pwc-archive/papers-with-abstracts huggingface dataset:
Bounded Inputs Total Energy Shaping for Mechanical Systems
Authors: M. Reza J. Harandi, Amir Molaei, Hamid D. Taghirad, Jose Guadalupe Romero
Date: 2021-04-06 00:00:00

Designing control systems with bounded input is a practical consideration since realizable physical systems are limited by the saturation of actuators. The actuators' saturation degrades the performance of the control system, and in extreme cases, the stability of the closed-loop system may be lost. However, actuator saturation is typically neglected in the design of control systems, with compensation being made in the form of over-designing the actuator or by post-analyzing the resulting system to ensure acceptable performance. The bounded input control of fully actuated systems has been investigated in multiple studies, but it is not generalized for under actuated mechanical systems. This article proposes a systematic framework for finding the upper bound of control effort in underactuated systems, based on interconnection and the damping assignment passivity based control (IDA-PBC) approach. The proposed method also offers design variables for the control law to be tuned, considering the actuator's limit. The major difficulty in finding the control input upper bounds is the velocity dependent kinetic energy related terms. Thus, the upper bound of velocity is computed using a suitable Lyapunov candidate as a function of closed-loop system parameters. The validity and application of the proposed method are investigated in detail through two benchmark systems.
Strategy Synthesis for Partially-known Switched Stochastic Systems
Authors: John Jackson, Luca Laurenti, Eric Frew, Morteza Lahijanian
Date: 2021-04-05 00:00:00

We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (LTL) over finite traces (LTLf), which is as expressive as LTL and enables interpretations over finite behaviors. The framework first learns the unknown dynamics via Gaussian process regression. Then, it builds a formal abstraction of the switched system in terms of an uncertain Markov model, namely an Interval Markov Decision Process (IMDP), by accounting for both the stochastic behavior of the system and the uncertainty in the learning step. Then, we synthesize a strategy on the resulting IMDP that maximizes the satisfaction probability of the LTLf specification and is robust against all the uncertainties in the abstraction. This strategy is then refined into a switching strategy for the original stochastic system. We show that this strategy is near-optimal and provide a bound on its distance (error) to the optimal strategy. We experimentally validate our framework on various case studies, including both linear and non-linear switched stochastic systems.
State Constrained Stochastic Optimal Control Using LSTMs
Authors: Bolun Dai, Prashanth Krishnamurthy, Andrew Papanicolaou, Farshad Khorrami
Date: 2021-04-05 00:00:00

In this paper, we propose a new methodology for state constrained stochastic optimal control (SOC) problems. The solution is based on past work in solving SOC problems using forward-backward stochastic differential equations (FBSDE). Our approach in solving the FBSDE utilizes a deep neural network (DNN), specifically Long Short-Term Memory (LSTM) networks. LSTMs are chosen to solve the FBSDE to address the curse of dimensionality, non-linearities, and long time horizons. In addition, the state constraints are incorporated using a hard penalty function, resulting in a controller that respects the constraint boundaries. Numerical instability that would be introduced by the penalty function is dealt with through an adaptive update scheme. The control design methodology is applicable to a large class of control problems. The performance and scalability of our proposed algorithm are demonstrated by numerical simulations.
Optimization of vertically mounted agrivoltaic systems
Authors: Pietro Elia Campana, Bengt Stridh, Stefano Amaducci, Michele Colauzzi
Date: 2021-04-05 00:00:00

