pwc-archive/papers-with-abstracts huggingface dataset:
Process, Bias and Temperature Scalable CMOS Analog Computing Circuits for Machine Learning
Authors: Pratik Kumar, Ankita Nandi, Shantanu Chakrabartty, Chetan Singh Thakur
Date: 2022-05-11 00:00:00
Tasks: BIG-bench Machine Learning

Analog computing is attractive compared to digital computing due to its potential for achieving higher computational density and higher energy efficiency. However, unlike digital circuits, conventional analog computing circuits cannot be easily mapped across different process nodes due to differences in transistor biasing regimes, temperature variations and limited dynamic range. In this work, we generalize the previously reported margin-propagation-based analog computing framework for designing novel \textit{shape-based analog computing} (S-AC) circuits that can be easily cross-mapped across different process nodes. Similar to digital designs S-AC designs can also be scaled for precision, speed, and power. As a proof-of-concept, we show several examples of S-AC circuits implementing mathematical functions that are commonly used in machine learning (ML) architectures. Using circuit simulations we demonstrate that the circuit input/output characteristics remain robust when mapped from a planar CMOS 180nm process to a FinFET 7nm process. Also, using benchmark datasets we demonstrate that the classification accuracy of a S-AC based neural network remains robust when mapped across the two processes and to changes in temperature.
Computing control invariant sets of nonlinear systems: decomposition and distributed computing
Authors: Benjamin Decardi-Nelson, Jinfeng Liu
Date: 2022-05-11 00:00:00
Tasks: Distributed Computing

In this work, we present a distributed framework based on the graph algorithm for computing control invariant set for nonlinear cascade systems. The proposed algorithm exploits the structure of the interconnections within a process network. First, the overall system is decomposed into several subsystems with overlapping states. Second, the control invariant set for the subsystems are computed in a distributed manner. Finally, an approximation of the control invariant set for the overall system is reconstructed from the subsystem solutions and validated. We demonstrate the efficacy and convergence of the proposed method to the centralized graph-based algorithm using several numerical examples including a six dimensional continuous stirred tank reactor system.
Reconstructing 3D Model from Single-View Sketch with Deep Neural Network
Authors: Fei Wang, Yu Yang, Baoquan Zhao, Dazhi Jiang, Siwei Chen, Jianqiang Sheng
Date: 2021-04-27 00:00:00
Proceeding: International Conference on Digital Home 2021 4
Tasks: 3D Reconstruction, 3D Shape Reconstruction

In this paper, we introduce a novel 3D shape reconstruction method from a single-view sketch image based on a deep neural network. The proposed pipeline is mainly composed of three modules. The first module is sketch component segmentation based on multimodal DNN fusion and is used to segment a given sketch into a series of basic units and build a transformation template by the knots between them. The second module is a nonlinear transformation network for multifarious sketch generation with the obtained transformation template. It creates the transformation representation of a sketch by extracting the shape features of an input sketch and transformation template samples. The third module is deep 3D shape reconstruction using multifarious sketches, which takes the obtained sketches as input to reconstruct 3D shapes with a generative model. It fuses and optimizes features of multiple views and thus is more likely to generate high-quality 3D shapes. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on a public 3D reconstruction dataset. The results demonstrate that our model can achieve better reconstruction performance than peer methods. Specifically, compared to the state-of-the-art method, the proposed model achieves a performance gain in terms of the five evaluation metrics by an average of 25.5% on the man-made model dataset and 23.4% on the character object dataset using synthetic sketches and by an average of 31.8% and 29.5% on the two datasets, respectively, using human drawing sketches.
DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers
Authors: Abdul Wahab, Rafet Sifa
Date: 2021-12-05 00:00:00
Proceeding: IEEE SSCI 2021 12
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, Natural Language Inference, Representation Learning, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentiment Analysis

