pwc-archive/papers-with-abstracts huggingface dataset:
The Secret Source : Incorporating Source Features to Improve Acoustic-to-Articulatory Speech Inversion
Authors: Yashish M. Siriwardena, Carol Espy-Wilson
Date: 2022-10-29 00:00:00

In this work, we incorporated acoustically derived source features, aperiodicity, periodicity and pitch as additional targets to an acoustic-to-articulatory speech inversion (SI) system. We also propose a Temporal Convolution based SI system, which uses auditory spectrograms as the input speech representation, to learn long-range dependencies and complex interactions between the source and vocal tract, to improve the SI task. The experiments are conducted with both the Wisconsin X-ray microbeam (XRMB) and Haskins Production Rate Comparison (HPRC) datasets, with comparisons done with respect to three baseline SI model architectures. The proposed SI system with the HPRC dataset gains an improvement of close to 28% when the source features are used as additional targets. The same SI system outperforms the current best performing SI models by around 9% on the XRMB dataset.
Synthesizer Preset Interpolation using Transformer Auto-Encoders
Authors: Gwendal Le Vaillant, Thierry Dutoit
Date: 2022-10-27 00:00:00

Sound synthesizers are widespread in modern music production but they increasingly require expert skills to be mastered. This work focuses on interpolation between presets, i.e., sets of values of all sound synthesis parameters, to enable the intuitive creation of new sounds from existing ones. We introduce a bimodal auto-encoder neural network, which simultaneously processes presets using multi-head attention blocks, and audio using convolutions. This model has been tested on a popular frequency modulation synthesizer with more than one hundred parameters. Experiments have compared the model to related architectures and methods, and have demonstrated that it performs smoother interpolations. After training, the proposed model can be integrated into commercial synthesizers for live interpolation or sound design tasks.
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement
Authors: Ryosuke Sawata, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Takashi Shibuya, Shusuke Takahashi, Yuki Mitsufuji
Date: 2022-10-27 00:00:00
Tasks: Denoising, Speech Enhancement

Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs. To tackle this problem, we introduce a DNN-based generative refiner, Diffiner, aiming to improve perceptual speech quality pre-processed by an SE method. We train a diffusion-based generative model by utilizing a dataset consisting of clean speech only. Then, our refiner effectively mixes clean parts newly generated via denoising diffusion restoration into the degraded and distorted parts caused by a preceding SE method, resulting in refined speech. Once our refiner is trained on a set of clean speech, it can be applied to various SE methods without additional training specialized for each SE module. Therefore, our refiner can be a versatile post-processing module w.r.t. SE methods and has high potential in terms of modularity. Experimental results show that our method improved perceptual speech quality regardless of the preceding SE methods used.
Artificial Potential Field-Based Path Planning for Cluttered Environments
Authors: Mosab Diab, Mostafa Mohammadkarimi, Raj Thilak Rajan
Date: 2022-10-31 00:00:00
Tasks: Combinatorial Optimization

In this paper, we study path planning algorithms of resource constrained mobile agents in unknown cluttered environments, which include but are not limited to various terrestrial missions e.g., search and rescue missions by drones in jungles, and space missions e.g., navigation of rovers on the Moon. In particular, we focus our attention on artificial potential field (APF) based methods, in which the target is attractive while the obstacles are repulsive to the mobile agent. In this paper, we propose two major updates to the classical APF algorithm which significantly improve the performance of path planning using APF. First, we propose to improve an existing classical method that replaces the gradient descent optimization of the potential field cost function on a continuous domain with a combinatorial optimization on a set of predefined points (called bacteria points) around the agent's current location. Our proposition includes an adaptive hyperparameter that changes the value of the potential function associated to each bacteria point based on the current environmental measurements. Our proposed solution improves the navigation performance in terms of convergence to the target at the expense of minimal increase in computational complexity. Second, we propose an improved potential field cost function of the bacteria points by introducing a new branching cost function which further improves the navigation performance. The algorithms were tested on a set of Monte Carlo simulation trials where the environment changes for each trial. Our simulation results show 25% lower navigation time and around 300% higher success rate compared to the conventional potential field method, and we present future directions for research.
Circuit Solutions towards Broadband Piezoelectric Energy Harvesting: An Impedance Analysis
Authors: Bao Zhao, Junrui Liang
Date: 2022-10-31 00:00:00

