pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics
Authors: Nadia Pocher, Mirko Zichichi, Fabio Merizzi, Muhammad Zohaib Shafiq, Stefano Ferretti
Date: 2022-06-07 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Graph Attention

In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public-private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.
Credible equilibrium
Authors: Mehmet S. Ismail
Date: 2022-06-10 00:00:00

Credible equilibrium is a solution concept that imposes a stronger credibility notion than subgame perfect equilibrium. A credible equilibrium is a refinement of subgame perfect equilibrium such that if a threat in a subgame g is "credible," then it must also be credible in every subgame g' that is "equivalent" to g. I show that (i) a credible equilibrium exists in multi-stage games, and (ii) if every stage game has a unique Nash equilibrium, then the credible equilibrium is unique even in infinite horizon multi-stage games. Moreover, in perfect information games, credible equilibrium is equivalent to subgame perfect equilibrium.
Feature-informed Embedding Space Regularization For Audio Classification
Authors: Yun-Ning Hung, Alexander Lerch
Date: 2022-06-10 00:00:00
Tasks: Audio Classification, Classification

Feature representations derived from models pre-trained on large-scale datasets have shown their generalizability on a variety of audio analysis tasks. Despite this generalizability, however, task-specific features can outperform if sufficient training data is available, as specific task-relevant properties can be learned. Furthermore, the complex pre-trained models bring considerable computational burdens during inference. We propose to leverage both detailed task-specific features from spectrogram input and generic pre-trained features by introducing two regularization methods that integrate the information of both feature classes. The workload is kept low during inference as the pre-trained features are only necessary for training. In experiments with the pre-trained features VGGish, OpenL3, and a combination of both, we show that the proposed methods not only outperform baseline methods, but also can improve state-of-the-art models on several audio classification tasks. The results also suggest that using the mixture of features performs better than using individual features.
Policy Gradient Reinforcement Learning for Uncertain Polytopic LPV Systems based on MHE-MPC
Authors: Hossein Nejatbakhsh Esfahani, Sebastien Gros
Date: 2022-06-10 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning (RL), Scheduling

In this paper, we propose a learning-based Model Predictive Control (MPC) approach for the polytopic Linear Parameter-Varying (LPV) systems with inexact scheduling parameters (as exogenous signals with inexact bounds), where the Linear Time Invariant (LTI) models (vertices) captured by combinations of the scheduling parameters becomes wrong. We first propose to adopt a Moving Horizon Estimation (MHE) scheme to simultaneously estimate the convex combination vector and unmeasured states based on the observations and model matching error. To tackle the wrong LTI models used in both the MPC and MHE schemes, we then adopt a Policy Gradient (PG) Reinforcement Learning (RL) to learn both the estimator (MHE) and controller (MPC) so that the best closed-loop performance is achieved. The effectiveness of the proposed RL-based MHE/MPC design is demonstrated using an illustrative example.
A Holistic Robust Motion Controller Framework for Autonomous Platooning
Authors: Hong Wang, Li-Ming Peng, Zi-Chun Wei, Kai Yang, Xian-Xu Bai, Luo Jiang, Ehsan Hashemi
Date: 2022-06-10 00:00:00
Tasks: Model Predictive Control, Motion Planning, Position

Safety is the foremost concern for autonomous platooning. The vehicle-to-vehicle (V2V) communication delay and the sudden appearance of obstacles will trigger the safety of the intended functionality (SOTIF) issues for autonomous platooning. This research proposes a holistic robust motion controller framework (MCF) for an intelligent and connected vehicle platoon system. The MCF utilizes a hierarchical structure to resolve the longitudinal string stability and the lateral control problem under the complex driving environment and time-varying communication delay. Firstly, the H-infinity feedback controller is developed to ensure the robustness of the platoon under time-varying communication delay in the upper-level coordination layer (UCL). The output from UCL will be delivered to the lower-level motion-planning layer (LML) as reference signals. Secondly, the model predictive control (MPC) algorithm is implemented in the LML to achieve multi-objective control, which comprehensively considers the reference signals, the artificial potential field, and multiple vehicle dynamics constraints. Furthermore, three critical scenarios are co-simulated for case studies, including platooning under time-varying communication delay, merging, and obstacle avoidance scenarios. The simulation results indicate that, compared with single-structure MPC, the proposed MCF can offer a better suppression on position error propagation, and get improvements on maximum position error in the three scenarios by $19.2\%$, $59.8\%$, and $15.3\%$, respectively. Last, the practicability and effectiveness of the proposed MCF are verified via hardware-in-the-loop experiment. The average conducting time of the proposed method on Speedgoat real-time target machine is 1.1 milliseconds, which meets the real-time requirements.
Equilibrium and stiffness study of clustered tensegrity structures with the consideration of pulley sizes
Authors: Shuo Ma, Yiqian Chen, Muhao Chen, Robert E. Skelton
Date: 2022-06-08 00:00:00