Prior research in the area of Natural Language Processing (NLP) has shown that including the syntactic structure of a sentence using a dependency parse tree while training a representation learning model improves the performance on downstream tasks. However, most of these modeling approaches make use of the dependency parse tree of sentences for learning task-specific word representations rather than considering that for learning generic representations. In this paper, we propose a new model named DIBERT which stands for Dependency Injected Bidirectional Encoder Representations from Transformers. DIBERT is a variation of the BERT, that apart from Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) also incorporates an additional third objective called Parent Prediction (PP). PP injects the syntactic structure of a dependency tree while pre-training the DIBERT, which generates syntax-aware generic representations. We use the WikiText-103 benchmark dataset to pre-train both the original BERT (BERT-Base) and the proposed DIBERT models. After fine-tuning, we observe that DIBERT performs better than BERT-Base on various NLP downstream tasks including Semantic Similarity, Natural Language Inference and Sentiment Analysis hinting at the fact that incorporating dependency information when learning textual representations can improve the quality of the learned representations.
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature
Authors: Qian Wan, Liang Gao, Xinyu Li, long wen
Date: 2021-01-06 00:00:00
Proceeding: IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021 1
Tasks: Anomaly Detection, Anomaly Localization, Clustering

Anomaly localization is valuable for improvement of complex production processing in smart manufacturing system. As the distribution of anomalies is unknowable and labeled data is few, unsupervised methods based on convolutional neural network (CNN) have been studied for anomaly localization. But there are still problems for real industrial applications, in terms of localization accuracy, computation time, and memory storage. This article proposes a novel framework called as Gaussian clustering of pretrained feature (GCPF), including the clustering and inference stage, for anomaly localization in unsupervised way. The GCPF consists of three modules which include pretrained deep feature extraction (PDFE), multiple independent multivariate Gaussian clustering (MIMGC), and multihierarchical anomaly scoring (MHAS). In the clustering stage, features of normal images are extracted by pretrained CNN at the PDFE module, and then clustered at the MIMGC module. In the inference stage, features of target images are extracted and then scored for anomaly localization at the MHAS module. The GCPF is compared with the state-of-the-art methods on MVTec dataset, achieving receiver operating characteristic curve of 96.86% over all 15 categories, and extended to NanoTWICE and DAGM datasets. The GCPF outperforms the compared methods for unsupervised anomaly localization, and significantly reserves the low computation complexity and online memory storage which are important for real industrial applications.
Multi-Step Reinforcement Learning for Single Image Super-Resolution
Authors: Kyle Vassilo, Cory Heatwole, Tarek Taha, Asif Mehmood
Date: 2020-07-28 00:00:00
Proceeding: IEEE/CVF 2020 7
Tasks: Image Super-Resolution, Multi-agent Reinforcement Learning, object-detection, Object Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Super-Resolution

Deep Learning (DL) has become prevalent in today's image processing research due to its power and versatility. It has dominated the Single Image Super-Resolution (SISR) field with its ability to obtain High-Resolution (HR) images from their Low-Resolution (LR) counterparts, particularly using Generative Adversarial Networks (GANs). Interest in SISR comes from its potential to increase the performance of supplementary image processing tasks such as object detection, localization, and classification. This research applies a multi-agent Reinforcement Learning (RL) algorithm to SISR, creating an advanced ensemble approach for combining powerful GANs. In our implementation each agent chooses a particular action from a fixed action set comprised of results from existing GAN SISR algorithms to update its pixel values. The pixel-wise or patch-wise arrangement of agents and rewards encourages the algorithm to learn a strategy to increase the resolution of an image by choosing the best pixel values from each option.
Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning
Authors: Jay Cao, Jacky Chen, Soroush Farghadani, John Hull, Zissis Poulos, Zeyu Wang, Jun Yuan
Date: 2022-05-10 00:00:00
Tasks: Distributional Reinforcement Learning, Position, quantile regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfolio delta neutral at the end of each day by taking a position in the underlying asset. We focus on how trades in the options can be used to manage gamma and vega. The option trades are subject to transaction costs. We consider three different objective functions. We reach conclusions on how the optimal hedging strategy depends on the trader's objective function, the level of transaction costs, and the maturity of the options used for hedging. We also investigate the robustness of the hedging strategy to the process assumed for the underlying asset.
Learning a Better Control Barrier Function
Authors: Bolun Dai, Prashanth Krishnamurthy, Farshad Khorrami
Date: 2022-05-11 00:00:00
Tasks: valid