Agrivoltaic systems represent a key technology for reaching sustainable development goals reducing the completion of land for food versus land for energy. Moreover, agrivoltaic systems are at the centre of the nexus between electricity production, crop production, and irrigation water saving. In this study, an optimization model for vertically mounted agrivoltaic systems with bifacial photovoltaic models is developed. The model combines three main submodels: solar radiation and shadings, photovoltaic, and crop yield. Validation of the submodels is performed showing good agreement with measured data and commercial software. The optimization model is set as multi objective to explore the trade-offs between competing agrivoltaic key performance indicators. The results shows that the row distance between bifacial photovoltaic modules structure affects significantly the photosyntetically active radiation distribution by reducing the crop yield of potato and oat of about 50% by passing from 20 m to 5 m. The implementation of agrivoltaic system for the investigated crops at the chosen location shows a land equivalent ratio above 1.2 that justify the technology for reaching the country sustainability goals.
A Generalized LinDistFlow Model for Power Flow Analysis
Authors: Jianqiao Huang, Bai Cui, Xinyang Zhou, Andrey Bernstein
Date: 2021-04-05 00:00:00
Tasks: model

This paper proposes a new linear power flow model for distribution system with accurate voltage magnitude estimates. The new model can be seen as a generalization of LinDistFlow model to multiphase distribution system with generic network topology (radial or meshed) around arbitrary linearization point. We have shown that the approximation quality of the proposed model strictly dominates that of the fixed-point linearization (FPL) method, a popular linear power flow model for distribution system analysis, when both are linearized around zero injection point. Numerical examples using standard IEEE test feeders are provided to illustrate the effectiveness of the proposed model as well as the improvement in accuracy over existing methods when linearized around non-zero injection points.
Limitations and Improvements of the Intelligent Driver Model (IDM)
Authors: Saleh Albeaik, Alexandre Bayen, Maria Teresa Chiri, Xiaoqian Gong, Amaury Hayat, Nicolas Kardous, Alexander Keimer, Sean T. McQuade, Benedetto Piccoli, Yiling You
Date: 2021-04-02 00:00:00

This contribution analyzes the widely used and well-known "intelligent driver model (briefly IDM), which is a second order car-following model governed by a system of ordinary differential equations. Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of the well-posedness has, to our knowledge, never been performed. First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to $-\infty$ in finite time, both undesirable properties for a car-following model. Various modifications of the IDM are then proposed in order to avoid such ill-posedness. The theoretical remediation of the model, rather than post facto by ad-hoc modification of code implementations, allows a more sound numerical implementation and preservation of the model features. Indeed, to avoid inconsistencies and ensure dynamics close to the one of the original model, one may need to inspect and clean large input data, which may result in practically impossible scenarios for large-scale simulations. Although well-posedness issues occur only for specific initial data, this may happen frequently when different traffic scenarios are analyzed, and especially in presence of lane-changing, on ramps and other network components as it is the case for most commonly used micro-simulators. On the other side, it is shown that well-posedness can be guaranteed by straight-forward improvements, such as those obtained by slightly changing the acceleration to prevent the velocity from becoming negative.
Relationship between fitness and heterogeneity in exponentially growing microbial populations
Authors: Anna Paola Muntoni, Alfredo Braunstein, Andrea Pagnani, Daniele De Martino, Andrea De Martino
Date: 2021-04-06 00:00:00

Despite major environmental and genetic differences, microbial metabolic networks are known to generate consistent physiological outcomes across vastly different organisms. This remarkable robustness suggests that, at least in bacteria, metabolic activity may be guided by universal principles. The constrained optimization of evolutionarily-motivated objective functions like the growth rate has emerged as the key theoretical assumption for the study of bacterial metabolism. While conceptually and practically useful in many situations, the idea that certain functions are optimized is hard to validate in data. Moreover, it is not always clear how optimality can be reconciled with the high degree of single-cell variability observed in experiments within microbial populations. To shed light on these issues, we develop an inverse modeling framework that connects the fitness of a population of cells (represented by the mean single-cell growth rate) to the underlying metabolic variability through the Maximum-Entropy inference of the distribution of metabolic phenotypes from data. While no clear objective function emerges, we find that, as the medium gets richer, the fitness and inferred variability for Escherichia coli populations follow and slowly approach the theoretically optimal bound defined by minimal reduction of variability at given fitness. These results suggest that bacterial metabolism may be crucially shaped by a population-level trade-off between growth and heterogeneity.
How Decentral Smart Grid Control limits non-Gaussian power grid frequency fluctuations
Authors: Benjamin Schäfer, Dirk Witthaut, Marc Timme
Date: 2021-03-24 00:00:00

