pwc-archive/papers-with-abstracts huggingface dataset:
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Authors: Brandon Trabucco, Mariano Phielipp, Glen Berseth
Date: 2022-06-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Zero-shot Generalization

The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology. Recent work aims to eliminate the re-training of policies by investigating whether a morphology-agnostic policy, trained on a diverse set of agents with similar task objectives, can be transferred to new agents with unseen morphologies without re-training. This is a challenging problem that required previous approaches to use hand-designed descriptions of the new agent's morphology. Instead of hand-designing this description, we propose a data-driven method that learns a representation of morphology directly from the reinforcement learning objective. Ours is the first reinforcement learning algorithm that can train a policy to generalize to new agent morphologies without requiring a description of the agent's morphology in advance. We evaluate our approach on the standard benchmark for agent-agnostic control, and improve over the current state of the art in zero-shot generalization to new agents. Importantly, our method attains good performance without an explicit description of morphology.
SAQAM: Spatial Audio Quality Assessment Metric
Authors: Pranay Manocha, Anurag Kumar, Buye Xu, Anjali Menon, Israel D. Gebru, Vamsi K. Ithapu, Paul Calamia
Date: 2022-06-24 00:00:00
Tasks: Audio Quality Assessment, Multi-Task Learning, Speech Enhancement, Triplet

Audio quality assessment is critical for assessing the perceptual realism of sounds. However, the time and expense of obtaining ''gold standard'' human judgments limit the availability of such data. For AR&VR, good perceived sound quality and localizability of sources are among the key elements to ensure complete immersion of the user. Our work introduces SAQAM which uses a multi-task learning framework to assess listening quality (LQ) and spatialization quality (SQ) between any given pair of binaural signals without using any subjective data. We model LQ by training on a simulated dataset of triplet human judgments, and SQ by utilizing activation-level distances from networks trained for direction of arrival (DOA) estimation. We show that SAQAM correlates well with human responses across four diverse datasets. Since it is a deep network, the metric is differentiable, making it suitable as a loss function for other tasks. For example, simply replacing an existing loss with our metric yields improvement in a speech-enhancement network.
Speech Quality Assessment through MOS using Non-Matching References
Authors: Pranay Manocha, Anurag Kumar
Date: 2022-06-24 00:00:00
Tasks: Self-Supervised Learning

Human judgments obtained through Mean Opinion Scores (MOS) are the most reliable way to assess the quality of speech signals. However, several recent attempts to automatically estimate MOS using deep learning approaches lack robustness and generalization capabilities, limiting their use in real-world applications. In this work, we present a novel framework, NORESQA-MOS, for estimating the MOS of a speech signal. Unlike prior works, our approach uses non-matching references as a form of conditioning to ground the MOS estimation by neural networks. We show that NORESQA-MOS provides better generalization and more robust MOS estimation than previous state-of-the-art methods such as DNSMOS and NISQA, even though we use a smaller training set. Moreover, we also show that our generic framework can be combined with other learning methods such as self-supervised learning and can further supplement the benefits from these methods.
Open-source objective-oriented framework for head-related transfer function
Authors: Adam Szwajcowski
Date: 2022-06-24 00:00:00

Throughout last 30 years, numerous head-related transfer function (HRTF) models have been developed and there are more to come. This paper describes a framework based on objective-oriented programming paradigm, in which each HRTF representation method can be implemented as a separate class. Its modular structure allows the source code to be conveniently shared between researchers, while common interface provides easy access to data regardless of the internal structure of the classes. The paper discusses difficulties of designing the framework, maintaining the balance between its flexibility and finding common features of every possible directivity representation. Exemplary use cases are included and explained. Adoption of the framework will enhance possibilities of accuracy comparison between various HRTF models, thus improving the evaluation of current and future representation methods. The framework, developed in the form of a MATLAB toolbox, is designed to handle not only HRTFs but also other types of spatial data, such as e.g. sound source directivity, microphone directivity, etc.
Data Augmentation and Squeeze-and-Excitation Network on Multiple Dimension for Sound Event Localization and Detection in Real Scenes
Authors: Byeong-Yun Ko, Hyeonuk Nam, Seong-Hu Kim, Deokki Min, Seung-Deok Choi, Yong-Hwa Park
Date: 2022-06-24 00:00:00
Tasks: Data Augmentation, Sound Event Localization and Detection

