pwc-archive/papers-with-abstracts huggingface dataset:
Multivariate Pair Trading by Volatility & Model Adaption Trade-off
Authors: Chenyanzi Yu, Tianyang Xie
Date: 2021-06-11 00:00:00
Tasks: Management, PAIR TRADING, Time Series, Time Series Analysis

Pair trading is one of the most discussed topics among financial researches. Despite a growing base of work, portfolio management for multivariate time series is rarely discussed. On the other hand, most researches focus on refining strategy rules instead of finding the optimal portfolio weight. In this paper, we brought up a simple yet profitable strategy called Volatility & Model Adaption Trade-off (VMAT) to leverage the issues. Experiment studies show its superior profit performance over baselines.
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model
Authors: Mohammad Jalilpour Monesi, Bernd Accou, Tom Francart, Hugo Van hamme
Date: 2021-06-17 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Decoding the speech signal that a person is listening to from the human brain via electroencephalography (EEG) can help us understand how our auditory system works. Linear models have been used to reconstruct the EEG from speech or vice versa. Recently, Artificial Neural Networks (ANNs) such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based architectures have outperformed linear models in modeling the relation between EEG and speech. Before attempting to use these models in real-world applications such as hearing tests or (second) language comprehension assessment we need to know what level of speech information is being utilized by these models. In this study, we aim to analyze the performance of an LSTM-based model using different levels of speech features. The task of the model is to determine which of two given speech segments is matched with the recorded EEG. We used low- and high-level speech features including: envelope, mel spectrogram, voice activity, phoneme identity, and word embedding. Our results suggest that the model exploits information about silences, intensity, and broad phonetic classes from the EEG. Furthermore, the mel spectrogram, which contains all this information, yields the highest accuracy (84%) among all the features.
Localization based on enhanced low frequency interaural level difference
Authors: Metin Calis, Steven van de Par, Richard Heusdens, Richard C. Hendriks
Date: 2021-06-17 00:00:00

The processing of low-frequency interaural time differences is found to be problematic among hearing-impaired people. The current generation of beamformers does not consider this deficiency. In an attempt to tackle this issue, we propose to replace the inaudible interaural time differences in the low-frequency region with the interaural level differences. In addition, a beamformer is introduced and analyzed, which enhances the low-frequency interaural level differences of the sound sources using a near-field transformation. The proposed beamforming problem is relaxed to a convex problem using semi-definite relaxation. The instrumental analysis suggests that the low-frequency interaural level differences are enhanced without hindering the provided intelligibility. A psychoacoustic localization test is done using a listening experiment, which suggests that the replacement of time differences into level differences improves the localization performance of normal-hearing listeners for an anechoic scene but not for a reverberant scene.
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation
Authors: Martin Strauss, Jouni Paulus, Matteo Torcoli, Bernd Edler
Date: 2021-06-16 00:00:00
Tasks: Music Source Separation, Transfer Learning

This paper describes a hands-on comparison on using state-of-the-art music source separation deep neural networks (DNNs) before and after task-specific fine-tuning for separating speech content from non-speech content in broadcast audio (i.e., dialog separation). The music separation models are selected as they share the number of channels (2) and sampling rate (44.1 kHz or higher) with the considered broadcast content, and vocals separation in music is considered as a parallel for dialog separation in the target application domain. These similarities are assumed to enable transfer learning between the tasks. Three models pre-trained on music (Open-Unmix, Spleeter, and Conv-TasNet) are considered in the experiments, and fine-tuned with real broadcast data. The performance of the models is evaluated before and after fine-tuning with computational evaluation metrics (SI-SIRi, SI-SDRi, 2f-model), as well as with a listening test simulating an application where the non-speech signal is partially attenuated, e.g., for better speech intelligibility. The evaluations include two reference systems specifically developed for dialog separation. The results indicate that pre-trained music source separation models can be used for dialog separation to some degree, and that they benefit from the fine-tuning, reaching a performance close to task-specific solutions.
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit
Authors: Einari Vaaras, Sari Ahlqvist-Björkroth, Konstantinos Drossos, Okko Räsänen
Date: 2021-06-14 00:00:00
Tasks: Active Learning, Binary Classification, Cross-corpus, Domain Adaptation, Emotion Recognition, Speech Emotion Recognition

