pwc-archive/papers-with-abstracts huggingface dataset:
Mixed membership distribution-free model
Authors: Huan Qing, Jingli Wang
Date: 2021-12-04 00:00:00
Tasks: Community Detection, model

We consider the problem of community detection in overlapping weighted networks, where nodes can belong to multiple communities and edge weights can be finite real numbers. To model such complex networks, we propose a general framework - the mixed membership distribution-free (MMDF) model. MMDF has no distribution constraints of edge weights and can be viewed as generalizations of some previous models, including the well-known mixed membership stochastic blockmodels. Especially, overlapping signed networks with latent community structures can also be generated from our model. We use an efficient spectral algorithm with a theoretical guarantee of convergence rate to estimate community memberships under the model. We also propose the fuzzy weighted modularity to evaluate the quality of community detection for overlapping weighted networks with positive and negative edge weights. We then provide a method to determine the number of communities for weighted networks by taking advantage of our fuzzy weighted modularity. Numerical simulations and real data applications are carried out to demonstrate the usefulness of our mixed membership distribution-free model and our fuzzy weighted modularity.
Seed-driven Document Ranking for Systematic Reviews: A Reproducibility Study
Authors: Shuai Wang, Harrisen Scells, Ahmed Mourad, Guido Zuccon
Date: 2021-12-08 00:00:00
Tasks: Document Ranking

Screening or assessing studies is critical to the quality and outcomes of a systematic review. Typically, a Boolean query retrieves the set of studies to screen. As the set of studies retrieved is unordered, screening all retrieved studies is usually required for high-quality systematic reviews. Screening prioritisation, or in other words, ranking the set of studies, enables downstream activities of a systematic review to begin in parallel. We investigate a method that exploits seed studies -- potentially relevant studies used to seed the query formulation process -- for screening prioritisation. Our investigation aims to reproduce this method to determine if it is generalisable on recently published datasets and determine the impact of using multiple seed studies on effectiveness.We show that while we could reproduce the original methods, we could not replicate their results exactly. However, we believe this is due to minor differences in document pre-processing, not deficiencies with the original methodology. Our results also indicate that our reproduced screening prioritisation method, (1) is generalisable across datasets of similar and different topicality compared to the original implementation, (2) that when using multiple seed studies, the effectiveness of the method increases using our techniques to enable this, (3) and that the use of multiple seed studies produces more stable rankings compared to single seed studies. Finally, we make our implementation and results publicly available at the following URL: https://github.com/ielab/sdr
Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics
Authors: Rony Abecidan, Vincent Itier, Jérémie Boulanger, Patrick Bas
Date: 2021-12-09 00:00:00
Proceeding: WIFS : IEEE International Workshop on Information Forensics and Security 2021 12
Tasks: Domain Adaptation, Image Forensics, Jpeg Forgery Localization, Unsupervised Domain Adaptation

Domain adaptation is a major issue for doing practical forensics. Since examined images are likely to come from a different development pipeline compared to the ones used for training our models, that may disturb them by a lot, degrading their performances. In this paper, we present a method enabling to make a forgery detector more robust to distributions different but related to its training one, inspired by [1]. The strategy exhibited in this paper foster a detector to find a feature invariant space where source and target distributions are close. Our study deals more precisely with discrepancies observed due to JPEG compressions and our experiments reveal that the proposed adaptation scheme can reasonably reduce the mismatch, even with a rather small target set with no labels when the source domain is properly selected. On top of that, when a small portion of labelled target images is available this method reduces the gap with mix training while being unsupervised.
Player Modeling using Behavioral Signals in Competitive Online Games
Authors: Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad Mobasher
Date: 2021-11-29 00:00:00

Competitive online games use rating systems to match players with similar skills to ensure a satisfying experience for players. In this paper, we focus on the importance of addressing different aspects of playing behavior when modeling players for creating match-ups. To this end, we engineer several behavioral features from a dataset of over 75,000 battle royale matches and create player models based on the retrieved features. We then use the created models to predict ranks for different groups of players in the data. The predicted ranks are compared to those of three popular rating systems. Our results show the superiority of simple behavioral models over mainstream rating systems. Some behavioral features provided accurate predictions for all groups of players while others proved useful for certain groups of players. The results of this study highlight the necessity of considering different aspects of the player's behavior such as goals, strategy, and expertise when making assignments.
Beam Squint in Ultra-wideband mmWave Systems: RF Lens Array vs. Phase-Shifter-Based Array
Authors: Sang-Hyun Park, Byoungnam Kim, Dong Ku Kim, Linglong Dai, Kai-Kit Wong, Chan-Byoung Chae
Date: 2021-12-08 00:00:00

