pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification
Authors: Mingsong Li, Wei Li, Yikun Liu, Yuwen Huang, and Gongping Yang.
Date: 2023-04-07 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2023 4
Tasks: Hyperspectral image analysis, Hyperspectral Image Classification, Hyperspectral Image Segmentation, image-classification, Image Classification

For the abundant spectral and spatial information recorded in hyperspectral images (HSIs), fully exploring spectral-spatial relationships has attracted widespread attention in hyperspectral image classification (HSIC) community. However, there are still some intractable obstructs. For one thing, in the patch-based processing pattern, some spatial neighbor pixels are often inconsistent with the central pixel in land-cover class. For another thing, linear and nonlinear correlations between different spectral bands are vital yet tough for representing and excavating. To overcome these mentioned issues, an adaptive mask sampling and manifold to Euclidean subspace learning (AMS-M2ESL) framework is proposed for HSIC. Specifically, an adaptive mask based intra-patch sampling (AMIPS) module is firstly formulated for intra-patch sampling in an adaptive mask manner based on central spectral vector oriented spatial relationships. Subsequently, based on distance covariance  descriptor, a dual channel distance covariance representation (DC-DCR) module is proposed for modeling unified spectral-spatial feature representations and exploring spectral-spatial relationships, especially linear and nonlinear interdependence in spectral domain. Furthermore, considering that distance covariance matrix lies on the symmetric positive definite (SPD) manifold, we implement a manifold to Euclidean subspace learning (M2ESL) module respecting Riemannian geometry of SPD manifold for high-level spectral-spatial feature learning. Additionally, we introduce an approximate matrix square-root (ASQRT) layer for efficient Euclidean subspace projection. Extensive experimental results on three popular HSI data sets with limited training samples demonstrate the superior performance of the proposed method compared with other state-of-the-art methods. The source code is available at https://github.com/lms-07/AMS-M2ESL.
Applications of Machine Learning in Fintech Credit Card Fraud Detection
Authors: Lacruz, F., Saniie, J.
Date: 2021-07-26 00:00:00
Proceeding: IEEE International Conference on Electro Information Technology (EIT) 2021 7
Tasks: Fraud Detection, regression

Fintech utilizes innovative technology to offer 
improved monetary administrations and financial solutions. 
According to data from the prediction of Autonomous Research 
artificial intelligence (AI) technologies will allow financial 
institutions to reduce their operational costs by 22% by 2030. 
Throughout this paper, we study how AI and machine learning 
algorithms can lead to credit card fraud detection. After making 
the theoretical approach to the subject, we develop two different 
methods Autoencoder (semi-supervised learning) and Logistic 
Regression (supervised learning) for fraud detection with a high 
level of accuracy. The results obtained with both methods are 
promising as we were able to predict fraud transactions with 94% 
certainty.
Neural Network Approach to Portfolio Optimization with Leverage Constraints:a Case Study on High Inflation Investment
Authors: Chendi Ni, Yuying Li, Peter A. Forsyth
Date: 2023-04-11 00:00:00
Tasks: Portfolio Optimization

Motivated by the current global high inflation scenario, we aim to discover a dynamic multi-period allocation strategy to optimally outperform a passive benchmark while adhering to a bounded leverage limit. To this end, we formulate an optimal control problem to outperform a benchmark portfolio throughout the investment horizon. Assuming the asset prices follow the jump-diffusion model during high inflation periods, we first establish a closed-form solution for the optimal strategy that outperforms a passive strategy under the cumulative quadratic tracking difference (CD) objective, assuming continuous trading and no bankruptcy. To obtain strategies under the bounded leverage constraint among other realistic constraints, we then propose a novel leverage-feasible neural network (LFNN) to represent control, which converts the original constrained optimization problem into an unconstrained optimization problem that is computationally feasible with standard optimization methods. We establish mathematically that the LFNN approximation can yield a solution that is arbitrarily close to the solution of the original optimal control problem with bounded leverage. We further apply the LFNN approach to a four-asset investment scenario with bootstrap resampled asset returns from the filtered high inflation regime data. The LFNN strategy is shown to consistently outperform the passive benchmark strategy by about 200 bps (median annualized return), with a greater than 90% probability of outperforming the benchmark at the end of the investment horizon.
Contingent Fees in Order Flow Auctions
Authors: Max Resnick
Date: 2023-04-11 00:00:00