In the studies of piezoelectric energy harvesting (PEH) systems, literature has shown that circuit advancement has a significant effect on the enhancement of energy harvesting capability in resonance. On the other hand, some recent studies using the phase-variable (PV) synchronized switch technologies have found that the advanced circuit solutions can also broaden the harvesting bandwidth. However, the available span of the electrically induced dynamics by the existing energy harvesting circuits was not properly defined and demonstrated. Performance comparison among different circuits cannot be fairly achieved without using a common theoretical language. Given these, this paper provides an impedance-based analysis and comparison on the electromechanical joint dynamics of the PEH systems using different interface circuits. Given that the resonance tunability by circuit solutions has received no attention in the conventional ideal model of kinetic energy harvester, we firstly propose a more inclusive ideal model for better generalization. In practice, it was proven that the attainable dynamic ranges of the practical energy harvesting circuits are only some subsets of the ideal realm. A detailed quantitative study on the attainable ranges of the PV circuit solutions is provided after the introduction of the ideal target. Simulation and experimental results of different interface circuits show good agreement with the theoretical analysis. It can be concluded that the resonance tunability strongly depends on the achievable extent in the reactive direction of the equivalent impedance plane. In practice, the electromechanical coupling conditions and dielectric loss might also influence the resonance tunability. The general ideal model and quantitative impedance analysis provided in this paper help guide the future design effort towards high-capability and broadband PEH systems.
Control and Simulation of Motion of Constrained Multibody Systems Based on Projection Matrix Formulation
Authors: Farhad Aghili
Date: 2022-10-31 00:00:00

This paper presents a unified approach for inverse and direct dynamics of constrained multibody systems that can serve as a basis for analysis, simulation, and control. The main advantage of the formulation of the dynamic is that it does not require the constraint equations to be linearly independent. Thus, a simulation may proceed even in the presence of redundant constraints or singular configurations and a controller does not need to change its structure whenever the mechanical system changes its topology or number of degrees of freedom. A motion control scheme is proposed based on a projected inverse-dynamics scheme which proves to be stable and minimizes the weighted Euclidean norm of the actuation force. The projection-based control scheme is further developed for constrained systems, e.g. parallel manipulators, which have some joints with no actuators (passive joints). This is complemented by the development of constraint force control. A condition on the inertia matrix resulting in a decoupled mechanical system is analytically derived which simplifies the implementation of the force control.
Parametrically driven inertial sensing in chip-scale optomechanical cavities at the thermodynamical limits with extended dynamic range
Authors: Jaime Gonzalo Flor Flores, Talha Yerebakan, Wenting Wang, Mingbin Yu, Dim-Lee Kwong, Andrey Matsko, Chee Wei Wong
Date: 2022-10-31 00:00:00
Tasks: Autonomous Driving, Sensitivity

Recent scientific and technological advances have enabled the detection of gravitational waves, autonomous driving, and the proposal of a communications network on the Moon (Lunar Internet or LunaNet). These efforts are based on the measurement of minute displacements and correspondingly the forces or fields transduction, which translate to acceleration, velocity, and position determination for navigation. State-of-the-art accelerometers use capacitive or piezo resistive techniques, and micro-electromechanical systems (MEMS) via integrated circuit (IC) technologies in order to drive the transducer and convert its output for electric readout. In recent years, laser optomechanical transduction and readout have enabled highly sensitive detection of motional displacement. Here we further examine the theoretical framework for the novel mechanical frequency readout technique of optomechanical transduction when the sensor is driven into oscillation mode [8]. We demonstrate theoretical and physical agreement and characterize the most relevant performance parameters with a device with 1.5mg/Hz acceleration sensitivity, a 2.5 fm/Hz1/2 displacement resolution corresponding to a 17.02 ug/Hz1/2 force-equivalent acceleration, and a 5.91 Hz/nW power sensitivity, at the thermodynamical limits. In addition, we present a novel technique for dynamic range extension while maintaining the precision sensing sensitivity. Our inertial accelerometer is integrated on-chip, and enabled for packaging, with a laser-detuning-enabled approach.
Monitoring and Detection of Low-current High-Impedance Faults in Distribution Networks
Authors: Anwarul Islam Sifat, Fiona J. Stevens McFadden, Ramesh Rayudu, Joseph Bailey
Date: 2022-10-30 00:00:00
Tasks: Fault Detection