In this article, we discuss the potential of radio frequency (RF) lens for ultra-wideband millimeter-wave (mmWave) systems. In terms of the beam squint, we compare the proposed RF lens antenna with the phase shifter-based array for hybrid beamforming. To reduce the complexities for fully digital beamforming, researchers have come up with RF lens-based hybrid beamforming. The use of mmWave systems, however, causes an increase in bandwidth, which gives rise to the beam squint phenomenon. We first find the causative factors for beam squint in the dielectric RF lens antenna. Based on the beamforming gain at each frequency, we verify that, in a specific situation, RF lens can be free of the beam squint effect. We use 3D electromagnetic analysis software to numerically interpret the beam squint of each antenna type. Based on the results, we present the degraded spectral efficiency by system-level simulations with 3D indoor ray tracing. Finally, to verify our analysis, we fabricate an actual RF lens antenna and demonstrate the real performance using a mmWave, NI PXIe, software-defined radio system.
Data-driven tube-based stochastic predictive control
Authors: Sebastian Kerz, Johannes Teutsch, Tim Brüdigam, Dirk Wollherr, Marion Leibold
Date: 2021-12-08 00:00:00
Tasks: LEMMA, Model Predictive Control

A powerful result from behavioral systems theory known as the fundamental lemma allows for predictive control akin to Model Predictive Control (MPC) for linear time invariant (LTI) systems with unknown dynamics purely from data. While most of data-driven predictive control literature focuses on robustness with respect to measurement noise, only few works consider exploiting probabilistic information of disturbances for performance-oriented control as in stochastic MPC. In this work, we propose a novel data-driven stochastic predictive control scheme for chance-constrained LTI systems subject to measurement noise and additive stochastic disturbances. In order to render the otherwise stochastic and intractable optimal control problem deterministic, our approach leverages ideas from tube-based MPC by decomposing the state into a deterministic nominal state driven by inputs and a stochastic error state affected by disturbances. By tightening constraints probabilistically with respect to the additive disturbance and robustly with respect to the measurement noise, satisfaction of original chance-constraints is guaranteed. The resulting data-driven receding horizon optimal control problem is lightweight and recursively feasible, and renders the closed loop input-to-state stable with respect to both additive disturbances and measurement noise. We demonstrate the effectiveness of the proposed approach in a simulation example.
On the use of Anderson acceleration in hierarchical control
Authors: Xuan-Huy Pham, Mazen Alamir, François Bonne, Patrick Bonnay
Date: 2021-12-08 00:00:00

This paper investigates the use of fixed-point Anderson acceleration method (AA) to a recently proposed hierarchical control framework. Due to its model-free property, the AA-based resulting hierarchical framework becomes more generic since no mathematical model of the subsystems at the lower layer is required at the upper coordinator layer. Numerical results are proposed to evaluate the effectiveness of this approach. The paper also presents a modified version of the original hierarchical approach that involves the AA in hierarchical control.
Supplementary Feedforward Voltage Control in a Reconfigurable Distribution Network
Authors: Young-Jin Kim
Date: 2021-12-08 00:00:00

Network reconfiguration (NR) has attracted much attention due to its ability to convert conventional distribution networks (DNs) into self-healing grids. This paper proposes a new strategy for real-time voltage regulation (VR) in a reconfigurable DN, whereby optimal feedforward control of synchronous and inverter-based distributed generators (DGs) is achieved in coordination with the operation of feeder line switches (SWs). This enables preemptive compensation of upcoming deviations in DN voltages caused by NR-aided load restoration. A robust optimization problem is formulated using a dynamic analytical model of NR to design the feedforward voltage controllers (FVCs) that minimize voltage deviations with respect to the H infinity norm. Errors in the estimates of DG parameters and load demands are reflected in the design of optimal FVCs through polytopic uncertainty modeling, further improving the robustness of the proposed VR strategy. Small-signal analysis and case studies are conducted, demonstrating the effectiveness of the optimal robust FVCs in improving real-time VR when NR is activated for load restoration. The performances of the proposed FVCs are also verified under various operating conditions of a reconfigurable DN, characterized principally by SW operations, network parameter errors, and communication time delays.
Nanogrid Power Management Based on Fuzzy Logic Controller
Authors: Walid Issa, Rashid Albadwawi, Tedjani Mesbahi, Mohammad Abusara
Date: 2021-12-08 00:00:00
Tasks: Management

