pwc-archive/papers-with-abstracts huggingface dataset:
Stereo Speech Enhancement Using Custom Mid-Side Signals and Monaural Processing
Authors: Aaron Master, Lie Lu, Nathan Swedlow
Date: 2022-11-25 00:00:00
Tasks: Speech Enhancement

Speech Enhancement (SE) systems typically operate on monaural input and are used for applications including voice communications and capture cleanup for user generated content. Recent advancements and changes in the devices used for these applications are likely to lead to an increase in the amount of two-channel content for the same applications. However, SE systems are typically designed for monaural input; stereo results produced using trivial methods such as channel independent or mid-side processing may be unsatisfactory, including substantial speech distortions. To address this, we propose a system which creates a novel representation of stereo signals called Custom Mid-Side Signals (CMSS). CMSS allow benefits of mid-side signals for center-panned speech to be extended to a much larger class of input signals. This in turn allows any existing monaural SE system to operate as an efficient stereo system by processing the custom mid signal. We describe how the parameters needed for CMSS can be efficiently estimated by a component of the spatio-level filtering source separation system. Subjective listening using state-of-the-art deep learning-based SE systems on stereo content with various speech mixing styles shows that CMSS processing leads to improved speech quality at approximately half the cost of channel-independent processing.
The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues
Authors: J. Waghorne, C. Howard, H. Hu, J. Pang, W. J. Peveler, L. Harris, O. Barrera
Date: 2022-11-21 00:00:00
Tasks: Generative Adversarial Network

Complex soft tissues, for example the knee meniscus, play a crucial role in mobility and joint health, but when damaged are incredibly difficult to repair and replace. This is due to their highly hierarchical and porous nature which in turn leads to their unique mechanical properties. In order to design tissue substitutes, the internal architecture of the native tissue needs to be understood and replicated. Here we explore a combined audio-visual approach - so called transperceptual - to generate artificial architectures mimicking the native ones. The proposed method uses both traditional imagery, and sound generated from each image as a method of rapidly comparing and contrasting the porosity and pore size within the samples. We have trained and tested a generative adversarial network (GAN) on the 2D image stacks. The impact of the training set of images on the similarity of the artificial to the original dataset was assessed by analyzing two samples. The first consisting of n=478 pairs of audio and image files for which the images were downsampled to 64 $\times$ 64 pixels, the second one consisting of n=7640 pairs of audio and image files for which the full resolution 256 $\times$ 256 pixels is retained but each image is divided into 16 squares to maintain the limit of 64 $\times$ 64 pixels required by the GAN. We reconstruct the 2D stacks of artificially generated datasets into 3D objects and run image analysis algorithms to characterize statistically the architectural parameters - pore size, tortuosity and pore connectivity - and compare them with the original dataset. Results show that the artificially generated dataset that undergoes downsampling performs better in terms of parameter matching. Our audiovisual approach has the potential to be extended to larger data sets to explore both how similarities and differences can be audibly recognized across multiple samples.
Multi-Scaling Differential Contraction Integral Method for Inverse Scattering Problems with Inhomogeneous Media
Authors: Yu Zhong, Francesco Zardi, Marco Salucci, Giacomo Oliveri, Andrea Massa
Date: 2022-11-28 00:00:00

Practical applications of microwave imaging often require the solution of inverse scattering problems with inhomogeneous backgrounds. Towards this end, a novel inversion strategy, which combines the multi-scaling (MS) regularization scheme and the Difference Contraction Integral Equation (DCIE) formulation, is proposed. Such an integrated approach mitigates the non-linearity and the ill-posedness of the problem to obtain reliable high-resolution reconstructions of the unknown scattering profiles. The arising algorithmic implementation, denoted as MS-DCIE, does not require the computation of the Green's function of the inhomogeneous background, thus it provides an efficient and effective way to deal with complex scenarios. The performance of the MS-DCIE are assessed by means of numerical and experimental tests, in comparison with competitive state-of-the-art inversion strategies, as well.
A Tutorial on Linear Least Square Estimation
Authors: Qingrui Zhang
Date: 2022-11-28 00:00:00
Tasks: parameter estimation

This is a brief tutorial on the least square estimation technique that is straightforward yet effective for parameter estimation. The tutorial is focused on the linear LSEs instead of nonlinear versions, since most nonlinear LSEs can be approximated non-trivially using its linear counterparts. Linear LSEs can also provide insight into the study of the nonlinear techniques, e.g., Gauss-Newton method and Lavenberg-Marquardt method etc. Linear LSEs are computationally efficient for most occasions, so they are widely applied in practice. In this tutorial, both the original batch least square estimation and its recursive variants are reviewed comprehensively with detailed mathematical derivations.
Critical Clearing Time Estimates of Power Grid Faults via a Set-Based Method
Authors: Willem Esterhuizen, Gyula Molnár, Tim Aschenbruck, Franz Rußwurm, Halil Askan, Stefan Streif
Date: 2022-11-28 00:00:00