Researchers have recently started to study how the emotional speech heard by young infants can affect their developmental outcomes. As a part of this research, hundreds of hours of daylong recordings from preterm infants' audio environments were collected from two hospitals in Finland and Estonia in the context of so-called APPLE study. In order to analyze the emotional content of speech in such a massive dataset, an automatic speech emotion recognition (SER) system is required. However, there are no emotion labels or existing indomain SER systems to be used for this purpose. In this paper, we introduce this initially unannotated large-scale real-world audio dataset and describe the development of a functional SER system for the Finnish subset of the data. We explore the effectiveness of alternative state-of-the-art techniques to deploy a SER system to a new domain, comparing cross-corpus generalization, WGAN-based domain adaptation, and active learning in the task. As a result, we show that the best-performing models are able to achieve a classification performance of 73.4% unweighted average recall (UAR) and 73.2% UAR for a binary classification for valence and arousal, respectively. The results also show that active learning achieves the most consistent performance compared to the two alternatives.
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
Authors: Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven H. Low
Date: 2021-06-17 00:00:00
Proceeding: NeurIPS 2021 12

We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances \textit{"consistency"}, which measures the competitive ratio when predictions are accurate, and \textit{"robustness"}, which bounds the competitive ratio when predictions are inaccurate. We propose a novel $\lambda$-confident policy and provide a competitive ratio upper bound that depends on a trust parameter $\lambda\in [0,1]$ set based on the confidence in the predictions and some prediction error $\varepsilon$. Motivated by online learning methods, we design a self-tuning policy that adaptively learns the trust parameter $\lambda$ with a competitive ratio that depends on $\varepsilon$ and the variation of system perturbations and predictions. We show that its competitive ratio is bounded from above by $ 1+{O(\varepsilon)}/({{\Theta(1)+\Theta(\varepsilon)}})+O(\mu_{\mathsf{Var}})$ where $\mu_\mathsf{Var}$ measures the variation of perturbations and predictions. It implies that when the variations of perturbations and predictions are small, by automatically adjusting the trust parameter online, the self-tuning scheme ensures a competitive ratio that does not scale up with the prediction error $\varepsilon$.
Microgrid Resilience: A Holistic and Context-Aware Resilience Metric
Authors: Sakshi Mishra, Ted Kwasnik, Kate Anderson
Date: 2021-06-17 00:00:00

Microgrids present an effective solution for the coordinated deployment of various distributed energy resources and furthermore provide myriad additional benefits such as resilience, decreased carbon footprint, and reliability to energy consumers and the energy system as a whole. Boosting the resilience of distribution systems is another major benefit of microgrids. This is because they can also serve as a backup power source when the utility grid operations are interrupted due to either high-probability low-impact events like a component failure or low-probability high-impact events - be it a natural disaster or a planned cyberattack. However, the degree to which any particular system can defend, adapt, and restore normal operation depends on various factors including the type and severity of events to which a microgrid is subjected. These factors, in turn, are dependent on the geographical location of the deployed microgrid as well as the cyber risk profile of the site where the microgrid is operating. Therefore, in this work, we attempt to capture this multi-dimensional interplay of various factors in quantifying the ability of the microgrid to be resilient in these varying aspects. This paper, thus, proposes a customized site-specific quantification of the resilience strength for the individual microgrid capability to absorb, restore, and adapt to the changing circumstances for sustaining the critical load when a low-probability high-impact event occurs - termed as - context-aware resilience metric. We also present a case study to illustrate the key elements of our integrated analytical approach.
Probabilistic Stability Assessment for Active Distribution Grids
Authors: Sebastian Liemann, Lia Strenge, Paul Schultz, Holm Hinners, Johannis Porst, Marcel Sarstedt, Frank Hellmann
Date: 2021-06-17 00:00:00

This paper demonstrates the concept of probabilistic stability assessment on large-signal stability in the use case of short circuits in an active distribution grid. Here, the concept of survivability is applied, which extends classical stability assessments by evaluating the stability and operational limits during transients for a wide range of operating points and failures. For this purpose, a free, open-source, and computationally efficient environment (Julia) for dynamic simulation of power grids is used to demonstrate its capabilities. The model implementation is validated against established commercial software and deviations are minimal with respect to power flow and dynamic simulations.The results of a large-scale survivability analysis reveal i) a broad field of application for probabilistic stability analysis and ii) that new non-intuitive stability correlations can be obtained. Hence,the proposed method shows strong potential to efficiently conduct power system stability analysis in active distribution grids.
A New Dissipativity Condition for Asymptotic Stability of Discounted Economic MPC
Authors: Mario Zanon, Sébastien Gros
Date: 2021-06-17 00:00:00
Tasks: Model Predictive Control

Economic Model Predictive Control has recently gained popularity due to its ability to directly optimize a given performance criterion, while enforcing constraint satisfaction for nonlinear systems. Recent research has developed both numerical algorithms and stability analysis for the undiscounted case. The introduction of a discount factor in the cost, however, can be desirable in some cases of interest, e.g., economics, stochastically terminating processes, Markov decision processes, etc. Unfortunately, the stability theory in this case is still not fully developed. In this paper we propose a new dissipativity condition to prove asymptotic stability in the infinite horizon case and we connect our results with existing ones in the literature on discounted economic optimal control. Numerical examples are provided to illustrate the theoretical results.
Optimal internal boundary control of lane-free automated vehicle traffic
Authors: Milad Malekzadeh, Ioannis Papamichail, Markos Papageorgiou, Klaus Bogenberger
Date: 2020-08-24 00:00:00