Control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constraints and for high relative degree systems. Meanwhile, finding a conservative CBF that only recovers a portion of the true safe set is usually possible. In this work, starting from a "conservative" handcrafted CBF (HCBF), we develop a method to find a CBF that recovers a reasonably larger portion of the safe set. Since the learned CBF controller is not guaranteed to be safe during training iterations, we use a model predictive controller (MPC) to ensure safety during training. Using the collected trajectory data containing safe and unsafe interactions, we train a neural network to estimate the difference between the HCBF and a CBF that recovers a closer solution to the true safe set. With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient. We validate our approach on two systems: a second-order integrator and a ball-on-beam.
A deep representation learning speech enhancement method using $β$-VAE
Authors: Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Mads Græsbøll Christensen
Date: 2022-05-11 00:00:00
Tasks: Disentanglement, Representation Learning, Speech Enhancement

In previous work, we proposed a variational autoencoder-based (VAE) Bayesian permutation training speech enhancement (SE) method (PVAE) which indicated that the SE performance of the traditional deep neural network-based (DNN) method could be improved by deep representation learning (DRL). Based on our previous work, we in this paper propose to use $\beta$-VAE to further improve PVAE's ability of representation learning. More specifically, our $\beta$-VAE can improve PVAE's capacity of disentangling different latent variables from the observed signal without the trade-off problem between disentanglement and signal reconstruction. This trade-off problem widely exists in previous $\beta$-VAE algorithms. Unlike the previous $\beta$-VAE algorithms, the proposed $\beta$-VAE strategy can also be used to optimize the DNN's structure. This means that the proposed method can not only improve PVAE's SE performance but also reduce the number of PVAE training parameters. The experimental results show that the proposed method can acquire better speech and noise latent representation than PVAE. Meanwhile, it also obtains a higher scale-invariant signal-to-distortion ratio, speech quality, and speech intelligibility.
Beyond Griffin-Lim: Improved Iterative Phase Retrieval for Speech
Authors: Tal Peer, Simon Welker, Timo Gerkmann
Date: 2022-05-11 00:00:00
Tasks: Retrieval

Phase retrieval is a problem encountered not only in speech and audio processing, but in many other fields such as optics. Iterative algorithms based on non-convex set projections are effective and frequently used for retrieving the phase when only STFT magnitudes are available. While the basic Griffin-Lim algorithm and its variants have been the prevalent method for decades, more recent advances, e.g. in optics, raise the question: Can we do better than Griffin-Lim for speech signals, using the same principle of iterative projection? In this paper we compare the classical algorithms in the speech domain with two modern methods from optics with respect to reconstruction quality and convergence rate. Based on this study, we propose to combine Griffin-Lim with the Difference Map algorithm in a hybrid approach which shows superior results, in terms of both convergence and quality of the final reconstruction.
Benefits of Feedforward for Model Predictive Airpath Control of Diesel Engines
Authors: Jiadi Zhang, Mohammad Reza Amini, Ilya Kolmanovsky, Munechika Tsutsumi, Hayato Nakada
Date: 2022-05-11 00:00:00
Tasks: Model Predictive Control