This paper is concerned with estimating critical clearing times in the transient stability problem of power grids without extensive time-domain simulations. We consider a highdimensional post-fault system (the grid after the fault is cleared) which we decouple into many smaller subsystems. Then, for each subsystem, we find the so-called safety sets and simulate the faulted system once to deduce the so-called safe and unsafe critical clearing times, which specify the intervals of time over which the fault may remain active before safety is compromised. We demonstrate the approach with a numerical example involving the IEEE 14 bus system.
Approximate Predictive Control Barrier Functions using Neural Networks: A Computationally Cheap and Permissive Safety Filter
Authors: Alexandre Didier, Robin C. Jacobs, Jerome Sieber, Kim P. Wabersich, Melanie N. Zeilinger
Date: 2022-11-28 00:00:00
Tasks: Autonomous Driving

A predictive control barrier function (PCBF) based safety filter is a modular framework to verify safety of a control input by predicting a future trajectory. The approach relies on the solution of two optimization problems, first computing the minimal state constraint violation given the current state in the form of slacks on the constraint, and then computing the minimal deviation from a proposed input given the previously computed minimal slacks. This paper presents an approximation procedure that uses a neural network to approximate the optimal value function of the first optimization problem, which defines a control barrier function (CBF). By including this explicit approximation in a CBF-based safety filter formulation, the online computation becomes independent of the prediction horizon. It is shown that this approximation guarantees convergence to a neighborhood of the feasible set of the PCBF safety filter problem with zero constraint violation. The convergence result relies on a novel class $\mathcal{K}$ lower bound on the PCBF decrease and depends on the approximation error of the neural network. Lastly, we demonstrate our approach in simulation for an autonomous driving example and show that the proposed approximation leads to a significant decrease in computation time compared to the original approach.
Understanding Impedance Ratio Criteria for Converter-Based AC Power System
Authors: Chongbin Zhao, Qirong Jiang
Date: 2022-11-28 00:00:00

Nyquist criterion-based impedance ratio criteria (IRCs) have been widely applied for inspecting the risk of small-signal instability among converter-based AC power systems. Aided by a comparative study on voltage source converter, including the single-input single-output (SISO) and multiple input multiple output (MIMO) analyses in both the dq and the sequence domain, two aspects are emphasized in this paper: 1) the sufficiency of SISO analysis when the mapping function (MF) is observable to potentially unstable modes, and 2) the inconvenience of IRCs with an unintended right-half plane pole emergence of MF due to the source-load partition. The strictness of analyses is proved by a systematical deduction of explicit analytical impedance models using the state space. Moreover, a novel criterion that relies on the logarithmic derivative of MFs is proposed, which can identify the system modes directly, serve as an alternative to IRCs, and be extended to other transfer function-based stability analyses.
Anderson Acceleration for Partially Observable Markov Decision Processes: A Maximum Entropy Approach
Authors: MinGyu Park, Jaeuk Shin, Insoon Yang
Date: 2022-11-28 00:00:00
Tasks: Decision Making, Sequential Decision Making

Partially observable Markov decision processes (POMDPs) is a rich mathematical framework that embraces a large class of complex sequential decision-making problems under uncertainty with limited observations. However, the complexity of POMDPs poses various computational challenges, motivating the need for an efficient algorithm that rapidly finds a good enough suboptimal solution. In this paper, we propose a novel accelerated offline POMDP algorithm exploiting Anderson acceleration (AA) that is capable of efficiently solving fixed-point problems using previous solution estimates. Our algorithm is based on the Q-function approximation (QMDP) method to alleviate the scalability issue inherent in POMDPs. Inspired by the quasi-Newton interpretation of AA, we propose a maximum entropy variant of QMDP, which we call soft QMDP, to fully benefit from AA. We prove that the overall algorithm converges to the suboptimal solution obtained by soft QMDP. Our algorithm can also be implemented in a model-free manner using simulation data. Provable error bounds on the residual and the solution are provided to examine how the simulation errors are propagated through the proposed algorithm. Finally, the performance of our algorithm is tested on several benchmark problems. According to the results of our experiments, the proposed algorithm converges significantly faster without degrading the solution quality compared to its standard counterparts.
Sub-1ms Instinctual Interference Adaptive GaN LNA Front-End with Power and Linearity Tuning
Authors: Jie Yang, Baibhab Chatterjee, Mohammad Abu Khater, Mattias Thorsell, Sten E. Gunnarsson, Tero Kiuru, Shreyas Sen
Date: 2022-11-27 00:00:00