The transport industry is undergoing unprecedented technological change. In the automotive sector, the voices of progress are, among other things, linked to the partial or total electrification of vehicles. Thanks to bi-directional charging technology, electric vehicles (EV) could significantly improve their homeowners' energy balance. For islanded household buildings where a supplementary turbine generator and photovoltaic (PV) generator are the main supplies, it is crucial to minimize the running costs and maximize the use of the PV power. In this paper, a supervisory controller based on fuzzy logic is proposed to utilize the battery in the EV, while parking, as an interactive element to replace the auxiliary turbine and to assure that the battery power and energy do not exceed their design limits and maintaining a stable power flow. The nanogrid considered in this paper consists of a PV, EV battery, load and a turbine supplementary unit. The fuzzy logic controller alters the AC bus frequency, which is used by the local controllers of the parallel units to curtail the power generated by the PV or to supplement the power from the turbine unit. The proposed FLC performance is verified by Matlab simulation.
Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors
Authors: Marica Pesce, Audrey Repetti, Anna Auría, Alessandro Daducci, Jean-Philippe Thiran, Yves Wiaux
Date: 2018-02-08 00:00:00
Tasks: Diffusion  MRI

High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a 3D kq-space under-sampling scheme to enable accelerated acquisitions. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes potentially enabling high spatio-angular resolution dMRI in the clinical setting.
La mujer a través de los personajes femeninos en el cine de temática financiera -- Women through female characters in financial topics films
Authors: I. Martín-de-Santos
Date: 2021-12-08 00:00:00
Tasks: Descriptive

Analysis and assessment of female characters in economic financial themes films that have appeared in some thirty relevant films, from the beginning of cinema, selected from various bibliographies. Descriptive, comparative and impartial study. Quantitative techniques are applied to measure the influence of women in terms of protagonism, positive values, negative values, compliance or non-compliance with the Bechdel test, and results are evaluated. It is based on two previous binding publications: Marzal et al. The crisis of the real. Representations of the 2008 financial crisis in contemporary audiovisual (2018) and Mart\'in-de-Santos, Financial administration through cinema. Analytical and critical study of the most interesting films for university education (20219. Realistic film versions are contrasted with actual events today.
Aproximacion a los estudios sobre la economia en la Segunda Republica espanola hasta 1936 -- Approaches to the economics of the Spanish Second Republic prior to 1936
Authors: I. Martin-de-Santos
Date: 2021-12-08 00:00:00

Macroeconomic data on the Spanish economy during the Second Republic is not accurate, the interpretation of historical events from the figures obtained is divergent and misleading. Hasty laws were enacted in attempts to resolve social problems arising mainly from deep economic inequalities, but they were often nothing more than declarations of good intentions. Spain suffered in the aftermath of the international economic downturn as it began to be felt at the end of the dictatorship of General Primo de Rivera. Economic policy was developed under the Constitution,but, despite the differences between the first and second biennium, there was a tendency to maintain the guidelines from the previous stage and in general, sometimes unfairly, it aimed at least to avoid the destabilization of the financial system. Nonetheless, it ultimately failed to achieve its goals, mainly because of the frequent changes of government mediated by a social crisis of greater significance that had relegated economic issues into the background.
Mathematical Model of International Trade and Global Economy
Authors: N. S. Gonchar, O. P. Dovzhyk, A. S. Zhokhin, W. H. Kozyrski, A. P. Makhort
Date: 2021-12-08 00:00:00
Tasks: Astronomy, model

This work was partially supported by the Program of Fundamental Research of the Department of Physics and Astronomy of the National Academy of Sciences of Ukraine "Mathematical models of non equilibrium processes in open systems" N 0120U100857.
Global Financial Cycle, Commodity Terms of Trade and Financial Spreads in Emerging Markets and Developing Economies
Authors: Jorge Carrera, Gabriel Montes-Rojas, Fernando Toledo
Date: 2021-12-08 00:00:00

We study the diffusion of shocks in the global financial cycle and global liquidity conditions to emerging and developing economies. We show that the classification according to their external trade patterns (as commodities' net exporters or net importers) allows to evaluate the relative importance of international monetary spillovers and their impact on the domestic financial cycle volatility -i.e., the coefficient of variation of financial spreads and risks. Given the relative importance of commodity trade in the economic structure of these countries, our study reveals that the sign and size of the trade balance of commodity goods are key parameters to rationalize the impact of global financial and liquidity conditions. Hence, the sign and volume of commodity external trade will define the effect on countries' financial spreads. We implement a two-equation dynamic panel data model for 33 countries during 1999:Q1-2020:Q4 that identifies the effect of global conditions on the countries' commodities terms of trade and financial spreads, first in a direct way, and then by a feedback mechanism by which the terms of trade have an asymmetric additional influence on spreads.
Sustainability Manifesto for Financial Products: Carbon Equivalence Principle
Authors: Chris Kenyon, Mourad Berrahoui, Andrea Macrina
Date: 2021-12-08 00:00:00

