pwc-archive/papers-with-abstracts huggingface dataset:
Coordinating O&M and Logistical Resources to Enhance Post-Disaster Resilience of Interdependent Power and Natural Gas Distribution Systems
Authors: Wei Wang, Kaigui Xie, Hongbin Wang, Tao Chen, Hongzhou Chen, Yufei He
Date: 2023-06-28 00:00:00
Tasks: Scheduling

Electric power and natural gas systems are becoming increasingly interdependent, driven by the growth of natural gas-fired generation and the electrification of the gas industry. Recent energy crises have underscored the urgent need for enhanced resilience in these interdependent systems. In response to this challenge, this paper focuses on the interdependent electric power and natural gas distribution systems and proposes a comprehensive strategy for enhancing their resilience, which involves optimally coordinating various resources encompassing emergency operation and maintenance (O&M) and logistical support, to efficiently restore damaged services and infrastructure. Specifically, generators, including those with fuel switching capability, are dispatched to provide emergency power, with the logistical process of fuel supply being particularly managed by coordinated scheduling of mobile fuel tankers. Additionally, integrated demand responses for power and gas are dispatched to alleviate operational stresses using a zonewise approach that groups customers with intertwined energy supplies into individual, dispatchable units. Furthermore, the strategy incorporates the repair of damaged facilities, with particular consideration given to the varying repair efficiencies dependent on the dispatched repair units, aiming to closely simulate actual repair scenarios. The strategy is encapsulated in a mixed-integer second-order cone programming model, with case studies conducted to validate its effectiveness.
Multiscale motion and deformation of bumps in stochastic neural fields with dynamic connectivity
Authors: Heather L Cihak, Zachary P Kilpatrick
Date: 2023-06-28 00:00:00

The distinct timescales of synaptic plasticity and neural activity dynamics play an important role in the brain's learning and memory systems. Activity-dependent plasticity reshapes neural circuit architecture, determining spontaneous and stimulus-encoding spatiotemporal patterns of neural activity. Neural activity bumps maintain short term memories of continuous parameter values, emerging in spatially-organized models with short term excitation and long-range inhibition. Previously, we demonstrated nonlinear Langevin equations derived using an interface method accurately describe the dynamics of bumps in continuum neural fields with separate excitatory/inhibitory populations. Here we extend this analysis to incorporate effects of slow short term plasticity that modifies connectivity described by an integral kernel. Linear stability analysis adapted to these piecewise smooth models with Heaviside firing rates further indicate how plasticity shapes bumps' local dynamics. Facilitation (depression), which strengthens (weakens) synaptic connectivity originating from active neurons, tends to increase (decrease) stability of bumps when acting on excitatory synapses. The relationship is inverted when plasticity acts on inhibitory synapses. Multiscale approximations of the stochastic dynamics of bumps perturbed by weak noise reveal the plasticity variables evolve to slowly diffusing and blurred versions of that arising in the stationary solution. Nonlinear Langevin equations associated with bump positions or interfaces coupled to slowly evolving projections of plasticity variables accurately describe the wandering of bumps underpinned by these smoothed synaptic efficacy profiles.
Endogenous Barriers to Learning
Authors: Olivier Compte
Date: 2023-06-29 00:00:00

Building on the idea that lack of experience is a source of errors but that experience should reduce them, we model agents' behavior using a stochastic choice model (logit quantal response), leaving endogenous the accuracy of their choices. In some games, higher accuracy leads to unstable logit-response dynamics. Starting from the lowest possible accuracy, we define the barrier to learning as the maximum accuracy which keeps the logit-response dynamic stable (for all lower accuracies). This defines a limit quantal response equilibrium. We apply the concept to centipede, travelers' dilemma, and 11-20 money-request games and to first-price and all-pay auctions, and discuss the role of strategy restrictions in reducing or amplifying barriers to learning.
Equal Pay for Similar Work
Authors: Diego Gentile Passaro, Fuhito Kojima, Bobak Pakzad-Hurson
Date: 2023-06-29 00:00:00

Equal pay laws increasingly require that workers doing "similar" work are paid equal wages within firm. We study such "equal pay for similar work" (EPSW) policies theoretically and test our model's predictions empirically using evidence from a 2009 Chilean EPSW. When EPSW only binds across protected class (e.g., no woman can be paid less than any similar man, and vice versa), firms segregate their workforce by gender. When there are more men than women in a labor market, EPSW increases the gender wage gap. By contrast, EPSW that is not based on protected class can decrease the gender wage gap.
Deep Learning-based F0 Synthesis for Speaker Anonymization
Authors: Ünal Ege Gaznepoglu, Nils Peters
Date: 2023-06-29 00:00:00
Tasks: Deep Learning, Speaker anonymization, Voice Conversion