A recently proposed paradigm for vehicular traffic in the era of CAV (connected and automated vehicles), called TrafficFluid, involves lane-free vehicle movement. Lane-free traffic implies that incremental road widening (narrowing) leads to corresponding incremental increase (decrease) of capacity; and this opens the way for consideration of real-time internal boundary control on highways and arterials, in order to flexibly share the total (both directions) road width and capacity among the two directions in dependence of the bi-directional demand and traffic conditions, so as to maximize the total (two directions) flow efficiency. The problem is formulated as a convex QP (Quadratic Programming) problem that may be solved efficiently, and representative case studies shed light on and demonstrate the features, capabilities and potential of the novel control action.
Temporal Logic Planning for Minimum-Time Positioning of Multiple Threat-Seduction Decoys
Authors: Tony A. Wood, Mitchell Khoo, Elad Michael, Chris Manzie, Iman Shames
Date: 2021-06-17 00:00:00
Tasks: Collision Avoidance, Motion Planning, Position

Reusable decoys offer a cost-effective alternative to the single-use hardware commonly applied to protect surface assets from threats. Such decoys portray fake assets to lure threats away from the true asset. To deceive a threat, a decoy first has to position itself such that it can break the radar lock. Considering multiple simultaneous threats, this paper introduces an approach for controlling multiple decoys to minimise the time required to break the locks of all the threats. The method includes the optimal allocation of one decoy to every threat with an assignment procedure that provides local position constraints to guarantee collision avoidance and thereby decouples the control of the decoys. A crude model of a decoy with uncertainty is considered for motion planning. The task of a decoy reaching a state in which the lock of the assigned threat can be broken is formulated as a temporal logic specification. To this end, the requirements to complete the task are modelled as time-varying set-membership constraints. The temporal and logical combination of the constraints is encoded in a mixed-integer optimisation problem. To demonstrate the results a simulated case study is provided.
Impact of Communication Loss on MPC based Cooperative Adaptive Cruise Control and Platooning
Authors: Mahdi Razzaghpour, Shahriar Shahram, Rodolfo Valiente, Yaser P. Fallah
Date: 2021-06-16 00:00:00
Tasks: Model Predictive Control

Cooperative driving, enabled by communication between automated vehicle systems, is expected to significantly contribute to transportation safety and efficiency. Cooperative Adaptive Cruise Control (CACC) and platooning are two of the main cooperative driving applications that are currently under study. These applications offer significant improvements over current advanced driver assistant systems such as adaptive cruise control (ACC). The primary motivation of CACC and Platooning is to reduce traffic congestion and improve traffic flow, traffic throughput, and highway capacity. These applications need an efficient controller to consider the computational cost and ensure driving comfort and high responsiveness. The advantage of Model Predictive Control is that we can realize high control performance since all constrain for these applications can be explicitly dealt with through solving an optimization problem. These applications highly depend on information update and Communication reliability for their safety and stability purposes. In this paper, we propose a Model Predictive Control (MPC) based approach for CACC and platooning, and examine the impact of communication loss on the performance and robustness of the control scheme. The results show an improvement in response time and string stability, demonstrating the potential of cooperation to attenuate disturbances and improve traffic flow.
The promise of energy-efficient battery-powered urban aircraft
Authors: Shashank Sripad, Venkatasubramanian Viswanathan
Date: 2021-06-16 00:00:00

Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 miles of range with payload equivalents of up to 7 passengers. We find that novel UAM aircraft consume between 130 Wh/passenger-mile up to ~1,200 Wh/passenger-mile depending on the design and utilization, relative to an expected consumption of over 220 Wh/passenger-mi for terrestrial electric vehicles and 1,000 Wh/passenger-mile for combustion engine vehicles. We also find that several UAM aircraft designs are approaching technological viability with current Li-ion batteries, based on the specific power-and-energy while rechargeability and lifetime performance remain uncertain. These aspects highlight the technological readiness of a new segment of transportation.
Exploring deterministic frequency deviations with explainable AI
Authors: Johannes Kruse, Benjamin Schäfer, Dirk Witthaut
Date: 2021-06-14 00:00:00
Tasks: Explainable artificial intelligence