This paper investigates options to complement a diesel engine airpath feedback controller with a feedforward. The control objective is to track the intake manifold pressure and exhaust gas recirculation (EGR) rate targets by manipulating the EGR valve and variable geometry turbine (VGT) while satisfying state and input constraints. The feedback controller is based on rate-based Model Predictive Control (MPC) that provides integral action for tracking. Two options for the feedforward are considered one based on a look-up table that specifies the feedforward as a function of engine speed and fuel injection rate, and another one based on a (non-rate-based) MPC that generates dynamic feedforward trajectories. The controllers are designed and verified using a high-fidelity engine model in GT-Power and exploit a low-order rate-based linear parameter-varying (LPV) model for prediction which is identified from transient response data generated by the GT-Power model. It is shown that the combination of feedforward and feedback MPC has the potential to improve the performance and robustness of the control design. In particular, the feedback MPC without feedforward can lose stability at low engine speeds, while MPC-based feedforward results in the best transient response. Mechanisms by which feedforward is able to assist in stabilization and improve performance are discussed.
Final Iteration Convergence Bound of Q-Learning: Switching System Approach
Authors: Donghwna Lee
Date: 2022-05-11 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Q-learning is known as one of the fundamental reinforcement learning (RL) algorithms. Its convergence has been the focus of extensive research over the past several decades. Recently, a new finitetime error bound and analysis for Q-learning was introduced using a switching system framework. This approach views the dynamics of Q-learning as a discrete-time stochastic switching system. The prior study established a finite-time error bound on the averaged iterates using Lyapunov functions, offering further insights into Q-learning. While valuable, the analysis focuses on error bounds of the averaged iterate, which comes with the inherent disadvantages: it necessitates extra averaging steps, which can decelerate the convergence rate. Moreover, the final iterate, being the original format of Q-learning, is more commonly used and is often regarded as a more intuitive and natural form in the majority of iterative algorithms. In this paper, we present a finite-time error bound on the final iterate of Q-learning based on the switching system framework. The proposed error bounds have different features compared to the previous works, and cover different scenarios. Finally, we expect that the proposed results provide additional insights on Q-learning via connections with discrete-time switching systems, and can potentially present a new template for finite-time analysis of more general RL algorithms.
Strong Sign Controllability of Diffusively-Coupled Networks
Authors: Nam-Jin Park, Seong-Ho Kwon, Yoo-Bin Bae, Byeong-Yeon Kim, Kevin L. Moore, Hyo-Sung Ahn
Date: 2022-05-11 00:00:00

This paper presents several conditions to determine strong sign controllability for diffusively-coupled undirected networks. The strong sign controllability is determined by the sign patterns (positive, negative, zero) of the edges. We first provide the necessary and sufficient conditions for strong sign controllability of basic components, such as path, cycle, and tree. Next, we propose a merging process to extend the basic componenets to a larger graph based on the conditions of the strong sign controllability. Furthermore, we develop an algorithm of polynomial complexity to find the minimum number of external input nodes while maintaining the strong sign controllability of a network.
Sparsity Based Non-Contact Vital Signs Monitoring of Multiple People Via FMCW Radar
Authors: Yonathan Eder, Yonina C. Eldar
Date: 2022-05-10 00:00:00

