where the virtual impedance is adopted to emulate various CLCs. It is discovered that matching the phase angle of the virtual impedance, emulated by the CLC, with that of the composed impedance from the capacitor to the fault location can maximize the voltage support capability of GFM inverters under asymmetrical grid faults. Rigorous theoretical analysis and experimental results verify this conclusion.
Finding Control Invariant Sets via Lipschitz Constants of Linear Programs
Authors: Matti Vahs, Shaohang Han, Jana Tumova
Date: 2024-11-07 00:00:00
Tasks: valid

Control invariant sets play an important role in safety-critical control and find broad application in numerous fields such as obstacle avoidance for mobile robots. However, finding valid control invariant sets of dynamical systems under input limitations is notoriously difficult. We present an approach to safely expand an initial set while always guaranteeing that the set is control invariant. Specifically, we define an expansion law for the boundary of a set and check for control invariance using Linear Programs (LPs). To verify control invariance on a continuous domain, we leverage recently proposed Lipschitz constants of LPs to transform the problem of continuous verification into a finite number of LPs. Using concepts from differentiable optimization, we derive the safe expansion law of the control invariant set and show how it can be interpreted as a second invariance problem in the space of possible boundaries. Finally, we show how the obtained set can be used to obtain a minimally invasive safety filter in a Control Barrier Function (CBF) framework. Our work is supported by theoretical results as well as numerical examples.
A Continuification-Based Control Solution for Large-Scale Shepherding
Authors: Beniamino Di Lorenzo, Gian Carlo Maffettone, Mario di Bernardo
Date: 2024-11-07 00:00:00
Tasks: Mixed Reality

In this paper, we address the large-scale shepherding control problem using a continuification-based strategy. We consider a scenario in which a large group of follower agents (targets) must be confined within a designated goal region through indirect interactions with a controllable set of leader agents (herders). Our approach transforms the microscopic agent-based dynamics into a macroscopic continuum model via partial differential equations (PDEs). This formulation enables efficient, scalable control design for the herders' behavior, with guarantees of global convergence. Numerical and experimental validations in a mixed-reality swarm robotics framework demonstrate the method's effectiveness.
Distributed Attack-Resilient Platooning Against False Data Injection
Authors: Lorenzo Lyons, Manuel Boldrer, Laura Ferranti
Date: 2024-11-07 00:00:00

This paper presents a novel distributed vehicle platooning control and coordination strategy. We propose a distributed predecessor-follower CACC scheme that allows to choose an arbitrarily small inter-vehicle distance while guaranteeing no rear-end collisions occur, even in the presence of undetected cyber-attacks on the communication channels such as false data injection. The safety guarantees of the CACC policy are derived by combining a sensor-based ACC policy that explicitly accounts for actuator saturation, and a communication-based predictive term that has state-dependent limits on its control authority, thus containing the effects of an unreliable communication channel. An undetected attack may still however be able to degrade platooning performance. To mitigate it, we propose a tailored Kalman observer-based attack detection algorithm that initially triggers a switch from the CACC policy to the ACC policy. Subsequently, by relying on a high-level coordinator, our strategy allows to isolate a compromised vehicle from the platoon formation by reconfiguring the platoon topology itself. The coordinator can also handle merging and splitting requests. We compare our algorithm in an extensive simulation study against a state of the art distributed MPC scheme and a robust control scheme. We additionally extensively test our full method in practice on a real system, a team of scaled-down car-like robots. Furthermore, we share the code to run both the simulations and robotic experiments.
Convergence and Robustness of Value and Policy Iteration for the Linear Quadratic Regulator
Authors: Bowen Song, Chenxuan Wu, Andrea Iannelli
Date: 2024-11-07 00:00:00

This paper revisits and extends the convergence and robustness properties of value and policy iteration algorithms for discrete-time linear quadratic regulator problems. In the model-based case, we extend current results concerning the region of exponential convergence of both algorithms. In the case where there is uncertainty on the value of the system matrices, we provide input-to-state stability results capturing the effect of model parameter uncertainties. Our findings offer new insights into these algorithms at the heart of several approximate dynamic programming schemes, highlighting their convergence and robustness behaviors. Numerical examples illustrate the significance of some of the theoretical results.
Sliding Mode Roll Control of Active Suspension Electric Vehicles
Authors: Mruganka Kashyap
Date: 2024-11-07 00:00:00

Vehicle roll control has been a well studied problem. One of the ubiquitous methods to mitigate vehicle rollover in the automobile industry is via a mechanical anti-roll bar. However with the advent of electric vehicles, rollover mitigation can be pursued using electric actuation. In this work, we study a roll control algorithm using sliding mode control for active suspension vehicles, where the actuation for the roll control signal is generated by electric motors independently at the four corners of the vehicle. This technology precludes the need for any mechanical actuation which is often slower as well as any anti-roll bar to mitigate vehicle rollover situations. We provide an implementation of the proposed algorithm and conduct numerical experiments to validate the functionality and effectiveness. Specifically, we perform Slalom and J-turn maneuvering tests on an active suspension electric vehicle with sliding model roll control and it is shown to mitigate rollover by atleast 50% compared to passive suspension vehicles, while simultaneously maintaining rider comfort.