One of the major challenges in communication, radar, and electronic warfare receivers arises from nearby device interference. The paper presents a 2-6 GHz GaN LNA front-end with onboard sensing, processing, and feedback utilizing microcontroller-based controls to achieve adaptation to a variety of interference scenarios through power and linearity regulations. The utilization of GaN LNA provides high power handling capability (30 dBm) and high linearity (OIP3= 30 dBm) for radar and EW applications. The system permits an LNA power consumption to tune from 500 mW to 2 W (4X increase) in order to adjust the linearity from P\textsubscript{1dB,IN}=-10.5 dBm to 0.5 dBm (>10X increase). Across the tuning range, the noise figure increases by approximately 0.4 dB. Feedback control methods are presented with backgrounds from control theory. The rest of the controls consume $\leq$10$\%$ (100 mW) of nominal LNA power (1 W) to achieve an adaptation time <1 ms.
Efficient Demand Response Location Targeting for Price Spike Mitigation by Exploiting Price-demand Relationship
Authors: Yufan Zhang, Honglin Wen, Tao Feng, Yize Chen
Date: 2022-11-27 00:00:00
Tasks: Benchmarking

Demand response (DR) leverages demand-side flexibility, offering a promising approach to enhance market conditions like mitigating wholesale price spikes. However, poorly chosen DR locations can inadvertently increase electricity prices. For that, we introduce a method to rigorously select DR locations and corresponding demand reductions. We formulate a bilevel program where the upper level determines the DR locations and demand reductions while ensuring the average nodal prices meet a predetermined target. The lower level tackles an economic dispatch (ED) problem and feeds the resulting nodal prices back to the upper level based on post-DR demands. This bilevel formulation presents challenges due to the lower-level non-convexity affecting the upper-level constraints on average nodal prices. To address this, we propose to replace the lower level with a piecewise linear function representing the price-demand relationship, solving iteratively for each linear segment. This results in a tractable mixed-integer linear program. An acceleration strategy is proposed to further reduce the computation time. Numerical studies demonstrate the ability of the proposed approach to reduce prices to a desired level. Besides, we empirically show that the proposed approach is robust against inaccurate system parameters and can reduce computation time by over 50%.
Tube-based Distributionally Robust Model Predictive Control for Nonlinear Process Systems via Linearization
Authors: Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
Date: 2022-11-26 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) is an effective approach to control multivariable dynamic systems with constraints. Most real dynamic models are however affected by plant-model mismatch and process uncertainties, which can lead to closed-loop performance deterioration and constraint violations. Methods such as stochastic MPC (SMPC) have been proposed to alleviate these problems; however, the resulting closed-loop state trajectory might still significantly violate the prescribed constraints if the real system deviates from the assumed disturbance distributions made during the controller design. In this work we propose a novel data-driven distributionally robust MPC scheme for nonlinear systems. Unlike SMPC, which requires the exact knowledge of the disturbance distribution, our scheme decides the control action with respect to the worst distribution from a distribution ambiguity set. This ambiguity set is defined as a Wasserstein ball centered at the empirical distribution. Due to the potential model errors that cause off-sets, the scheme is also extended by leveraging an offset-free method. The favorable results of this control scheme are demonstrated and empirically verified with a nonlinear mass spring system and a nonlinear CSTR case study.
BERN-NN: Tight Bound Propagation For Neural Networks Using Bernstein Polynomial Interval Arithmetic
Authors: Wael Fatnassi, Haitham Khedr, Valen Yamamoto, Yasser Shoukry
Date: 2022-11-22 00:00:00

In this paper, we present BERN-NN as an efficient tool to perform bound propagation of Neural Networks (NNs). Bound propagation is a critical step in wide range of NN model checkers and reachability analysis tools. Given a bounded input set, bound propagation algorithms aim to compute tight bounds on the output of the NN. So far, linear and convex optimizations have been used to perform bound propagation. Since neural networks are highly non-convex, state-of-the-art bound propagation techniques suffer from introducing large errors. To circumvent such drawback, BERN-NN approximates the bounds of each neuron using a class of polynomials called Bernstein polynomials. Bernstein polynomials enjoy several interesting properties that allow BERN-NN to obtain tighter bounds compared to those relying on linear and convex approximations. BERN-NN is efficiently parallelized on graphic processing units (GPUs). Extensive numerical results show that bounds obtained by BERN-NN are orders of magnitude tighter than those obtained by state-of-the-art verifiers such as linear programming and linear interval arithmetic. Moreoveer, BERN-NN is both faster and produces tighter outputs compared to convex programming approaches like alpha-CROWN.
Double-replica theory for evolution of genotype-phenotype interrelationship
Authors: Tuan Minh Pham, Kunihiko Kaneko
Date: 2022-11-28 00:00:00