Voice conversion for speaker anonymization is an emerging concept for privacy protection. In a deep learning setting, this is achieved by extracting multiple features from speech, altering the speaker identity, and waveform synthesis. However, many existing systems do not modify fundamental frequency (F0) trajectories, which convey prosody information and can reveal speaker identity. Moreover, mismatch between F0 and other features can degrade speech quality and intelligibility. In this paper, we formally introduce a method that synthesizes F0 trajectories from other speech features and evaluate its reconstructional capabilities. Then we test our approach within a speaker anonymization framework, comparing it to a baseline and a state-of-the-art F0 modification that utilizes speaker information. The results show that our method improves both speaker anonymity, measured by the equal error rate, and utility, measured by the word error rate.
Double Binnable RGB, RGBW and LMS Color Filter Arrays
Authors: Mritunjay Singh, Tripurari Singh
Date: 2023-06-29 00:00:00
Tasks: Demosaicking

New, high-resolution CMOS image sensors for mobile phones have moved beyond the single-binnable Quad Bayer and RGBW-Kodak patterns to the double binnable Hexadeca Bayer pattern featuring 4x4 tiles of like-colored pixels. Pixel binning enables high-speed, low-power readout in low-resolution modes and, more importantly, a reduction of read noise via floating diffusion binning. In this paper, we present the non-intuitive result that Nona and Hexadeca Bayer can be superior to Quad Bayer in demosaicking quality due to degeneracies in the latter's spectrum. However, Hexadeca Bayer suffers from the weakness of generating Quad Bayer after on round of binning. We introduce novel double binnable RGBW and LMS CFAs, composed of 2x2 tiles capable of 4:1 floating diffusion binning, that are free from spectral degeneracies and thus demosaic well in full resolution and both binned modes. RGBW offers a 6 dB low-light, read noise-limited, SNR advantage over Quad and Hexadeca Bayer in all resolution modes, while for LMS, the corresponding advantage is 4.2 dB.
Processing Energy Modeling for Neural Network Based Image Compression
Authors: Christian Herglotz, Fabian Brand, Andy Regensky, Felix Rievel, André Kaup
Date: 2023-06-29 00:00:00
Tasks: GPU, Image Compression

Nowadays, the compression performance of neural-networkbased image compression algorithms outperforms state-of-the-art compression approaches such as JPEG or HEIC-based image compression. Unfortunately, most neural-network based compression methods are executed on GPUs and consume a high amount of energy during execution. Therefore, this paper performs an in-depth analysis on the energy consumption of state-of-the-art neural-network based compression methods on a GPU and show that the energy consumption of compression networks can be estimated using the image size with mean estimation errors of less than 7%. Finally, using a correlation analysis, we find that the number of operations per pixel is the main driving force for energy consumption and deduce that the network layers up to the second downsampling step are consuming most energy.
A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition
Authors: Pavel Sinha, Ioannis Psaromiligkos, Zeljko Zilic
Date: 2023-06-29 00:00:00
Tasks: image-classification, Image Classification, Quantization

We propose a generalized convolutional neural network (CNN) architecture that first decomposes the input signal into subbands by an adaptive filter bank structure, and then uses convolutional layers to extract features from each subband independently. Fully connected layers finally combine the extracted features to perform classification. The proposed architecture restrains each of the subband CNNs from learning using the entire input signal spectrum, resulting in structural regularization. Our proposed CNN architecture is fully compatible with the end-to-end learning mechanism of typical CNN architectures and learns the subband decomposition from the input dataset. We show that the proposed CNN architecture has attractive properties, such as robustness to input and weight-and-bias quantization noise, compared to regular full-band CNN architectures. Importantly, the proposed architecture significantly reduces computational costs, while maintaining state-of-the-art classification accuracy. Experiments on image classification tasks using the MNIST, CIFAR-10/100, Caltech-101, and ImageNet-2012 datasets show that the proposed architecture allows accuracy surpassing state-of-the-art results. On the ImageNet-2012 dataset, we achieved top-5 and top-1 validation set accuracy of 86.91% and 69.73%, respectively. Notably, the proposed architecture offers over 90% reduction in computation cost in the inference path and approximately 75% reduction in back-propagation (per iteration) with just a single-layer subband decomposition. With a 2-layer subband decomposition, the computational gains are even more significant with comparable accuracy results to the single-layer decomposition.
Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar
Authors: Samia Kazemi, Bariscan Yonel, Birsen Yazıcı
Date: 2023-06-29 00:00:00
Tasks: Denoising, Retrieval