Performance of sound event localization and detection (SELD) in real scenes is limited by small size of SELD dataset, due to difficulty in obtaining sufficient amount of realistic multi-channel audio data recordings with accurate label. We used two main strategies to solve problems arising from the small real SELD dataset. First, we applied various data augmentation methods on all data dimensions: channel, frequency and time. We also propose original data augmentation method named Moderate Mixup in order to simulate situations where noise floor or interfering events exist. Second, we applied Squeeze-and-Excitation block on channel and frequency dimensions to efficiently extract feature characteristics. Result of our trained models on the STARSS22 test dataset achieved the best ER, F1, LE, and LR of 0.53, 49.8%, 16.0deg., and 56.2% respectively.
Confidence Score Based Conformer Speaker Adaptation for Speech Recognition
Authors: Jiajun Deng, Xurong Xie, Tianzi Wang, Mingyu Cui, Boyang Xue, Zengrui Jin, Mengzhe Geng, Guinan Li, Xunying Liu, Helen Meng
Date: 2022-06-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

A key challenge for automatic speech recognition (ASR) systems is to model the speaker level variability. In this paper, compact speaker dependent learning hidden unit contributions (LHUC) are used to facilitate both speaker adaptive training (SAT) and test time unsupervised speaker adaptation for state-of-the-art Conformer based end-to-end ASR systems. The sensitivity during adaptation to supervision error rate is reduced using confidence score based selection of the more "trustworthy" subset of speaker specific data. A confidence estimation module is used to smooth the over-confident Conformer decoder output probabilities before serving as confidence scores. The increased data sparsity due to speaker level data selection is addressed using Bayesian estimation of LHUC parameters. Experiments on the 300-hour Switchboard corpus suggest that the proposed LHUC-SAT Conformer with confidence score based test time unsupervised adaptation outperformed the baseline speaker independent and i-vector adapted Conformer systems by up to 1.0%, 1.0%, and 1.2% absolute (9.0%, 7.9%, and 8.9% relative) word error rate (WER) reductions on the NIST Hub5'00, RT02, and RT03 evaluation sets respectively. Consistent performance improvements were retained after external Transformer and LSTM language models were used for rescoring.
Multi-Frequency Joint Community Detection and Phase Synchronization
Authors: Lingda Wang, Zhizhen Zhao
Date: 2022-06-16 00:00:00
Tasks: Community Detection, Stochastic Block Model

This paper studies the joint community detection and phase synchronization problem on the \textit{stochastic block model with relative phase}, where each node is associated with an unknown phase angle. This problem, with a variety of real-world applications, aims to recover the cluster structure and associated phase angles simultaneously. We show this problem exhibits a \textit{``multi-frequency''} structure by closely examining its maximum likelihood estimation (MLE) formulation, whereas existing methods are not originated from this perspective. To this end, two simple yet efficient algorithms that leverage the MLE formulation and benefit from the information across multiple frequencies are proposed. The former is a spectral method based on the novel multi-frequency column-pivoted QR factorization. The factorization applied to the top eigenvectors of the observation matrix provides key information about the cluster structure and associated phase angles. The second approach is an iterative multi-frequency generalized power method, where each iteration updates the estimation in a matrix-multiplication-then-projection manner. Numerical experiments show that our proposed algorithms significantly improve the ability of exactly recovering the cluster structure and the accuracy of the estimated phase angles, compared to state-of-the-art algorithms.
Cooperative Control in Eco-Driving of Electric Connected and Autonomous Vehicles in an Un-Signalized Urban Intersection
Authors: Vinith Kumar Lakshmanan, Antonio Sciarretta, Ouafae El Ganaoui-Mourlan
Date: 2022-06-24 00:00:00
Tasks: Autonomous Vehicles