This paper presents the equilibrium and stiffness study of clustered tensegrity structures (CTS) considering pulley sizes. We first derive the geometric relationship between clustered strings and pulleys, where the nodal vector is chosen as the generalized coordinate. Then, the equilibrium equations of the clustered tensegrity structure with pulleys based on the Lagrangian method are given. Since the stiffness of a structure is usually weakened when using clustering strings, we formulate the tangent stiffness matrix equations for analysis. It is also shown that as pulley sizes go to zero, the governing equations of the clustered tensegrity system with pulleys yield to the classical clustered tensegrity structure without pulleys, which is consistent with the existing literature. Three examples are demonstrated to validate the given theory. The proposed method allows one to conduct equilibrium, stiffness, and robustness studies of cluster tensegrity structures with pulleys. Nevertheless, the approach developed in this paper is not limited to the tensegrity structures. It can also be applied to a wide range of applications with pulley-rope systems, such as drilling rigs, ocean platform anchors, and cargo cranes.
A Comprehensive Review on Power System Risk-Based Transient Stability
Authors: Umair Shahzad
Date: 2022-06-08 00:00:00

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to suggest a different methodology for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a cautious principle and can result in high-priced expansion projects or operational limits. With the increasing system uncertainties and widespread electricity market deregulation, there is a strong inevitability to incorporate risk in the traditional transient stability analysis. Accurate assessment of transient stability in a modern power network is becoming a strict requirement both in planning and in real-time operation, due to the increasingly intricate dynamics of a power system. Further, increasing sources of uncertainty in forecast state and in the reaction to faults highly implies the implementation of risk-based approach in assessing transient stability. Thus, this paper aims to provide a comprehensive review of risk-based transient stability in power networks and the accompanying research. It is believed that this review can be an inception for researchers in the domain of power system planning and security.
Optimization for Infrastructure Cyber-Physical Systems
Authors: Arunchandar Vasan, Prasant Misra, Srinarayana Nagarathinam, Venkata Ramakrishna, Ramasubramanian Suriyanarayanan, Yashovardhan Chati
Date: 2022-05-31 00:00:00
Tasks: Decision Making

Cyber-physical systems (CPS) are systems where a decision making (cyber/control) component is tightly integrated with a physical system (with sensing/actuation) to enable real-time monitoring and control. Recently, there has been significant research effort in viewing and optimizing physical infrastructure in built environments as CPS, even if the control action is not in real-time. Some examples of infrastructure CPS include electrical power grids; water distribution networks; transportation and logistics networks; heating, ventilation, and air conditioning (HVAC) in buildings; etc. Complexity arises in infrastructure CPS from the large scale of operations; heterogeneity of system components; dynamic and uncertain operating conditions; and goal-driven decision making and control with time-bounded task completion guarantees. For control optimization, an infrastructure CPS is typically viewed as a system of semi-autonomous sub-systems with a network of sensors and uses distributed control optimization to achieve system-wide objectives that are typically measured and quantified by better, cheaper, or faster system performance. In this article, we first illustrate the scope for control optimization in common infrastructure CPS. Next, we present a brief overview of current optimization techniques. Finally, we share our research position with a description of specific optimization approaches and their challenges for infrastructure CPS of the future.
Machine Learning Inference on Inequality of Opportunity
Authors: Juan Carlos Escanciano, Joël Robert Terschuur
Date: 2022-06-10 00:00:00
Tasks: BIG-bench Machine Learning, Model Selection, Selection bias, valid

Equality of opportunity has emerged as an important ideal of distributive justice. Empirically, Inequality of Opportunity (IOp) is measured in two steps: first, an outcome (e.g., income) is predicted given individual circumstances; and second, an inequality index (e.g., Gini) of the predictions is computed. Machine Learning (ML) methods are tremendously useful in the first step. However, they can cause sizable biases in IOp since the bias-variance trade-off allows the bias to creep in the second step. We propose a simple debiased IOp estimator robust to such ML biases and provide the first valid inferential theory for IOp. We demonstrate improved performance in simulations and report the first unbiased measures of income IOp in Europe. Mother's education and father's occupation are the circumstances that explain the most. Plug-in estimators are very sensitive to the ML algorithm, while debiased IOp estimators are robust. These results are extended to a general U-statistics setting.
Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!
Authors: Luis Gruber, Gregor Kastner
Date: 2022-06-10 00:00:00
Tasks: Variable Selection