Incoherent processing for synthetic aperture radar (SAR) is a promising approach that enables low implementation costs, simplified hardware designs and operations in high frequency spectrum compared to the conventional imaging methods using coherent processing. Existing non-convex phaseless imaging algorithms offer recovery guarantees over limited range of forward models. In recent years, several deep learning (DL) based techniques have been introduced with the goal of extending applicability of phaseless imaging techniques to wave-based imaging modalities by addressing fundamental challenges, such as, lack of redundancy, non-uniqueness issues encountered commonly with inverse scattering models. In this paper, we introduce a DL-based phaseless SAR imaging approach that is designed under the premise that the spectral estimation technique, widely used for initializing non-convex phase retrieval algorithms, has significance far beyond generating good initial points. We extend the iterative power method for spectral estimation by using deep denoisers at each iteration, and subsequently design a deep imaging network within the plug-and-play framework. Finally, we verify the feasibility of our approach using synthetic SAR measurements.
Ongoing EEG artifact correction using blind source separation
Authors: Nicole Ille, Yoshiaki Nakao, Yano Shumpei, Toshiyuki Taura, Arndt Ebert, Harald Bornfleth, Suguru Asagi, Kanoko Kozawa, Izumi Itabashi, Takafumi Sato, Rie Sakuraba, Rie Tsuda, Yosuke Kakisaka, Kazutaka Jin, Nobukazu Nakasato
Date: 2023-06-29 00:00:00
Tasks: blind source separation, EEG, Electroencephalogram (EEG), Seizure Detection

Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. Methods: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. Results: The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. Conclusions: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. Significance: The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.
Sparse RF Lens Antenna Array Design for AoA Estimation in Wideband Systems: Placement Optimization and Performance Analysis
Authors: Joo-Hyun Jo, Jae-Nam Shim, Chan-Byoung Chae, Dong Ku Kim, Robert W. Heath Jr
Date: 2023-06-29 00:00:00

In this paper, we propose a novel architecture for a lens antenna array (LAA) designed to work with a small number of antennas and enable angle-of-arrival (AoA) estimation for advanced 5G vehicle-to-everything (V2X) use cases that demand wider bandwidths and higher data rates. We derive a received signal in terms of optical analysis to consider the variability of the focal region for different carrier frequencies in a wideband multi-carrier system. By taking full advantage of the beam squint effect for multiple pilot signals with different frequencies, we propose a novel reconfiguration of antenna array (RAA) for the sparse LAA and a max-energy antenna selection (MS) algorithm for the AoA estimation. In addition, this paper presents an analysis of the received power at the single antenna with the maximum energy and compares it to simulation results. In contrast to previous studies on LAA that assumed a large number of antennas, which can require high complexity and hardware costs, the proposed RAA with MS estimation algorithm is shown meets the requirements of 5G V2X in a vehicular environment while utilizing limited RF hardware and has low complexity.
AoA-based Position and Orientation Estimation Using Lens MIMO in Cooperative Vehicle-to-Vehicle Systems
Authors: Joo-Hyun Jo, Jae-Nam Shim, Byoungnam, Kim, Chan-Byoung Chae, Dong Ku Kim
Date: 2023-06-29 00:00:00
Tasks: Position

Positioning accuracy is a critical requirement for vehicle-to-everything (V2X) use cases. Therefore, this paper derives the theoretical limits of estimation for the position and orientation of vehicles in a cooperative vehicle-to-vehicle (V2V) scenario, using a lens-based multiple-input multiple-output (lens-MIMO) system. Following this, we analyze the Cram$\acute{\text{e}}$r-Rao lower bounds (CRLBs) of the position and orientation estimation and explore a received signal model of a lens-MIMO for the particular angle of arrival (AoA) estimation with a V2V geometric model. Further, we propose a lower complexity AoA estimation technique exploiting the unique characteristics of the lens-MIMO for a single target vehicle; as a result, its estimation scheme is effectively extended by the successive interference cancellation (SIC) method for multiple target vehicles. Given these AoAs, we investigate the lens-MIMO estimation capability for the positions and orientations of vehicles. Subsequently, we prove that the lens-MIMO outperforms a conventional uniform linear array (ULA) in a certain configuration of a lens's structure. Finally, we confirm that the proposed localization algorithm is superior to ULA's CRLB as the resolution of the lens increases in spite of the lower complexity.
Radar Enhanced Multi-Armed Bandit for Rapid Beam Selection in Millimeter Wave Communications
Authors: Akanksha Sneh, Sumit Darak, Shobha Sundar Ram, Manjesh Hanawal
Date: 2023-06-29 00:00:00