Deterministic frequency deviations (DFDs) critically affect power grid frequency quality and power system stability. A better understanding of these events is urgently needed as frequency deviations have been growing in the European grid in recent years. DFDs are partially explained by the rapid adjustment of power generation following the intervals of electricity trading, but this intuitive picture fails especially before and around noonday. In this article, we provide a detailed analysis of DFDs and their relation to external features using methods from explainable Artificial Intelligence. We establish a machine learning model that well describes the daily cycle of DFDs and elucidate key interdependencies using SHapley Additive exPlanations (SHAP). Thereby, we identify solar ramps as critical to explain patterns in the Rate of Change of Frequency (RoCoF).
Optimising simulations for diphoton production at hadron colliders using amplitude neural networks
Authors: Joseph Aylett-Bullock, Simon Badger, Ryan Moodie
Date: 2021-06-17 00:00:00

Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We focus on the case of loop-induced diphoton production through gluon fusion and develop a realistic simulation method that can be applied to hadron collider observables. Neural networks are trained using the one-loop amplitudes implemented in the NJet C++ library and interfaced to the Sherpa Monte Carlo event generator where we perform a detailed study for $2\to3$ and $2\to4$ scattering problems. We also consider how the trained networks perform when varying the kinematic cuts effecting the phase space and the reliability of the neural network simulations.
The Concept, Types and Structure of Corruption
Authors: Oleg Antonov, Ekaterina Lineva
Date: 2021-06-17 00:00:00

The article analyzes the essence of the phenomenon of corruption, highlights its main varieties and characteristics. The authors of the study apply historical analysis, emphasizing the long-term nature of corruption and its historical roots. The paper uses legal analysis to characterize the legal interpretation of corruption as an economic crime.
Effects of Covid-19 Pandemic on Chinese Commodity Futures Markets
Authors: Ahmet Goncu
Date: 2021-06-17 00:00:00

In this study, empirical moments and the cointegration for all the liquid commodity futures traded in the Chinese futures markets are analyzed for the periods before and after Covid-19, which is important for trading strategies such as pairs trading. The results show that the positive change in the average returns of the products such as soybean, corn, corn starch, and iron ore futures are significantly stronger than other products in the post Covid-19 era, whereas the volatility increased most for silver, petroleum asphalt and egg futures after the pandemic started. The number of cointegrated pairs are reduced after the pandemic indicating the differentiation in returns due to the structural changes caused in the demand and supply conditions across commodities.
Politicians' Willingness to Agree: Evidence from the interactions in Twitter of Chilean Deputies
Authors: Pablo Henríquez, Jorge Sabat, José Patrìcio Sullivan
Date: 2021-06-16 00:00:00

Measuring the number of "likes" in Twitter and the number of bills voted in favor by the members of the Chilean Chambers of Deputies. We empirically study how signals of agreement in Twitter translates into cross-cutting voting during a high political polarization period of time. Our empirical analysis is guided by a spatial voting model that can help us to understand Twitter as a market of signals. Our model, which is standard for the public choice literature, introduces authenticity, an intrinsic factor that distort politicians' willigness to agree (Trilling, 2009). As our main contribution, we document empirical evidence that "likes" between opponents are positively related to the number of bills voted by the same pair of politicians in Congress, even when we control by politicians' time-invariant characteristics, coalition affiliation and following links in Twitter. Our results shed light into several contingent topics, such as polarization and disagreement within the public sphere.
Multi-activity Influence and Intervention
Authors: Ryan Kor, Junjie Zhou
Date: 2021-06-17 00:00:00

Using a general network model with multiple activities, we analyse a planner's welfare maximising interventions taking into account within-activity network spillovers and cross-activity interdependence. We show that the direction of the optimal intervention, under sufficiently large budgets, critically depends on the spectral properties of two matrices: the first matrix depicts the social connections among agents, while the second one quantifies the strategic interdependence among different activities. In particular, the first principal component of the interdependence matrix determines budget resource allocation across different activities, while the first (last) principal component of the network matrix shapes the resource allocation across different agents when network effects are strategic complements (substitutes). We explore some comparative statics analysis with respective to model primitives and discuss several applications and extensions.
Defying Gravity: The Economic Effects of Social Distancing
Authors: Alfredo D. Garcia, Christopher A. Hartwell, Martín Andrés Szybisz
Date: 2021-06-17 00:00:00

The COVID-19 pandemic has forced changes in production and especially in human interaction, with "social distancing" a standard prescription for slowing transmission of the disease. This paper examines the economic effects of social distancing at the aggregate level, weighing both the benefits and costs to prolonged distancing. Specifically we fashion a model of economic recovery when the productive capacity of factors of production is restricted by social distancing, building a system of equations where output growth and social distance changes are interdependent. The model attempts to show the complex interactions between output levels and social distancing, developing cycle paths for both variables. Ultimately, however, defying gravity via prolonged social distancing shows that a lower growth path is inevitable as a result.