Vector autogressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesian methods, more concretely shrinkage priors, have shown to be successful in improving prediction performance. In the present paper, we introduce the semi-global framework, in which we replace the traditional global shrinkage parameter with group-specific shrinkage parameters. We show how this framework can be applied to various shrinkage priors, such as global-local priors and stochastic search variable selection priors. We demonstrate the virtues of the proposed framework in an extensive simulation study and in an empirical application forecasting data of the US economy. Further, we shed more light on the ongoing ``Illusion of Sparsity'' debate, finding that forecasting performances under sparse/dense priors vary across evaluated economic variables and across time frames. Dynamic model averaging, however, can combine the merits of both worlds.
The perils of Kremlin's influence: evidence from Ukraine
Authors: Chiara Natalie Focacci, Mitja Kovac, Rok Spruk
Date: 2022-06-10 00:00:00

We examine the contribution of institutional integration to the institutional quality. To this end, we exploit the 2007 political crisis in Ukraine and examine the effects of staying out of the European Union for 28 Ukrainian provinces in the period 1996-2020. We construct novel subnational estimates of institutional quality for Ukraine and central and eastern European countries based on the latent residual component extraction of institutional quality from the existing governance indicators by making use of Bayesian posterior analysis under non-informative objective prior function. By comparing the residualized institutional quality trajectories of Ukrainian provinces with their central and eastern European peers that were admitted to the European Union in 2004 and after, we assess the institutional quality cost of being under Russian political influence and interference. Based on the large-scale synthetic control analysis, we find evidence of large-scale negative institutional quality effects of staying out of the European Union such as heightened political instability and rampant deterioration of the rule of law and control of corruption. Statistical significance of the estimated effects is evaluated across a comprehensive placebo simulation with more than 34 billion placebo averages for each institutional quality outcome.
To What Extent Do Disadvantaged Neighborhoods Mediate Social Assistance Dependency? Evidence from Sweden
Authors: Cheng Lin, Adel Daoud, Maria Branden
Date: 2022-06-09 00:00:00

Occasional social assistance prevents individuals from a range of social ills, particularly unemployment and poverty. It remains unclear, however, how and to what extent continued reliance on social assistance leads to individuals becoming trapped in social assistance dependency. In this paper, we build on the theory of cumulative disadvantage and examine whether the accumulated use of social assistance over the life course is associated with an increased risk of future social assistance recipiency. We also analyze the extent to which living in disadvantaged neighborhoods constitutes an important mechanism in the explanation of this association. Our analyses use Swedish population registers for the full population of individuals born in 1981, and these individuals are followed for approximately 17 years. While most studies are limited by a lack of granular, life-history data, our granular individual-level data allow us to apply causal-mediation analysis, and thereby quantify the extent to which the likelihood of ending up in social assistance dependency is affected by residing in disadvantaged neighborhoods. Our findings show the accumulation of social assistance over the studied period is associated with a more than four-fold increase on a risk ratio scale for future social assistance recipiency, compared to never having received social assistance during the period examined. Then, we examine how social assistance dependency is mediated by prolonged exposure to disadvantaged neighborhoods. Our results suggest that the indirect effect of disadvantaged neighborhoods is weak to moderate. Therefore, social assistance dependency may be a multilevel process. Future research is to explore how the mediating effects of disadvantaged neighborhoods vary in different contexts.
A Fast Third-Step Second-Order Explicit Numerical Approach To Investigating and Forecasting The Dynamic of Corruption And Poverty In Cameroon
Authors: Eric Ngondiep
Date: 2022-06-03 00:00:00

This paper constructs a third-step second-order numerical approach for solving a mathematical model on the dynamic of corruption and poverty. The stability and error estimates of the proposed technique are analyzed using the $L^{2}$-norm. The developed algorithm is at less zero-stable and second-order accurate. Furthermore, the new method is explicit, fast and more efficient than a large class of numerical schemes applied to nonlinear systems of ordinary differential equations and can serve as a robust tool for integrating general systems of initial-value problems. Some numerical examples confirm the theory and also consider the corruption and poverty in Cameroon.
Classes of Aggregation Rules for Ethical Decision Making in Automated Systems
Authors: Federico Fioravanti, Iyad Rahwan, Fernando Abel Tohmé
Date: 2022-06-10 00:00:00
Tasks: Decision Making