Frequency fluctuations in power grids, caused by unpredictable renewable energy sources, consumer behavior and trading, need to be balanced to ensure stable grid operation. Standard smart grid solutions to mitigate large frequency excursions are based on centrally collecting data and give rise to security and privacy concerns. Furthermore, control of fluctuations is often tested by employing Gaussian perturbations. Here, we demonstrate that power grid frequency fluctuations are in general non-Gaussian, implying that large excursions are more likely than expected based on Gaussian modeling. We consider real power grid frequency measurements from Continental Europe and compare them to stochastic models and predictions based on Fokker-Planck equations. Furthermore, we review a decentral smart grid control scheme to limit these fluctuations. In particular, we derive a scaling law of how decentralized control actions reduce the magnitude of frequency fluctuations and demonstrate the power of these theoretical predictions using a test grid. Overall, we find that decentral smart grid control may reduce grid frequency excursions due to both Gaussian and non-Gaussian power fluctuations and thus offers an alternative pathway for mitigating fluctuation-induced risks.
Revisiting the empirical fundamental relationship of traffic flow for highways using a causal econometric approach
Authors: Anupriya, Daniel J. Graham, Daniel Hörcher, Prateek Bansal
Date: 2021-04-06 00:00:00
Tasks: regression

The fundamental relationship of traffic flow is empirically estimated by fitting a regression curve to a cloud of observations of traffic variables. Such estimates, however, may suffer from the confounding/endogeneity bias due to omitted variables such as driving behaviour and weather. To this end, this paper adopts a causal approach to obtain an unbiased estimate of the fundamental flow-density relationship using traffic detector data. In particular, we apply a Bayesian non-parametric spline-based regression approach with instrumental variables to adjust for the aforementioned confounding bias. The proposed approach is benchmarked against standard curve-fitting methods in estimating the flow-density relationship for three highway bottlenecks in the United States. Our empirical results suggest that the saturated (or hypercongested) regime of the estimated flow-density relationship using correlational curve fitting methods may be severely biased, which in turn leads to biased estimates of important traffic control inputs such as capacity and capacity-drop. We emphasise that our causal approach is based on the physical laws of vehicle movement in a traffic stream as opposed to a demand-supply framework adopted in the economics literature. By doing so, we also aim to conciliate the engineering and economics approaches to this empirical problem. Our results, thus, have important implications both for traffic engineers and transport economists.
Driving Factors Behind the Social Role of Retail Centers on Recreational Activities
Authors: Sepideh Baghaee, Saeed Nosratabadi, Farshid Aram, Amir Mosavi
Date: 2021-04-06 00:00:00

Retail centers can be considered as places for interactional and recreational activities and such social roles of retail centers contribute to the popularity of the retail centers. Therefore, the main objective of this study was to identify effective factors encouraging customers to engage with interactional activities and measure how these factors affect customer behavior. Accordingly, two hypotheses were raised illustrating that the travel time (i.e., the time it takes for a customer to reach the retail center) and the variety of shops (in a retail center) increase the percentage of people who spend their leisure time and recreational activities retail centers. Two case studies were conducted in two analogous retail centers, one in Tehran, Iran, and the other in Madrid, Spain. According to the results, there is an interaction between the travel time and the motivation for the presence of people in the retail center. Furthermore, the results revealed that half of both retail center goers who spend more than 10 minutes to reach the retail centers prefer to do leisure activities and browsing than shopping. In other words, the longer it takes a person to get to the center, the more likely he/she is to spend more time in the mall and do more leisure activities. It is also found that there is a significant relationship between the variety of shops in a retail center and the motivation of customers attending a retail center that encourages people to spend their leisure time in retail centers.
Wost Case in Voting and Bargaining
Authors: Anna bogomolnaia Ron Holzman Herve Moulin
Date: 2021-04-06 00:00:00

