pwc-archive/papers-with-abstracts huggingface dataset:
Illuminant Aware Gamut-Based Color Transfer
Authors: Rang Nguyen, Seon Joo Kim, Michael S. Brown
Date: 2014-10-01 00:00:00
Proceeding: Pacific Graphics 2014 10

This paper proposes a new approach for color transfer between two images. Our method is unique in its considera- tion of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white-balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same ‘white axis’ and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant-aware strategy gives a better result than directly working with the original source and target image’s luminance channel as done by many previous methods. Afterwards, our method performs a full gamut-based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant-aware and gamut-based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.
Group-Theme Recoloring for Multi-Image Color Consistency
Authors: Rang Nguyen, Brian Price, Scott Cohen, and Michael S. Brown
Date: 2017-10-15 00:00:00
Proceeding: Pacific Graphics 2017 10

Modifying the colors of an image is a fundamental editing task with a wide range of methods available. Manipulating multiple images to share similar colors is more challenging, with limited tools available. Methods such as color transfer are effective in making an image share similar colors with a target image; however, color transfer is not suitable for modifying multiple images. Approaches for color consistency for photo collections give good results when the photo collection contains similar scene content, but are not applicable for general input images. To address these gaps, we propose an application framework for achieving color consistency for multi-image input. Our framework derives a group color theme from the input images’ individual color palettes and uses this group color theme to recolor the image collection. This group-theme recoloring provides an effective way to ensure color consistency among multiple images and naturally lends itself to the inclusion of an additional external color theme. We detail our group-theme recoloring approach and demonstrate its effectiveness on a number of examples.
Experimental Evaluation of Multi-operator RIS-assisted Links in Indoor Environment
Authors: Mir Lodro, Jean-Baptiste Gros, Steve Greedy, Geoffroy Lerosey, Anas Al Rawi, Gabriele Gradoni
Date: 2022-06-15 00:00:00

In this work, we present reconfigurable intelligent surface (RIS)-assisted optimization of the multiple links in the same indoor environment. Multiple RISs from different operators can co-exists and handle independent robust communication links in the same indoor environment. We investigated the key performance metrics with the help of two simultaneously operating RIS-empowered robust communication links at different center frequencies in the same indoor environment. We found with the help of bit error rate (BER) and error vector magnitude (EVM) measurements that two operators can co-exist in the same RF environment without seriously impacting quality of service of users.
A Comprehensive Eco-Driving Strategy for Connected and Autonomous Vehicles (CAVs) with Microscopic Traffic Simulation Testing Evaluation
Authors: Ozgenur Kavas-Torris, Levent Guvenc
Date: 2022-06-16 00:00:00
Tasks: Autonomous Vehicles

In this paper, a comprehensive Eco-Driving strategy for CAVs is presented. In this setup, multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible, while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. This Eco-Driving deterministic controller for an ego CAV was equipped with Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) algorithms. Simulation results are used to show that the HL controller ensures significant fuel economy improvement as compared to baseline driving modes with no collisions between the ego CAV and traffic vehicles while the driving mode of the ego CAV was set correctly under changing constraints.
Multi-path fading and interference mitigation with Reconfigurable Intelligent Surfaces
Authors: Jean Baptiste Gros, Geoffroy Lerosey, Fabrice Lemoult, Mir Lodro, Steve Greedy, Gabriele Gradoni
Date: 2022-06-16 00:00:00

We exploit multi-path fading propagation to improve both the signal-to-interference-plus-noise-ratio and the stability of wireless communications within electromagnetic environments that support rich multipath propagation. Quasi-passive propagation control with multiple binary reconfigurable intelligent surfaces is adopted to control the stationary waves supported by a metallic cavity hosting a software-defined radio link. Results are demonstrated in terms of the error vector magnitude minimization of a quadrature phase-shift modulation scheme under no-line-of-sight conditions. It is found that the magnitude of fluctuation of received symbols is reduced to a stable constellation by increasing the number of individual surfaces, or elements, thus demonstrating channel hardening. By using a second software-defined radio device as a jammer, we demonstrate the ability of the RIS to mitigate the co-channel interference by channel hardening. Results are of particular interest in smart radio environments for mobile network architectures beyond 5G.
The Road to a Successful HRI: AI, Trust and ethicS-TRAITS
Authors: Alessandra Rossi, Antonio Andriella, Silvia Rossi, Anouk van Maris
Date: 2022-06-07 00:00:00
Tasks: Ethics