The relationship between genotype and phenotype plays a crucial role in determining the function and robustness of biological systems. Here the evolution progresses through the change in genotype, whereas the selection is based on the phenotype, and genotype-phenotype relation also evolves. Theory for such phenotypic evolution remains poorly-developed, in contrast to evolution under the fitness landscape determined by genotypes. Here we provide statistical-physics formulation of this problem by introducing replicas for genotype and phenotype. We apply it to an evolution model, in which phenotypes are given by spin configurations; genotypes are interaction matrix for spins to give the Hamiltonian, and the fitness depends only on the configuration of a subset of spins called target. We describe the interplay between the genetic variations and phenotypic variances by noise in this model by our new approach that extends the replica theory for spin-glasses to include spin-replica for phenotypes and coupling-replica for genotypes. Within this framework we obtain a phase diagram of the evolved phenotypes against the noise and selection pressure, where each phase is distinguished by the fitness and overlaps for genotypes and phenotypes. Among the phases, robust fitted phase, relevant to biological evolution, is achieved under the intermediate level of noise (temperature), where robustness to noise and to genetic mutation are correlated, as a result of replica symmetry. We also find a trade-off between maintaining a high fitness level of phenotype and acquiring a robust pattern of genes as well as the dependence of this trade-off on the ratio between the size of the functional (target) part to that of the remaining non-functional (non-target) one. The selection pressure needed to achieve high fitness increases with the fraction of target spins.
Sparse Edge Encoder (SEE): I. Visual recognition in neuronal networks
Authors: Chia-Ying Lin, Mei Ian Sam, Yi-Ching Tsai, Hsiu-Hau Lin
Date: 2022-11-28 00:00:00

In the past few decades, there have been intense debates whether the brain operates at a critical state. To verify the criticality hypothesis in the neuronal networks is challenging and the accumulating experimental and theoretical results remain controversial at this point. Here we simulate how visual information of a nature image is processed by the finite Kinouchi-Copelli neuronal network, extracting the trends of the mutual information (how sensible the neuronal network is), the dynamical range (how sensitive the network responds to external stimuli) and the statistical fluctuations (how criticality is defined in conventional statistical physics). It is rather remarkable that the optimized state for visual recognition, although close to, does not coincide with the critical state where the statistical fluctuations reach the maximum. Different images and/or network sizes of course lead to differences in details but the trend of the information optimization remains the same. Our findings pave the first step to investigate how the information processing is optimized in different neuronal networks and suggest that the criticality hypothesis may not be necessary to explain why a neuronal network can process information smartly.
Machine Learning for Screening Large Organic Molecules
Authors: Christopher Gaul, Santiago Cuesta-Lopez
Date: 2022-11-23 00:00:00

Organic semiconductors are promising materials for cheap, scalable and sustainable electronics, light-emitting diodes and photovoltaics. For organic photovoltaic cells, it is a challenge to find compounds with suitable properties in the vast chemical compound space. For example, the ionization energy should fit to the optical spectrum of sun light, and the energy levels must allow efficient charge transport. Here, a machine-learning model is developed for rapidly and accurately estimating the HOMO and LUMO energies of a given molecular structure. It is build upon the SchNet model (Sch\"utt et al. (2018)) and augmented with a `Set2Set' readout module (Vinyals et al. (2016)). The Set2Set module has more expressive power than sum and average aggregation and is more suitable for the complex quantities under consideration. Most previous models have been trained and evaluated on rather small molecules. Therefore, the second contribution is extending the scope of machine-learning methods by adding also larger molecules from other sources and establishing a consistent train/validation/test split. As a third contribution, we make a multitask ansatz to resolve the problem of different sources coming at different levels of theory. All three contributions in conjunction bring the accuracy of the model close to chemical accuracy.
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
Authors: Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-Gonzalez, Marco Letizia, Anthony L. Caterini
Date: 2022-11-23 00:00:00
Tasks: Density Estimation

Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors. The most computationally expensive simulations involve calorimeter showers. Advances in deep generative modelling - particularly in the realm of high-dimensional data - have opened the possibility of generating realistic calorimeter showers orders of magnitude more quickly than physics-based simulation. However, the high-dimensional representation of showers belies the relative simplicity and structure of the underlying physical laws. This phenomenon is yet another example of the manifold hypothesis from machine learning, which states that high-dimensional data is supported on low-dimensional manifolds. We thus propose modelling calorimeter showers first by learning their manifold structure, and then estimating the density of data across this manifold. Learning manifold structure reduces the dimensionality of the data, which enables fast training and generation when compared with competing methods.