This paper addresses the problem of finding the optimal Eco-Driving (ED) speed profile of an electric Connected and Automated Vehicle (CAV) in an isolated urban un-signalized intersection. The problem is formulated as a single-level optimization and solved using Pontryagin's Minimum Principle (PMP). Analytical solutions are presented for various conflicts that occur at an intersection. Cooperation is introduced amongst CAVs as the ability to share intentions. Two levels of cooperation, namely the Cooperative ED (C-ED) and Non-Cooperative (NC-ED) algorithms are evaluated, in a simulation environment, for energy efficiency with Intelligent Driver Model (IDM) as the baseline.
Adaptive Nonlinear Regulation via Gaussian Process
Authors: Lorenzo Gentilini, Michelangelo Bin, Lorenzo Marconi
Date: 2022-06-24 00:00:00

The paper deals with the problem of output regulation of nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. We borrow from the adaptive internal model design technique recently proposed in [1] and extend it by means of a Gaussian process regressor. The learning-based adaptation is performed by following an "event-triggered" logic so that hybrid tools are used to analyse the resulting closed-loop system. Unlike the approach proposed in [1] where the friend is supposed to belong to a specific finite-dimensional model set, here we only require smoothness of the ideal steady-state control action. The paper also presents numerical simulations showing how the proposed method outperforms previous approaches.
Optimization-Based Exploration of the Feasible Power Flow Space for Rapid Data Collection
Authors: Ignasi Ventura Nadal, Samuel Chevalier
Date: 2022-06-24 00:00:00

This paper provides a systematic investigation into the various nonlinear objective functions which can be used to explore the feasible space associated with the optimal power flow problem. A total of 40 nonlinear objective functions are tested, and their results are compared to the data generated by a novel exhaustive rejection sampling routine. The Hausdorff distance, which is a min-max set dissimilarity metric, is then used to assess how well each nonlinear objective function performed (i.e., how well the tested objective functions were able to explore the non-convex power flow space). Exhaustive test results were collected from five PGLib test-cases and systematically analyzed.
On Data-Driven Log-Optimal Portfolio: A Sliding Window Approach
Authors: Pei-Ting Wang, Chung-Han Hsieh
Date: 2022-06-24 00:00:00

In this paper, we propose a data-driven sliding window approach to solve a log-optimal portfolio problem. In contrast to many of the existing papers, this approach leads to a trading strategy with time-varying portfolio weights rather than fixed constant weights. We show, by conducting various empirical studies, that the approach possesses a superior trading performance to the classical log-optimal portfolio in the sense of having a higher cumulative rate of returns.
Using Differential Geometry to Revisit the Paradoxes of the Instantaneous Frequency
Authors: Federico Milano, Georgios Tzounas, Ioannis Dassios, Mohammed Ahsan Adib Murad, Taulant Kërçi
Date: 2022-06-24 00:00:00

This paper proposes a general framework to interpret the concept of Instantaneous Frequency (IF) in three-phase systems. The paper first recalls the conventional frequency-domain analysis based on the Fourier transform as well as the definition of IF which is based on the concept of analytic signals. The link between analytic signals and Clarke transform of three-phase voltages of an ac power system is also shown. Then the well-known five paradoxes of the IF are stated. In the second part of the paper, an approach based on a geometric interpretation of the frequency is proposed. This approach serves to revisit the five IF paradoxes and explain them through a common framework. The case study illustrates the features of the proposed framework based on a variety of examples and on a detailed model of the IEEE 39-bus system.
Path Integral Methods with Stochastic Control Barrier Functions
Authors: Chuyuan Tao, Hyung-Jin Yoon, Hunmin Kim, Naira Hovakimyan, Petros Voulgaris
Date: 2022-06-23 00:00:00