The guarantee of an anonymous mechanism is the worst case welfare an agent can secure against unanimously adversarial others. How high can such a guarantee be, and what type of mechanism achieves it? We address the worst case design question in the n-person probabilistic voting/bargaining model with p deterministic outcomes. If n is no less than p the uniform lottery is the only maximal (unimprovable) guarantee; there are many more if p>n, in particular the ones inspired by the random dictator mechanism and by voting by veto. If n=2 the maximal set M(n,p) is a simple polytope where each vertex combines a round of vetoes with one of random dictatorship. For p>n>2, we show that the dual veto and random dictator guarantees, together with the uniform one, are the building blocks of 2 to the power d simplices of dimension d in M(n,p), where d is the quotient of p-1 by n. Their vertices are guarantees easy to interpret and implement. The set M(n,p) may contain other guarantees as well; what we can say in full generality is that it is a finite union of polytopes, all sharing the uniform guarantee.
Integrating Frequency Translational Invariance in TDNNs and Frequency Positional Information in 2D ResNets to Enhance Speaker Verification
Authors: Jenthe Thienpondt, Brecht Desplanques, Kris Demuynck
Date: 2021-04-06 00:00:00
Tasks: Speaker Verification, Task 2

This paper describes the IDLab submission for the text-independent task of the Short-duration Speaker Verification Challenge 2021 (SdSVC-21). This speaker verification competition focuses on short duration test recordings and cross-lingual trials, along with the constraint of limited availability of in-domain DeepMine Farsi training data. Currently, both Time Delay Neural Networks (TDNNs) and ResNets achieve state-of-the-art results in speaker verification. These architectures are structurally very different and the construction of hybrid networks looks a promising way forward. We introduce a 2D convolutional stem in a strong ECAPA-TDNN baseline to transfer some of the strong characteristics of a ResNet based model to this hybrid CNN-TDNN architecture. Similarly, we incorporate absolute frequency positional encodings in an SE-ResNet34 architecture. These learnable feature map biases along the frequency axis offer this architecture a straightforward way to exploit frequency positional information. We also propose a frequency-wise variant of Squeeze-Excitation (SE) which better preserves frequency-specific information when rescaling the feature maps. Both modified architectures significantly outperform their corresponding baseline on the SdSVC-21 evaluation data and the original VoxCeleb1 test set. A four system fusion containing the two improved architectures achieved a third place in the final SdSVC-21 Task 2 ranking.
LEAP Submission for the Third DIHARD Diarization Challenge
Authors: Prachi Singh, Rajat Varma, Venkat Krishnamohan, Srikanth Raj Chetupalli, Sriram Ganapathy
Date: 2021-04-06 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization

The LEAP submission for DIHARD-III challenge is described in this paper. The proposed system is composed of a speech bandwidth classifier, and diarization systems fine-tuned for narrowband and wideband speech separately. We use an end-to-end speaker diarization system for the narrowband conversational telephone speech recordings. For the wideband multi-speaker recordings, we use a neural embedding based clustering approach, similar to the baseline system. The embeddings are extracted from a time-delay neural network (called x-vectors) followed by the graph based path integral clustering (PIC) approach. The LEAP system showed 24% and 18% relative improvements for Track-1 and Track-2 respectively over the baseline system provided by the organizers. This paper describes the challenge submission, the post-evaluation analysis and improvements observed on the DIHARD-III dataset.
Detecting English Speech in the Air Traffic Control Voice Communication
Authors: Igor Szoke, Santosh Kesiraju, Ondrej Novotny, Martin Kocour, Karel Vesely, Jan "Honza" Cernocky
Date: 2021-04-06 00:00:00