Faults in electricity distribution networks have the potential to ignite fires, cause electrocution, and damage the system itself. High current Low Impedance Faults (LIF) are typically detected and mitigated via over-current, distance, directional relays, fuses, etc. In contrast, while High Impedance Faults (HIF) are equally hazardous, they are much more challenging to detect due to the fault current being much lower than load currents and their time-varying and nonlinear behaviour. Moreover, New Zealand distribution networks are extensive and largely unmonitored beyond the substation, and suitable HIF detection schemes are still an ongoing research challenge. To date, we have built a physical test facility for power system fault analysis and developing and evaluating our sensing and fault detection system. We have simulated LIF and HIF with different fault surface materials and load-switching events. From the data collected, we have characterized the unique fault behaviour for both LIF and HIF in 400V networks and trained a Deep Learning classifier to recognize the type of fault present from its unique signature. We have developed an outdoor pole mountable sensing system and have installed this in Wellington Electricity's network for ongoing data collection and evaluation. This paper will describe the test facility and our experience developing and implementing the sensing system. The widest range of HIF phenomena observed was in the fault experiments involving the tree branch. For brevity, therefore, this paper reports on the results of just these tree-branch experiments. HIF faults on other surface materials will be reported elsewhere. Finally, we will detail the pole-mountable sensing system installed in Wellington Electricity's network and the outcomes thus far.
Impact of Reconfigurable Intelligent Surface Geometry on Communication Performance
Authors: Zhuangzhuang Cui, Sofie Pollin
Date: 2022-10-29 00:00:00

When beamforming is applied at the transmitter, only part of reconfigurable intelligent surfaces (RISs) will be active, and it becomes indispensable to study the impact of RIS geometry. This letter aims at evaluating RIS geometry, ranging from linear (1D), and planar (2D), to cylindrical (3D) structures. We first derive the effective illuminated surface of different RIS topologies and determine the resulting signal-to-noise ratio (SNR) and outage probability. Then, we investigate the optimal RIS location to trade off active area versus received power considering near-field propagation. Numerical results quantify the benefit of RIS geometric compactness and provide their applicable ranges.
An Analytical Model for Stepwise Adiabatic Driver Energy Consumption
Authors: Eric J. Carlson, Joshua R. Smith
Date: 2022-10-29 00:00:00

This paper presents a complete closed-form analytical model for determining the per-cycle energy consumption of stepwise adiabatic drivers used for driving a capacitive load such as a power FET gate. The model takes into account the number of steps used, the stepwise driver tank capacitance, the load capacitance, and the stepwise driver switch resistance and on-time. Model accuracy is compared to that of simulation and models from previous work.
IRS-User Association in IRS-Aided MISO Wireless Networks: Convex Optimization and Machine Learning Approaches
Authors: Hamid Amiriara, Farid Ashtiani, Mahtab Mirmohseni, Masoumeh Nasiri-Kenari
Date: 2022-10-29 00:00:00

This paper concentrates on the problem of associating an intelligent reflecting surface (IRS) to multiple users in a multiple-input single-output (MISO) downlink wireless communication network. The main objective of the paper is to maximize the sum-rate of all users by solving the joint optimization problem of the IRS-user association, IRS reflection, and BS beamforming, formulated as a non-convex mixed-integer optimization problem. The variable separation and relaxation are used to transform the problem into three convex sub-problems, which are alternatively solved through the convex optimization (CO) method. The major drawback of the proposed CO-based algorithm is high computational complexity. Thus, we make use of machine learning (ML) to tackle this problem. To this end, first, we convert the optimization problem into a regression problem. Then, we solve it with feed-forward neural networks (FNNs), trained by CO-based generated data. Simulation results show that the proposed ML-based algorithm has a performance equivalent to the CO-based algorithm, but with less computation complexity due to its offline training procedure.
A Uniform Approach to Compare Architectures in Decentralized Discrete-Event Systems
Authors: K. Ritsuka, Karen Rudie
Date: 2022-10-29 00:00:00

Solutions to decentralized discrete-event systems problems are characterized by the way local decisions are fused to yield a global decision. A fusion rule is colloquially called an architecture. Current approaches do not provide a direct way to compare existing architectures. Determining whether an architecture is more permissive than another architecture had relied on producing examples ad hoc and on individual inspiration that puts the conditions for solvability in each architecture into some form that admits comparison. In response to these research efforts, a method based on morphisms between graphs has been extracted to yield a uniform approach to compare the permissiveness of the architectures.
Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems
Authors: Bing Mao, Xiaoqun Wu, Hui Liu, Yuhua Xu, Jinhu Lü
Date: 2022-10-29 00:00:00