Safe control designs for robotic systems remain challenging because of the difficulties of explicitly solving optimal control with nonlinear dynamics perturbed by stochastic noise. However, recent technological advances in computing devices enable online optimization or sampling-based methods to solve control problems. For example, Control Barrier Functions (CBFs), a Lyapunov-like control algorithm, have been proposed to numerically solve convex optimizations that determine control input to stay in the safe set. Model Predictive Path Integral (MPPI) uses forward sampling of stochastic differential equations to solve optimal control problems online. Both control algorithms are widely used for nonlinear systems because they avoid calculating the derivatives of the nonlinear dynamic function. In this paper, we utilize Stochastic Control Barrier Functions (SCBFs) constraints to limit sample regions in the sample-based algorithm, ensuring safety in a probabilistic sense and improving sample efficiency with a stochastic differential equation. We provide a sampling complexity analysis for the required sample size of our algorithm and show that our algorithm needs fewer samples than the original MPPI algorithm does. Finally, we apply our algorithm to a path planning problem in a cluttered environment and compare the performance of the algorithms.
A physics-guided data-driven feedforward tracking controller for systems with unmodeled dynamics -- applied to 3D printing
Authors: Cheng-Hao Chou, Molong Duan, Chinedum E. Okwudire
Date: 2022-06-23 00:00:00

A hybrid (i.e., physics-guided data-driven) feedforward tracking controller is proposed for systems with unmodeled linear or nonlinear dynamics. The controller is based on the filtered basis function (FBF) approach, hence it is called a hybrid FBF controller. It formulates the feedforward control input to a system as a linear combination of a set of basis functions whose coefficients are selected to minimize tracking errors. The basis functions are filtered using a combination of two linear models to predict and minimize the tracking errors. The first model is physics-based and remains unaltered during the execution of the controller, while the second is data-driven and is continuously updated during the execution of the controller. To ensure its practicality and safe learning, the proposed hybrid FBF controller is equipped with the ability to handle delays in data acquisition and to detect impending instability due to its inherent data-driven feedback loop. Its effectiveness is demonstrated via application to vibration compensation of a 3D printer with unmodeled linear and nonlinear dynamics. Thanks to the proposed hybrid FBF controller, the tracking accuracy of the 3D printer is significantly improved in experiments involving high-speed printing, compared to a standard FBF controller that does not incorporate a data-driven model. Furthermore, the ability of the hybrid FBF controller to detect and, hence, potentially avoid impending instability is demonstrated offline using data collected online from experiments.
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects
Authors: Oliver Linton, Maximilian Ruecker, Michael Vogt, Christopher Walsh
Date: 2022-06-24 00:00:00
Tasks: Dimensionality Reduction, Time Series, Time Series Analysis

We develop new econometric methods for estimation and inference in high-dimensional panel data models with interactive fixed effects. Our approach can be regarded as a non-trivial extension of the very popular common correlated effects (CCE) approach. Roughly speaking, we proceed as follows: We first construct a projection device to eliminate the unobserved factors from the model by applying a dimensionality reduction transform to the matrix of cross-sectionally averaged covariates. The unknown parameters are then estimated by applying lasso techniques to the projected model. For inference purposes, we derive a desparsified version of our lasso-type estimator. While the original CCE approach is restricted to the low-dimensional case where the number of regressors is small and fixed, our methods can deal with both low- and high-dimensional situations where the number of regressors is large and may even exceed the overall sample size. We derive theory for our estimation and inference methods both in the large-T-case, where the time series length T tends to infinity, and in the small-T-case, where T is a fixed natural number. Specifically, we derive the convergence rate of our estimator and show that its desparsified version is asymptotically normal under suitable regularity conditions. The theoretical analysis of the paper is complemented by a simulation study and an empirical application to characteristic based asset pricing.
Impact of the political risk on food reserve ratio: evidence across countries
Authors: Kai Xing, Shang Li, Xiaoguang Yang
Date: 2022-06-24 00:00:00