Many early order flow auction designs handle the payment for orders when they execute on the chain rather than when they are won in the auction. Payments in these auctions only take place when the orders are executed, creating a free option for whoever wins the order. Bids in these auctions set the strike price of this option rather than the option premium. This paper develops a simple model of an order flow auction and compares contingent fees with upfront payments as well as mixtures of the two. Results suggest that auctions with a greater share of the payment contingent on execution have lower execution probability, lower revenue, and increased effective spreads in equilibrium. A Reputation system can act as a negative contingent fee, partially mitigating the downsides; however, unless the system is calibrated perfectly, some of the undesirable qualities of the contingent fees remain. Results suggest that designers of order flow auctions should avoid contingent fees whenever possible.
$\textit{e-Uber}$: A Crowdsourcing Platform for Electric Vehicle-based Ride- and Energy-sharing
Authors: Ashutosh Timilsina, Simone Silvestri
Date: 2023-03-31 00:00:00
Tasks: energy trading, reinforcement-learning

The sharing-economy-based business model has recently seen success in the transportation and accommodation sectors with companies like Uber and Airbnb. There is growing interest in applying this model to energy systems, with modalities like peer-to-peer (P2P) Energy Trading, Electric Vehicles (EV)-based Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and Battery Swapping Technology (BST). In this work, we exploit the increasing diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly enables ride-sharing and energy-sharing through V2G and BST. e-Uber exploits spatial crowdsourcing, reinforcement learning, and reverse auction theory. Specifically, the platform uses reinforcement learning to understand the drivers' preferences towards different ride-sharing and energy-sharing tasks. Based on these preferences, a personalized list is recommended to each driver through CMAB-based Algorithm for task Recommendation System (CARS). Drivers bid on their preferred tasks in their list in a reverse auction fashion. Then e-Uber solves the task assignment optimization problem that minimizes cost and guarantees V2G energy requirement. We prove that this problem is NP-hard and introduce a bipartite matching-inspired heuristic, Bipartite Matching-based Winner selection (BMW), that has polynomial time complexity. Results from experiments using real data from NYC taxi trips and energy consumption show that e-Uber performs close to the optimum and finds better solutions compared to a state-of-the-art approach
Holographic MIMO Communications with Arbitrary Surface Placements: Near-Field LoS Channel Model and Capacity Limit
Authors: Tierui Gong, Li Wei, Chongwen Huang, Zhijia Yang, Jiguang He, Mérouane Debbah, Chau Yuen
Date: 2023-04-11 00:00:00

Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits. Empowered by the nearly continuous, large and energy-efficient surfaces with powerful electromagnetic (EM) wave control capabilities, H-MIMO opens up the opportunity for signal processing in a more fundamental EM-domain, paving the way for realizing holographic imaging level communications in supporting the extremely high spectral efficiency and energy efficiency in future networks. In this article, we try to implement a generalized EM-domain near-field channel modeling and study its capacity limit of point-to-point H-MIMO systems that equips arbitrarily placed surfaces in a line-of-sight (LoS) environment. Two effective and computational-efficient channel models are established from their integral counterpart, where one is with a sophisticated formula but showcases more accurate, and another is concise with a slight precision sacrifice. Furthermore, we unveil the capacity limit using our channel model, and derive a tight upper bound based upon an elaborately built analytical framework. Our result reveals that the capacity limit grows logarithmically with the product of transmit element area, receive element area, and the combined effects of $1/{{d}_{mn}^2}$, $1/{{d}_{mn}^4}$, and $1/{{d}_{mn}^6}$ over all transmit and receive antenna elements, where $d_{mn}$ indicates the distance between each transmit and receive elements. Numerical evaluations validate the effectiveness of our channel models, and showcase the slight disparity between the upper bound and the exact capacity, which is beneficial for predicting practical system performance.
Phase Calibration of Distributed Antenna Arrays
Authors: Erik G. Larsson, Joao Vieira
Date: 2023-04-11 00:00:00

Antenna arrays can be either reciprocity calibrated (R-calibrated), which facilitates reciprocity-based beamforming, or fully calibrated (F-calibrated), which additionally facilitates transmission and reception in specific physical directions. We first expose, to provide context, the fundamental principles of over-the-air R- and F-calibration of distributed arrays. We then describe a new method for calibration of two arrays that are individually F-calibrated, such that the combined array becomes jointly F-calibrated.
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas
Authors: Mohamed Tarek, Jose Storopoli, Casey Davis, Chris Elrod, Julius Krumbiegel, Chris Rackauckas, Vijay Ivaturi
Date: 2023-03-31 00:00:00
Tasks: Bayesian Inference