Multi-arm bandit (MAB) algorithms have been used to learn optimal beams for millimeter wave communication systems. Here, the complexity of learning the optimal beam linearly scales with the number of beams, leading to high latency when there are a large number of beams. In this work, we propose to integrate radar with communication to enhance the MAB learning performance by searching only those beams where the radar detects a scatterer. Further, we use radar to distinguish the beams that show mobile targets from those which indicate the presence of static clutter, thereby reducing the number of beams to scan. Simulations show that our proposed radar-enhanced MAB reduces the exploration time by searching only the beams with distinct radar mobile targets resulting in improved throughput.
Radar-Based Respiratory Measurement of a Rhesus Monkey by Suppressing Nonperiodic Body Motion Components
Authors: Takuya Sakamoto, Daisuke Sanematsu, Itsuki Iwata, Toshiki Minami, Masako Myowa
Date: 2023-06-29 00:00:00

We propose a method to measure the respiration of a rhesus monkey using a millimeter-wave radar system with an antenna array. Unlike humans, small animals are generally restless and hyperactive in nature, and suppression of their body motion components is thus necessary to realize accurate respiratory measurements. The proposed method detects and suppresses nonperiodic body motion components while also combining and emphasizing the periodic components from multiple echoes acquired from the target. Results indicate that the proposed method can measure respiration rate of the target monkey accurately, even with frequent body movements.
Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formula
Authors: Kirill Brilliantov, Fedor Pavutnitskiy, Dmitry Pasechnyuk, German Magai
Date: 2023-06-01 00:00:00
Tasks: Language Modelling

Computing homotopy groups of spheres has long been a fundamental objective in algebraic topology. Various theoretical and algorithmic approaches have been developed to tackle this problem. In this paper we take a step towards the goal of comprehending the group-theoretic structure of the generators of these homotopy groups by leveraging the power of machine learning. Specifically, in the simplicial group setting of Wu's formula, we reformulate the problem of generating simplicial cycles as a problem of sampling from the intersection of algorithmic datasets related to Dyck languages. We present and evaluate language modelling approaches that employ multi-label information for input sequences, along with the necessary group-theoretic toolkit and non-neural baselines.
Data-driven Multiperiod Robust Mean-Variance Optimization
Authors: Xin Hai, Gregoire Loeper, Kihun Nam
Date: 2023-06-29 00:00:00

We study robust mean-variance optimization in multiperiod portfolio selection by allowing the true probability measure to be inside a Wasserstein ball centered at the empirical probability measure. Given the confidence level, the radius of the Wasserstein ball is determined by the empirical data. The numerical simulations of the US stock market provide a promising result compared to other popular strategies.
A Quantitative Functional Central Limit Theorem for Shallow Neural Networks
Authors: Valentina Cammarota, Domenico Marinucci, Michele Salvi, Stefano Vigogna
Date: 2023-06-29 00:00:00

We prove a Quantitative Functional Central Limit Theorem for one-hidden-layer neural networks with generic activation function. The rates of convergence that we establish depend heavily on the smoothness of the activation function, and they range from logarithmic in non-differentiable cases such as the Relu to $\sqrt{n}$ for very regular activations. Our main tools are functional versions of the Stein-Malliavin approach; in particular, we exploit heavily a quantitative functional central limit theorem which has been recently established by Bourguin and Campese (2020).
A phase-field model for active contractile surfaces
Authors: Sebastian Aland, Claudia Wohlgemuth
Date: 2023-06-29 00:00:00
Tasks: Contact mechanics, model

The morphogenesis of cells and tissues involves an interplay between chemical signals and active forces on their surrounding surface layers. The complex interaction of hydrodynamics and material flows on such active surfaces leads to pattern formation and shape dynamics which can involve topological transitions, for example during cell division. To better understand such processes requires novel numerical tools. Here, we present a phase-field model for an active deformable surface interacting with the surrounding fluids. The model couples hydrodynamics in the bulk to viscous flow along the diffuse surface, driven by active contraction of a surface species. As a new feature in phase-field modeling, we include the viscosity of a diffuse interface and stabilize the interface profile in the Stokes-Cahn-Hilliard equation by an auxiliary advection velocity, which is constant normal to the interface. The method is numerically validated with previous results based on linear stability analysis. Further, we highlight some distinct features of the new method, like the avoidance of re-meshing and the inclusion of contact mechanics, as we simulate the self-organized polarization and migration of a cell through a narrow channel. Finally, we study the formation of a contractile ring on the surface and illustrate the capability of the method to resolve topological transitions by a first simulation of a full cell division.
Are task representations gated in macaque prefrontal cortex?
Authors: Timo Flesch, Valerio Mante, William Newsome, Andrew Saxe, Christopher Summerfield, David Sussillo
Date: 2023-06-29 00:00:00

A recent paper (Flesch et al, 2022) describes behavioural and neural data suggesting that task representations are gated in the prefrontal cortex in both humans and macaques. This short note proposes an alternative explanation for the reported results from the macaque data.