The aim of this workshop is to foster the exchange of insights on past and ongoing research towards effective and long-lasting collaborations between humans and robots. This workshop will provide a forum for representatives from academia and industry communities to analyse the different aspects of HRI that impact on its success. We particularly focus on AI techniques required to implement autonomous and proactive interactions, on the factors that enhance, undermine, or recover humans' acceptance and trust in robots, and on the potential ethical and legal concerns related to the deployment of such robots in human-centred environments. Website: https://sites.google.com/view/traits-hri-2022
Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations
Authors: Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix, Keisuke Kinoshita, Takafumi Moriya, Naoki Makishima, Mana Ihori, Tomohiro Tanaka, Ryo Masumura
Date: 2022-06-16 00:00:00
Tasks: Speaker Identification, Speech Extraction

Target speech extraction is a technique to extract the target speaker's voice from mixture signals using a pre-recorded enrollment utterance that characterize the voice characteristics of the target speaker. One major difficulty of target speech extraction lies in handling variability in ``intra-speaker'' characteristics, i.e., characteristics mismatch between target speech and an enrollment utterance. While most conventional approaches focus on improving {\it average performance} given a set of enrollment utterances, here we propose to guarantee the {\it worst performance}, which we believe is of great practical importance. In this work, we propose an evaluation metric called worst-enrollment source-to-distortion ratio (SDR) to quantitatively measure the robustness towards enrollment variations. We also introduce a novel training scheme that aims at directly optimizing the worst-case performance by focusing on training with difficult enrollment cases where extraction does not perform well. In addition, we investigate the effectiveness of auxiliary speaker identification loss (SI-loss) as another way to improve robustness over enrollments. Experimental validation reveals the effectiveness of both worst-enrollment target training and SI-loss training to improve robustness against enrollment variations, by increasing speaker discriminability.
DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR
Authors: Ruchao Fan, Abeer Alwan
Date: 2022-06-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition, Transfer Learning

Self-supervised learning (SSL) in the pretraining stage using un-annotated speech data has been successful in low-resource automatic speech recognition (ASR) tasks. However, models trained through SSL are biased to the pretraining data which is usually different from the data used in finetuning tasks, causing a domain shifting problem, and thus resulting in limited knowledge transfer. We propose a novel framework, domain responsible adaptation and finetuning (DRAFT), to reduce domain shifting in pretrained speech models through an additional adaptation stage. In DRAFT, residual adapters (RAs) are inserted in the pretrained model to learn domain-related information with the same SSL loss as the pretraining stage. Only RA parameters are updated during the adaptation stage. DRAFT is agnostic to the type of SSL method used and is evaluated with three widely used approaches: APC, Wav2vec2.0, and HuBERT. On two child ASR tasks (OGI and MyST databases), using SSL models trained with un-annotated adult speech data (Librispeech), relative WER improvements of up to 19.7% are observed when compared to the pretrained models without adaptation. Additional experiments examined the potential of cross knowledge transfer between the two datasets and the results are promising, showing a broader usage of the proposed DRAFT framework.
To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets
Authors: Jean-Marc Valin, Ritwik Giri, Shrikant Venkataramani, Umut Isik, Arvindh Krishnaswamy
Date: 2022-06-16 00:00:00
Tasks: Denoising, Speech Enhancement

In real life, room effect, also known as room reverberation, and the present background noise degrade the quality of speech. Recently, deep learning-based speech enhancement approaches have shown a lot of promise and surpassed traditional denoising and dereverberation methods. It is also well established that these state-of-the-art denoising algorithms significantly improve the quality of speech as perceived by human listeners. But the role of dereverberation on subjective (perceived) speech quality, and whether the additional artifacts introduced by dereverberation cause more harm than good are still unclear. In this paper, we attempt to answer these questions by evaluating a state of the art speech enhancement system in a comprehensive subjective evaluation study for different choices of dereverberation targets.
Modeling, robust control synthesis and worst-case analysis for an on-orbit servicing mission with large flexible spacecraft
Authors: Ricardo Rodrigues, Valentin Preda, Francesco Sanfedino, Daniel Alazard
Date: 2022-06-16 00:00:00