We launched a community platform for collecting the ATC speech world-wide in the ATCO2 project. Filtering out unseen non-English speech is one of the main components in the data processing pipeline. The proposed English Language Detection (ELD) system is based on the embeddings from Bayesian subspace multinomial model. It is trained on the word confusion network from an ASR system. It is robust, easy to train, and light weighted. We achieved 0.0439 equal-error-rate (EER), a 50% relative reduction as compared to the state-of-the-art acoustic ELD system based on x-vectors, in the in-domain scenario. Further, we achieved an EER of 0.1352, a 33% relative reduction as compared to the acoustic ELD, in the unseen language (out-of-domain) condition. We plan to publish the evaluation dataset from the ATCO2 project.
Hyperspectral Image Denoising Based On Multi-Stream Denoising Network
Authors: Yan Gao, Feng Gao, Junyu Dong
Date: 2021-04-06 00:00:00
Tasks: Denoising, Hyperspectral Image Denoising, Image Denoising, Noise Estimation

Hyperspectral images (HSIs) have been widely applied in many fields, such as military, agriculture, and environment monitoring. Nevertheless, HSIs commonly suffer from various types of noise during acquisition. Therefore, denoising is critical for HSI analysis and applications. In this paper, we propose a novel blind denoising method for HSIs based on Multi-Stream Denoising Network (MSDNet). Our network consists of the noise estimation subnetwork and denoising subnetwork. In the noise estimation subnetwork, a multiscale fusion module is designed to capture the noise from different scales. Then, the denoising subnetwork is utilized to obtain the final denoising image. The proposed MSDNet can obtain robust noise level estimation, which is capable of improving the performance of HSI denoising. Extensive experiments on HSI dataset demonstrate that the proposed method outperforms four closely related methods.
Disentangled Non-Local Network for Hyperspectral and LiDAR Data Classification
Authors: Wenxia Liu, Feng Gao, Junyu Dong
Date: 2021-04-06 00:00:00
Tasks: Classification, General Classification

As the ground objects become increasingly complex, the classification results obtained by single source remote sensing data can hardly meet the application requirements. In order to tackle this limitation, we propose a simple yet effective attention fusion model based on Disentangled Non-local (DNL) network for hyperspectral and LiDAR data joint classification task. In this model, according to the spectral and spatial characteristics of HSI and LiDAR, a multiscale module and a convolutional neural network (CNN) are used to capture the spectral and spatial characteristics respectively. In addition, the extracted HSI and LiDAR features are fused through some operations to obtain the feature information more in line with the real situation. Finally, the above three data are fed into different branches of the DNL module, respectively. Extensive experiments on Houston dataset show that the proposed network is superior and more effective compared to several of the most advanced baselines in HSI and LiDAR joint classification missions.
I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications
Authors: Nooshin Mojab, Vahid Noroozi, Abdullah Aleem, Manoj P. Nallabothula, Joseph Baker, Dimitri T. Azar, Mark Rosenblatt, RV Paul Chan, Darvin Yi, Philip S. Yu, Joelle A. Hallak
Date: 2021-03-30 00:00:00

Data from clinical real-world settings is characterized by variability in quality, machine-type, setting, and source. One of the primary goals of medical computer vision is to develop and validate artificial intelligence (AI) based algorithms on real-world data enabling clinical translations. However, despite the exponential growth in AI based applications in healthcare, specifically in ophthalmology, translations to clinical settings remain challenging. Limited access to adequate and diverse real-world data inhibits the development and validation of translatable algorithms. In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings. We present the infrastructure employed to collect, annotate, and anonymize images from multiple sources, demonstrating the complexity of real-world retrospective data and its limitations. I-ODA includes 12 imaging modalities with a total of 3,668,649 ophthalmic images of 33,876 individuals from the Department of Ophthalmology and Visual Sciences at the Illinois Eye and Ear Infirmary of the University of Illinois Chicago (UIC) over the course of 12 years.