Sustainability is a key point for financial markets and the label "Green" is an attempt to address this. Acquisition of the label "Green" for financial products carries potential benefits, hence the controversy and attractiveness of the label. However, such a binary label inadequately represents the carbon impact - we use carbon as a useful simplification of sustainability. Carbon impact has a range either size of zero. Both carbon emissions, and sequestration of carbon, are possible results of financial products. A binary label does not allow differentiation between a carbon neutral investment and a coal power plant. Carbon impact has timing and duration, a planted forest takes time to grow, a coal power plant takes time to emit. Hence we propose the Carbon Equivalence Principle (CEP) for financial products: that the carbon effect of a financial product shall be included as a linked term sheet compatible with existing bank systems. This can either be a single flow, i.e., a summary carbon flow, or a linked termsheet describing the carbon impacts in volume and time. The CEP means that the carbon impact of investment follows the money. Making carbon impacts consistent with existing bank systems enables direct alignment of financial product use and sustainability, improving on non-compatible disclosure proposals.
Aggregation of Pareto optimal models
Authors: Hamed Hamze Bajgiran, Houman Owhadi
Date: 2021-12-08 00:00:00

In statistical decision theory, a model is said to be Pareto optimal (or admissible) if no other model carries less risk for at least one state of nature while presenting no more risk for others. How can you rationally aggregate/combine a finite set of Pareto optimal models while preserving Pareto efficiency? This question is nontrivial because weighted model averaging does not, in general, preserve Pareto efficiency. This paper presents an answer in four logical steps: (1) A rational aggregation rule should preserve Pareto efficiency (2) Due to the complete class theorem, Pareto optimal models must be Bayesian, i.e., they minimize a risk where the true state of nature is averaged with respect to some prior. Therefore each Pareto optimal model can be associated with a prior, and Pareto efficiency can be maintained by aggregating Pareto optimal models through their priors. (3) A prior can be interpreted as a preference ranking over models: prior $\pi$ prefers model A over model B if the average risk of A is lower than the average risk of B. (4) A rational/consistent aggregation rule should preserve this preference ranking: If both priors $\pi$ and $\pi'$ prefer model A over model B, then the prior obtained by aggregating $\pi$ and $\pi'$ must also prefer A over B. Under these four steps, we show that all rational/consistent aggregation rules are as follows: Give each individual Pareto optimal model a weight, introduce a weak order/ranking over the set of Pareto optimal models, aggregate a finite set of models S as the model associated with the prior obtained as the weighted average of the priors of the highest-ranked models in S. This result shows that all rational/consistent aggregation rules must follow a generalization of hierarchical Bayesian modeling. Following our main result, we present applications to Kernel smoothing, time-depreciating models, and voting mechanisms.
Prediction-Aware Quality Enhancement of VVC Using CNN
Authors: Fatemeh Nasiri, Wassim Hamidouche, Luce Morin, Nicolas Dhollande, Gildas Cocherel
Date: 2021-12-08 00:00:00
Tasks: Prediction

The upcoming video coding standard, Versatile Video Coding (VVC), has shown great improvement compared to its predecessor, High Efficiency Video Coding (HEVC), in terms of bitrate saving. Despite its substantial performance, compressed videos might still suffer from quality degradation at low bitrates due to coding artifacts such as blockiness, blurriness and ringing. In this work, we exploit Convolutional Neural Networks (CNN) to enhance quality of VVC coded frames after decoding in order to reduce low bitrate artifacts. The main contribution of this work is the use of coding information from the compressed bitstream. More precisely, the prediction information of intra frames is used for training the network in addition to the reconstruction information. The proposed method is applied on both luminance and chrominance components of intra coded frames of VVC. Experiments on VVC Test Model (VTM) show that, both in low and high bitrates, the use of coding information can improve the BD-rate performance by about 1% and 6% for luma and chroma components, respectively.
User Activity Detection and Channel Estimation of Spatially Correlated Channels via AMP in Massive MTC
Authors: Hamza Djelouat, Leatile Marata, Markus Leinonen, Hirley Alves, Markku Juntti
Date: 2021-12-08 00:00:00
Tasks: Action Detection, Activity Detection, User Identification

This paper addresses the problem of joint user identification and channel estimation (JUICE) for grant-free access in massive machine-type communications (mMTC). We consider the JUICE under a spatially correlated fading channel model as that reflects the main characteristics of the practical multiple-input multiple-output channels. We formulate the JUICE as a sparse recovery problem in a multiple measurement vector setup and present a solution based on the approximate message passing (AMP) algorithm that takes into account the channel spatial correlation. Using the state evolution, we provide a detailed theoretical analysis on the activity detection performance of AMP-based JUICE by deriving closed-from expressions for the probabilities of miss detection and false alarm. The simulation experiments show that the performance predicted by the theoretical analysis matches the one obtained by the numerical results.