This paper outlines a complete methodology for modeling an on-orbit servicing mission scenario and designing a feedback control system for the attitude dynamics that is guaranteed to robustly meet pointing requirements, despite model uncertainties as well as large inertia and flexibility changes throughout the mission scenario. A model of the uncertain plant was derived, which fully captures the dynamics and couplings between all subsystems as well as the decoupled/coupled configurations of the chaser/target system in a single linear fractional representation (LFR). In addition, a new approach is proposed to model and analyze a closed-loop kinematic chain formed by the chaser and the target spacecraft through the chaser's robotic arm, which uses two local spring-damper systems with uncertain damping and stiffness. This approach offers the possibility to model the dynamical behaviour of a docking mechanism with dynamic stiffness and damping. The controller was designed by taking into account all the interactions between subsystems and uncertainties as well as the time-varying and coupled flexible dynamics. Lastly, the robust stability and worst-case performances were assessed by means of a structured singular value analysis.
Data-Driven Abstraction-Based Control Synthesis
Authors: Milad Kazemi, Rupak Majumdar, Mahmoud Salamati, Sadegh Soudjani, Ben Wooding
Date: 2022-06-16 00:00:00

This paper studies formal synthesis of controllers for continuous-space systems with unknown dynamics to satisfy requirements expressed as linear temporal logic formulas. Formal abstraction-based synthesis schemes rely on a precise mathematical model of the system to build a finite abstract model, which is then used to design a controller. The abstraction-based schemes are not applicable when the dynamics of the system are unknown. We propose a data-driven approach that computes the growth bound of the system using a finite number of trajectories. The growth bound together with the sampled trajectories are then used to construct the abstraction and synthesise a controller. Our approach casts the computation of the growth bound as a robust convex optimisation program (RCP). Since the unknown dynamics appear in the optimisation, we formulate a scenario convex program (SCP) corresponding to the RCP using a finite number of sampled trajectories. We establish a sample complexity result that gives a lower bound for the number of sampled trajectories to guarantee the correctness of the growth bound computed from the SCP with a given confidence. We also provide a sample complexity result for the satisfaction of the specification on the system in closed loop with the designed controller for a given confidence. Our results are founded on estimating a bound on the Lipschitz constant of the system and provide guarantees on satisfaction of both finite and infinite-horizon specifications. We show that our data-driven approach can be readily used as a model-free abstraction refinement scheme by modifying the formulation of the growth bound and providing similar sample complexity results. The performance of our approach is shown on three case studies.
Energy-Grade Double Pricing Rule in the Heating Market
Authors: Xinyi Yi, Ye Guo, Hongbin Sun
Date: 2022-06-16 00:00:00

The problem of heat system pricing is considered. A direct extension of locational marginal prices (LMP) in electricity markets to heat systems may lead to revenue inadequate issues. The underlying reason for such a problem is that, unlike electric power, heat has different grades and cannot be considered as homogenized commodity. Accordingly, an energy-grade double pricing rule is proposed in this paper. Heat energy and grade prices are explained as the shadow prices related to the nodal heat balance constraints and temperature requirements constraints at the optimal solution. The resulting merchandise surplus at each dispatch interval can be decomposed into several explainable parts, namely, congestion rent, impact from the last period, and impact from the upcoming period. And the total merchandise surplus over all dispatch intervals can be decomposed into several non-negative interpretable parts, including congestion rent and impact from the initial state, thus guaranteeing the revenue adequacy for the heat system operator. Simulations verify the effectiveness of the proposed mechanism.
Resilient Operational Planning for Microgrids Against Extreme Events
Authors: Cunzhi Zhao, Jesus Silva-Rodriguez, Xingpeng Li
Date: 2022-06-16 00:00:00
Tasks: energy management, Management

This paper proposes a novel resilience index, a microgrid survivability rate (SR) under extreme events, and then proposes a novel Resilient Operational Planning (ROP) algorithm to maximize the proposed resilience index SR. The proposed ROP algorithm can incorporate predetermined inverter failure probabilities and generate multiple scenarios accordingly to optimize resilient operations during an extreme event. The implemented ROP algorithm consists of two main steps: (i) optimization of resilient operational planning, and (ii) preventive resilience enhancement if minimum SR is not met per the analysis in step 1. A typical microgrid (MG) is studied to compare the proposed ROP algorithm against a traditional microgrid energy management (MEM) model. Results indicate that an enhanced resilience operation is achieved by the ROP algorithm, which is demonstrated by the quantification of resilience via the SR. Moreover, the proposed ROP algorithm is able to obtain a greater SR overall compared to that achieved by the traditional MEM, and this benefit of using the proposed ROP increases as the inverter failure probabilities increase.
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions
Authors: Rayan Mazouz, Karan Muvvala, Akash Ratheesh, Luca Laurenti, Morteza Lahijanian
Date: 2022-06-15 00:00:00