This paper provides a comprehensive tutorial for Bayesian practitioners in pharmacometrics using Pumas workflows. We start by giving a brief motivation of Bayesian inference for pharmacometrics highlighting limitations in existing software that Pumas addresses. We then follow by a description of all the steps of a standard Bayesian workflow for pharmacometrics using code snippets and examples. This includes: model definition, prior selection, sampling from the posterior, prior and posterior simulations and predictions, counter-factual simulations and predictions, convergence diagnostics, visual predictive checks, and finally model comparison with cross-validation. Finally, the background and intuition behind many advanced concepts in Bayesian statistics are explained in simple language. This includes many important ideas and precautions that users need to keep in mind when performing Bayesian analysis. Many of the algorithms, codes, and ideas presented in this paper are highly applicable to clinical research and statistical learning at large but we chose to focus our discussions on pharmacometrics in this paper to have a narrower scope in mind and given the nature of Pumas as a software primarily for pharmacometricians.
Porównanie metod detekcji zajętości widma radiowego z wykorzystaniem uczenia federacyjnego z oraz bez węzła centralnego
Authors: Łukasz Kułacz
Date: 2023-03-31 00:00:00

Dynamic spectrum access systems typically require information about the spectrum occupancy and thus the presence of other users in order to make a spectrum al-location decision for a new device. Simple methods of spectrum occupancy detection are often far from reliable, hence spectrum occupancy detection algorithms supported by machine learning or artificial intelligence are often and successfully used. To protect the privacy of user data and to reduce the amount of control data, an interesting approach is to use federated machine learning. This paper compares two approaches to system design using federated machine learning: with and without a central node.
Adapting to Disruptions: Flexibility as a Pillar of Supply Chain Resilience
Authors: Ambra Amico, Luca Verginer, Giona Casiraghi, Giacomo Vaccario, Frank Schweitzer
Date: 2023-04-11 00:00:00

Supply chain disruptions cause shortages of raw material and products. To increase resilience, i.e., the ability to cope with shocks, substituting goods in established supply chains can become an effective alternative to creating new distribution links. We demonstrate its impact on supply deficits through a detailed analysis of the US opioid distribution system. Reconstructing 40 billion empirical distribution paths, our data-driven model allows a unique inspection of policies that increase the substitution flexibility. Our approach enables policymakers to quantify the trade-off between increasing flexibility, i.e., reduced supply deficits, and increasing complexity of the supply chain, which could make it more expensive to operate.
Coordination for Connected Automated Vehicles at Merging Roadways in Mixed Traffic Environment
Authors: Viet-Anh Le, Hao M. Wang, Gabor Orosz, Andreas A. Malikopoulos
Date: 2023-04-11 00:00:00

In this paper, we present an optimal control framework to address motion coordination of connected automated vehicles (CAVs) in the presence of human-driven vehicles (HDVs) in merging scenarios. Our framework combines an unconstrained trajectory solution of a low-level energy-optimal control problem with an upper-level optimization problem that yields the minimum travel time for CAVs. We predict the future trajectories of the HDVs using Newell's car-following model. To handle potential deviations of HDVs' actual behavior from the predicted one, we design a safety filter for CAVs based on control barrier functions. The effectiveness of the proposed control framework is demonstrated via simulations with heterogeneous human driving behaviors.
Stability/instability study of density systems and control law design
Authors: Igor Furtat
Date: 2023-04-11 00:00:00

The paper considers some class of dynamical systems that called density systems. For such systems the derivative of quadratic function depends on so-called density function. The density function is used to set the properties of phase space, therefore, it influences the behaviour of investigated systems. A particular class of such systems is previously considered for (in)stability study of dynamical systems using the flow and divergence of a phase vector. In this paper, a more general class of such systems is considered, and it is shown that the density function can be used not only to study (in)stability, but also to set the properties of space in order to change the behaviour of dynamical systems. The development of control laws based on use the density function for systems with known and unknown parameters is considered. All obtained results are accompanied by the simulations illustrating the theoretical conclusions.
Coverage analysis of Tethered UAV-assisted Large Scale Cellular Networks
Authors: Safa Khemiri, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2023-04-11 00:00:00

One of the major challenges slowing down the use of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is the limited on-board power supply which reduces the UAV's flight time. Using a tether to provide UAVs with power can be considered a reasonable compromise that will enhance the flight time while limiting the UAV's mobility. In this work, we propose a system where ABSs are deployed at the centers of user hotspots to offload the traffic and assist terrestrial base stations (TBSs). Firstly, given the location of the ground station in the user hotspot (user cluster) and the users spatial distribution, we compute the optimal inclination angle and length of the tether. Using these results, we compute the densities of the tethered UAVs deployed at different altitudes, which enables tractable analysis of the interference in the considered setup. Next, using tools from stochastic geometry and an approach of dividing user clusters into finite frames, we analyze the coverage probability as a function of the maximum tether length, the density of accessible rooftops for UAV ground station deployment, and the density of clusters. We verify our findings using Monte-Carlo simulations and draw multiple useful insights. For instance, we show that it is actually better to deploy UAVs at a fraction of the clusters, not all of them as it is usually assumed in literature.
Unit commitment with analytical underfrequency load-shedding constraints for island power systems
Authors: Almudena Rouco, Mohammad Rajabdorri, Lukas Sigrist, Enrique Lobato, Ignacio Egido
Date: 2023-04-11 00:00:00