We study a class of {\em aggregation rules} that could be applied to ethical AI decision-making. These rules yield the decisions to be made by automated systems based on the information of profiles of preferences over possible choices. We consider two different but very intuitive notions of preferences of an alternative over another one, namely {\it pairwise majority} and {\it position} dominance. Preferences are represented by permutation processes over alternatives and aggregation rules are applied to obtain results that are socially considered to be ethically correct. In this setting, we find many aggregation rules that satisfy desirable properties for an autonomous system. We also address the problem of the stability of the aggregation process, which is important when the information is variable. These results are a contribution for an AI designer that wants to justify the decisions made by an autonomous system.\\ \textit{Keywords:} Aggregation Operators; Permutation Process; Decision Analysis.
Discrimination in Heterogeneous Games
Authors: Annick Laruelle, André Rocha
Date: 2022-06-10 00:00:00
Tasks: Vocal Bursts Type Prediction

In this paper, we consider coordination and anti-coordination heterogeneous games played by a finite population formed by different types of individuals who fail to recognize their own type but do observe the type of their opponent. We show that there exists symmetric Nash equilibria in which players discriminate by acting differently according to the type of opponent that they face in anti-coordination games, while no such equilibrium exists in coordination games. Moreover, discrimination has a limit: the maximum number of groups where the treatment differs is three. We then discuss the theoretical results in light of the observed behavior of people in some specific psychological contexts.
Dilated POCS: Minimax Convex Optimization
Authors: Albert R. Yu, Robert J. Marks II, Keith E. Schubert, Charles Baylis, Austin Egbert, Adam Goad, Sam Haug
Date: 2022-06-09 00:00:00
Tasks: Image Generation

Alternating projection onto convex sets (POCS) provides an iterative procedure to find a signal that satisfies two or more convex constraints when the sets intersect. For nonintersecting constraints, the method of simultaneous projections produces a minimum mean square error (MMSE) solution. In certain cases, a minimax solution is more desirable. Generating a minimax solution is possible using dilated POCS. The minimax solution uses morphological dilation of nonintersecting signal convex constraints. The sets are progressively dilated to the point where there is intersection at a minimax solution. Examples are given contrasting the MMSE and minimax solutions in problems of tomographic reconstruction of images. Dilated POCS adds a new imaging modality for image synthesis. Lastly, morphological erosion of signal sets is suggested as a method to shrink the overlap when sets intersect at more than one point.
Multi-faceted Graph Attention Network for Radar Target Recognition in Heterogeneous Radar Network
Authors: Han Meng, Yuexing Peng, Wei Xiang, Xu Pang, Wenbo Wang
Date: 2022-06-10 00:00:00
Tasks: Graph Attention

Radar target recognition (RTR), as a key technology of intelligent radar systems, has been well investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Most existing methods are based on a single radar or the homogeneous radar network, which do not fully exploit frequency-dimensional information. In this paper, a two-stream semantic feature fusion model, termed Multi-faceted Graph Attention Network (MF-GAT), is proposed to greatly improve the accuracy in the low SNR region of the heterogeneous radar network. By fusing the features extracted from the source domain and transform domain via a graph attention network model, the MF-GAT model distills higher-level semantic features before classification in a unified framework. Extensive experiments are presented to demonstrate that the proposed model can greatly improve the RTR performance at low SNRs.
The Z3RO Family of Precoders Cancelling Nonlinear Power Amplification Distortion in Large Array Systems
Authors: François Rottenberg, Gilles Callebaut, Liesbet Van der Perre
Date: 2022-06-10 00:00:00

Large array systems use a massive number of antenna elements and clever precoder designs to achieve an array gain at the user location. These precoders require linear front-ends, and more specifically linear power amplifiers (PAs), to avoid distortion. This reduces the energy efficiency since PAs are most efficient close to saturation, where they generate most nonlinear distortion. Moreover, the use of conventional precoders can induce a coherent combining of distortion at the user locations, degrading the signal quality. In this work, novel linear precoders, simple to compute and to implement, are proposed that allow working close to saturation, while cancelling the third-order nonlinearity of the PA without prior knowledge of the signal statistics and PA model. Their design consists in saturating a single or a few antennas on purpose together with an negative gain with respect to all other antennas to compensate for the overall nonlinear distortion at the user location. The performance gains of the designs are significant for PAs working close to saturation, as compared to maximum ratio transmission (MRT) precoding and perfect per-antenna digital pre-distortion (DPD) compensation.