Neural Networks (NNs) have been successfully employed to represent the state evolution of complex dynamical systems. Such models, referred to as NN dynamic models (NNDMs), use iterative noisy predictions of NN to estimate a distribution of system trajectories over time. Despite their accuracy, safety analysis of NNDMs is known to be a challenging problem and remains largely unexplored. To address this issue, in this paper, we introduce a method of providing safety guarantees for NNDMs. Our approach is based on stochastic barrier functions, whose relation with safety are analogous to that of Lyapunov functions with stability. We first show a method of synthesizing stochastic barrier functions for NNDMs via a convex optimization problem, which in turn provides a lower bound on the system's safety probability. A key step in our method is the employment of the recent convex approximation results for NNs to find piece-wise linear bounds, which allow the formulation of the barrier function synthesis problem as a sum-of-squares optimization program. If the obtained safety probability is above the desired threshold, the system is certified. Otherwise, we introduce a method of generating controls for the system that robustly maximizes the safety probability in a minimally-invasive manner. We exploit the convexity property of the barrier function to formulate the optimal control synthesis problem as a linear program. Experimental results illustrate the efficacy of the method. Namely, they show that the method can scale to multi-dimensional NNDMs with multiple layers and hundreds of neurons per layer, and that the controller can significantly improve the safety probability.
Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK
Authors: Victor Chernozhukov, Iván Fernández-Val, Siyi Luo
Date: 2018-11-28 00:00:00
Tasks: regression

We develop a distribution regression model under endogenous sample selection. This model is a semi-parametric generalization of the Heckman selection model. It accommodates much richer effects of the covariates on outcome distribution and patterns of heterogeneity in the selection process, and allows for drastic departures from the Gaussian error structure, while maintaining the same level tractability as the classical model. The model applies to continuous, discrete and mixed outcomes. We provide identification, estimation, and inference methods, and apply them to obtain wage decomposition for the UK. Here we decompose the difference between the male and female wage distributions into composition, wage structure, selection structure, and selection sorting effects. After controlling for endogenous employment selection, we still find substantial gender wage gap -- ranging from 21% to 40% throughout the (latent) offered wage distribution that is not explained by composition. We also uncover positive sorting for single men and negative sorting for married women that accounts for a substantive fraction of the gender wage gap at the top of the distribution.
Likelihood ratio test for structural changes in factor models
Authors: Jushan Bai, Jiangtao Duan, Xu Han
Date: 2022-06-16 00:00:00

A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but a change in the variance of its factors. This effectively transforms a structural change problem of high dimension into a problem of low dimension. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly explores a special feature of the estimated factors: the pre-break and post-break variances can be a singular matrix under the alternative hypothesis, making the LR test diverging faster and thus more powerful than Wald-type tests. The better power property of the LR test is also confirmed by simulations. We also consider mean changes and multiple breaks. We apply the procedure to the factor modelling and structural change of the US employment using monthly industry-level-data.
Optimality of Matched-Pair Designs in Randomized Controlled Trials
Authors: Yuehao Bai
Date: 2022-06-15 00:00:00

In randomized controlled trials (RCTs), treatment is often assigned by stratified randomization. I show that among all stratified randomization schemes which treat all units with probability one half, a certain matched-pair design achieves the maximum statistical precision for estimating the average treatment effect (ATE). In an important special case, the optimal design pairs units according to the baseline outcome. In a simulation study based on datasets from 10 RCTs, this design lowers the standard error for the estimator of the ATE by 10% on average, and by up to 34%, relative to the original designs.
Equilibria in Network Constrained Energy Markets
Authors: Leonardo Massai, Giacomo Como, Fabio Fagnani
Date: 2022-06-15 00:00:00

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and links are the physical lines with a finite capacity connecting them. Producers play a networked Cournot game on such a network together with a centralized authority, called market maker, that facilitates the trade between geographically separate markets via the constrained power network and aims to maximize a certain welfare function. We first prove a general result that links the optimal action of the market maker with the capacity constraint enforced on the power network. Under mild assumptions, we study the existence and uniqueness of Nash equilibria and exploit our general result to prove a connection between capacity bottlenecks in the power network and the emergence of price differences between different markets that are separated by saturated lines, a phenomenon that is often observed in real power networks.