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples.
Modeling and Rapid Prototyping of Integrated Transmission-Distribution OPF Formulations with PowerModelsITD.jl
Authors: Juan Ospina, David M. Fobes, Russell Bent, Andreas Wächter
Date: 2022-10-28 00:00:00

Conventional electric power systems are composed of different unidirectional power flow stages of generation, transmission, and distribution, managed independently by transmission system and distribution system operators. However, as distribution systems increase in complexity due to the integration of distributed energy resources, coordination between transmission and distribution networks will be imperative for the optimal operation of the power grid. However, coupling models and formulations between transmission and distribution is non-trivial, in particular due to the common practice of modeling transmission systems as single-phase, and distribution systems as multi-conductor phase-unbalanced. To enable the rapid prototyping of power flow formulations, in particular in the modeling of the boundary conditions between these two seemingly incompatible data models, we introduce PowerModelsITD.jl, a free, open-source toolkit written in Julia for integrated transmission-distribution (ITD) optimization that leverages mature optimization libraries from the InfrastructureModels.jl-ecosystem. The primary objective of the proposed framework is to provide baseline implementations of steady-state ITD optimization problems, while providing a common platform for the evaluation of emerging formulations and optimization problems. In this work, we introduce the nonlinear formulations currently supported in PowerModelsITD.jl, which include AC-polar, AC-rectangular, current-voltage, and a linear network transportation model. Results are validated using combinations of IEEE transmission and distribution networks.
Critical growth of cerebral tissue in organoids: theory and experiments
Authors: Egor I. Kiselev, Florian G. Pflug, Arndt von Haeseler
Date: 2022-10-31 00:00:00

We develop a Fokker-Planck theory of tissue growth with three types of cells (symmetrically dividing, asymmetrically dividing and non-dividing) as main agents to study the growth dynamics of human cerebral organoids. Fitting the theory to lineage tracing data obtained in next generation sequencing experiments, we show that the growth of cerebral organoids is a critical process. We derive analytical expressions describing the time evolution of clonal lineage sizes and show how power-law distributions arise in the limit of long times due to the vanishing of a characteristic growth scale. We discuss that the independence of critical growth on initial conditions could be biologically advantageous.
AtOMICS: A neural network-based Automated Optomechanical Intelligent Coupling System for testing and characterization of silicon photonics chiplets
Authors: Jaime Gonzalo Flor Flores, Connor Nasseraddin, Jim Solomon, Talha Yerebakan, Andrey B. Matsko, Chee Wei Wong
Date: 2022-10-30 00:00:00
Tasks: Time Series, Time Series Analysis

Recent advances in silicon photonics promise to revolutionize modern technology by improving performance of everyday devices in multiple fields. However, as the industry moves into a mass fabrication phase, the problem of effective testing of integrated silicon photonics devices remains to be solved. A cost-efficient manner that reduces schedule risk needs to involve automated testing of multiple devices that share common characteristics such as input-output coupling mechanisms, but at the same time needs to be generalizable to multiple types of devices and scenarios. In this paper we present a neural network-based automated system designed for in-plane fiber-chip-fiber testing, characterization, and active alignment of silicon photonic devices that use process-design-kit library edge couplers. The presented approach combines state-of-the-art computer vision techniques with time-series analysis, in order to control a testing setup that can process multiple devices and can be easily tuned to incorporate additional hardware. The system can operate at vacuum or atmospheric pressures and maintains stability for fairly long time periods in excess of a month.
Investigation of chemical structure recognition by encoder-decoder models in learning progress
Authors: Shumpei Nemoto, Tadahaya Mizuno, Hiroyuki Kusuhara
Date: 2022-10-24 00:00:00
Tasks: Decoder

Descriptor generation methods using latent representations of encoder$-$decoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorability to the structure. However, it is not clear how the structure is recognized in the learning progress of ED models. In this work, we created ED models of various learning progress and investigated the relationship between structural information and learning progress. We showed that compound substructures were learned early in ED models by monitoring the accuracy of downstream tasks and input$-$output substructure similarity using substructure$-$based descriptors, which suggests that existing evaluation methods based on the accuracy of downstream tasks may not be sensitive enough to evaluate the performance of ED models with SMILES as descriptor generation methods. On the other hand, we showed that structure restoration was time$-$consuming, and in particular, insufficient learning led to the estimation of a larger structure than the actual one. It can be inferred that determining the endpoint of the structure is a difficult task for the model. To our knowledge, this is the first study to link the learning progress of SMILES by ED model to chemical structures for a wide range of chemicals.