This letter presents a corrective frequency-constrained UC (C-FCUC) for island power systems implementing analytical constraints on underfrequency load shedding (UFLS). Since UFLS is inevitable for sufficiently large disturbances, one can argue that less spinning reserve could be held back since UFLS takes place anyway. Congruently, the reserve criterion should consider UFLS likely to occur under disturbances. The C-FCUC can be converted into a preventive frequency-constrained UC (P-FCUC) or the standard unit commitment (UC) and the C-FCUC is thus a generalization. The proposed formulation is successfully applied to a Spanish island power system.
Cooperative Online Learning for Multi-Agent System Control via Gaussian Processes with Event-Triggered Mechanism: Extended Version
Authors: Xiaobing Dai, Zewen Yang, Sihua Zhang, Di-Hua Zhai, Yuanqing Xia, Sandra Hirche
Date: 2023-04-11 00:00:00
Tasks: Gaussian Processes, regression

In the realm of the cooperative control of multi-agent systems (MASs) with unknown dynamics, Gaussian process (GP) regression is widely used to infer the uncertainties due to its modeling flexibility of nonlinear functions and the existence of a theoretical prediction error bound. Online learning, which involves incorporating newly acquired training data into Gaussian process models, promises to improve control performance by enhancing predictions during the operation. Therefore, this paper investigates the online cooperative learning algorithm for MAS control. Moreover, an event-triggered data selection mechanism, inspired by the analysis of a centralized event-trigger, is introduced to reduce the model update frequency and enhance the data efficiency. With the proposed learning-based control, the practical convergence of the MAS is validated with guaranteed tracking performance via the Lynaponve theory. Furthermore, the exclusion of the Zeno behavior for individual agents is shown. Finally, the effectiveness of the proposed event-triggered online learning method is demonstrated in simulations.
Cyber Physical Aquaponic System (CyPhA): a CPS Testbed
Authors: Anand Agrawal, Praneeta Maganti, Rajib Ranjan Maiti
Date: 2023-04-11 00:00:00
Tasks: Decision Making

Aquaponics system promises a sustainable urban development and food production by combining vegetable and fish farming in a single water loop. However, traditional aquaponics suffers from a significant amount of manual intervention with regard to decision-making in the water circulation and water quality control. In this work, we design, build and deploy a laboratory-scale real aquaponics system by considering this system as a cyber physical system, and we call it as Cyber Physical Aquaponics (CyPhA) system. The design of our CyPhA system has five stages, Stage-1 contains a vertical vegetable farming unit, Stage-2 contains fish farming unit, Stage-3 contains natural nitrification system, Stage-4 contains bio-filtration system and Stage-5 contains water accumulation and release system. Water transfer from one stage to the next is done using water pumps, and oxygen mixing in the water in any stage is achieved using aeration pumps. CyPhA system uses sensors for pH, dissolved oxygen (DO), total dissolved solid (TDS), water temperature, air temperature and humidity. A critical level of any of the water parameters in any stage is indicated using a LED-based alert indicator. Sensor data and actuator control commands among the stagewise edge devices and the CyPhA Controller are exchanged over Message Queue Telemetry Transport (MQTT) protocol. Overall, CyPhA system is housed within an area of about 80 sq. ft. We have been successfully operating CyPhA system for the last 75 days and maintaining a good quality of water for both fish and vegetable farming units.
Fast IMU-based Dual Estimation of Human Motion and Kinematic Parameters via Progressive In-Network Computing
Authors: Xiaobing Dai, Huanzhuo Wu, Siyi Wang, Junjie Jiao, Giang T. Nguyen, Frank H. P. Fitzek, Sandra Hirche
Date: 2023-04-11 00:00:00
Tasks: parameter estimation, State Estimation

Many applications involve humans in the loop, where continuous and accurate human motion monitoring provides valuable information for safe and intuitive human-machine interaction. Portable devices such as inertial measurement units (IMUs) are applicable to monitor human motions, while in practice often limited computational power is available locally. The human motion in task space coordinates requires not only the human joint motion but also the nonlinear coordinate transformation depending on the parameters such as human limb length. In most applications, measuring these kinematics parameters for each individual requires undesirably high effort. Therefore, it is desirable to estimate both, the human motion and kinematic parameters from IMUs. In this work, we propose a novel computational framework for dual estimation in real-time exploiting in-network computational resources. We adopt the concept of field Kalman filtering, where the dual estimation problem is decomposed into a fast state estimation process and a computationally expensive parameter estimation process. In order to further accelerate the convergence, the parameter estimation is progressively computed on multiple networked computational nodes. The superiority of our proposed method is demonstrated by a simulation of a human arm, where the estimation accuracy is shown to converge faster than with conventional approaches.