Using an unbalanced panel data covering 75 countries from 1991 to 2019, we explore how the political risk impacts on food reserve ratio. The empirical findings show that an increasing political risk negatively affect food reserve ratio, and same effects hold for both internal risk and external risk. Moreover, we find that the increasing external or internal risks both negatively affect production and exports, but external risk does not significantly impact on imports and it positively impacts on consumption, while internal risk negatively impacts on imports and consumption. The results suggest that most of governments have difficulty to raise subsequent food reserve ratio in face of an increasing political risk, no matter it is an internal risk or an external risk although the mechanisms behind the impacts are different.
Quantitative Analysis of Implementation Challenges of IoT-Based Digital Supply Chain (Supply Chain 0/4)
Authors: Hamed Nozari, Mohammad Ebrahim Sadeghi, Javid Ghahremani nahr, Seyyed Esmaeil Najafi
Date: 2022-06-17 00:00:00

Over the past thirty years, logistics has undergone tremendous change from a purely operational performance that led to sales or production and focused on securing supply and delivery lines to customers, with the transformation of intelligent technologies into a professional operator. In today's world, the fourth generation of industry has forced companies to rethink how their supply chain is designed. In this case, in addition to the need for adaptation, supply chains also have the opportunity to reach operational horizons, use emerging digital supply chain business models, and transform the company into a digital supply chain. One of the transformational technologies that has had a tremendous impact on the supply chain in this regard is IoT technology. This technology, as one of the largest sources of data production, can facilitate supply chain processes in all its dimensions. However, due to the presence of the Internet and the location of supply chain components in the context of information networks, this digital supply chain always faces major challenges. Therefore, in this paper, an attempt was made to examine and prioritize the most important challenges of implementing a supply chain 0/4 using a nonlinear hierarchical analysis method. In order to investigate these challenges, the opinions of experts active in the supply chain of Fast-moving consumer goods industries (FMCG) were used as a case study as well as some academic experts.The results show that the lack of technological infrastructure and security challenges are the most important challenges of implementing supply chain 0/4 in the era of digital developments, which should be given special attention for a successful implementation.
Rate-Distortion Optimal Transform Coefficient Selection for Unoccupied Regions in Video-Based Point Cloud Compression
Authors: Christian Herglotz, Nils Genser, André Kaup
Date: 2022-06-24 00:00:00

This paper presents a novel method to determine rate-distortion optimized transform coefficients for efficient compression of videos generated from point clouds. The method exploits a generalized frequency selective extrapolation approach that iteratively determines rate-distortion-optimized coefficients for all basis functions of two-dimensional discrete cosine and sine transforms. The method is applied to blocks containing both occupied and unoccupied pixels in video based point cloud compression for HEVC encoding. In the proposed algorithm, only the values of the transform coefficients are changed such that resulting bit streams are compliant to the V-PCC standard. For all-intra coded point clouds, bitrate savings of more than 4% for geometry and more than 6% for texture error metrics with respect to standard encoding can be observed. These savings are more than twice as high as savings obtained using competing methods from literature. In the randomaccess case, our proposed method outperforms competing V-PCC methods by more than 0.5%.
Frame-type Sensitive RDO Control for Content-Adaptive-encoding
Authors: Vibhoothi, François Pitié, Anil Kokaram
Date: 2022-06-23 00:00:00
Tasks: Vocal Bursts Type Prediction

Video transcoding is an increasingly important application in the streaming media industry. It has become important to investigate the optimisation of transcoder parameters for a single clip simply because of the immense number of playbacks for popular clips. In this paper, we explore the use of a canned optimiser to estimate the optimal RD tradeoff achievable for a particular clip. We show that by adjusting the Lagrange multiplier in RD optimisation on keyframes alone we can achieve more than 10$\times$ the previous BD-Rate gains possible without affecting quality for any operating point.
Real-time Dual-channel 2 * 2 MIMO Fiber-THz-Fiber Seamless Integration System at 385 GHz and 435 GHz
Authors: Jiao Zhang, Min Zhu, Bingchang Hua, Mingzheng Lei, Yuancheng Cai, Liang Tian, Yucong Zou, Like Ma, Yongming Huang, Jianjun Yu, Xiaohu You
Date: 2022-06-24 00:00:00

We demonstrate the first practical real-time dual-channel fiber-THz-fiber 2 * 2 MIMO seamless integration system with a record net data rate of 2 * 103.125 Gb/s at 385 GHz and 435 GHz over two spans of 20 km SSMF and 3 m wireless link.