Non-contact technology for monitoring multiple people's vital signs, such as respiration and heartbeat, has been investigated in recent years due to the rising cardiopulmonary morbidity, the risk of transmitting diseases, and the heavy burden on the medical staff. Frequency modulated continuous wave (FMCW) radars have shown great promise in meeting these needs. However, contemporary techniques for non-contact vital signs monitoring (NCVSM) via FMCW radars, are based on simplistic models, and present difficulties coping with noisy environments containing multiple objects. In this work, we develop an extended model of FMCW radar signals in a noisy setting containing multiple people and clutter. By utilizing the sparse nature of the modeled signals in conjunction with human-typical cardiopulmonary features, we can accurately localize humans and reliably monitor their vital signs, using only a single channel and a single-input-single-output setup. To this end, we first show that spatial sparsity allows for both accurate detection of multiple people and computationally efficient extraction of their Doppler samples, using a joint sparse recovery approach. Given the extracted samples, we develop a method named Vital Signs based Dictionary Recovery (VSDR), which uses a dictionary-based approach to search for the desired rates of respiration and heartbeat over high-resolution grids corresponding to normal cardiopulmonary activity. The advantages of the proposed method are illustrated through examples that combine the proposed model with real data of $30$ monitored individuals. We demonstrate accurate human localization in a clutter-rich scenario that includes both static and vibrating objects, and show that our VSDR approach outperforms existing techniques, based on several statistical metrics. The findings support the widespread use of FMCW radars with the proposed algorithms in healthcare.
Vibration-Based Bridge Health Monitoring using Telecommunication Cables
Authors: Jingxiao Liu, Siyuan Yuan, Bin Luo, Biondo Biondi, Hae Young Noh
Date: 2022-05-10 00:00:00

Bridge Health Monitoring (BHM) enables early damage detection of bridges and is thus critical for avoiding more severe damages that might result in major financial and human losses. However, conventional BHM systems require dedicated sensors on bridges, which is costly to install and maintain and hard to scale up. To overcome this challenge, we introduce a new system that uses existing telecommunication cables for Distributed Acoustic Sensing (DAS) to collect bridge dynamic strain responses. In addition, we develop a two-module physics-guided system identification method to extract bridge damage-sensitive information (e.g., natural frequencies and mode shapes) from noisy DAS data by constraining strain and displacement mode shapes by bridge dynamics. This approach does not require installation and maintenance of dedicated sensors on bridges. We evaluate our system with field experiments on a concrete bridge with fiber cable running in a conduit under the deck. Our system successfully identified modal frequencies and reconstructed meter-scale mode shapes.
Gamma Rhythm Analysis and Simulation Using Neuron Models
Authors: Evgeniia S. Sevasteeva, Sergei A. Plotnikov, Dmitry R. Belov
Date: 2022-05-10 00:00:00
Tasks: Rhythm

Neural oscillations are electrical activities of the brain measurable at different frequencies. This paper studies the interaction between the fast and slow processes in the brain. We recorded signals intracranially from the simple Wistar rats, performed the signal processing, and computed the correlation between envelopes of the high-frequency gamma rhythm and a low-frequency ECoG signal. The analysis shows that the low-frequency signal (delta rhythm) modulates the gamma rhythm with a small time delay. Further, we used simple excitable neuron models, namely FitzHugh-Nagumo and Hindmarsh-Rose, to simulate the gamma rhythm. The low-frequency signal delta rhythm can be used as the input to affect the threshold and simulate gamma rhythm using these neuron models.
An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures
Authors: Peter Vouras, Kumar Vijay Mishra, Alexandra Artusio-Glimpse, Samuel Pinilla, Angeliki Xenaki, David W. Griffith, Karen Egiazarian
Date: 2022-05-11 00:00:00

Rapid developments in synthetic aperture (SA) systems, which generate a larger aperture with greater angular resolution than is inherently possible from the physical dimensions of a single sensor alone, are leading to novel research avenues in several signal processing applications. The SAs may either use a mechanical positioner to move an antenna through space or deploy a distributed network of sensors. With the advent of new hardware technologies, the SAs tend to be denser nowadays. The recent opening of higher frequency bands has led to wide SA bandwidths. In general, new techniques and setups are required to harness the potential of wide SAs in space and bandwidth. Herein, we provide a brief overview of emerging signal processing trends in such spatially and spectrally wideband SA systems. This guide is intended to aid newcomers in navigating the most critical issues in SA analysis and further supports the development of new theories in the field. In particular, we cover the theoretical framework and practical underpinnings of wideband SA radar, channel sounding, sonar, radiometry, and optical applications. Apart from the classical SA applications, we also discuss the quantum electric-field-sensing probes in SAs that are currently undergoing active research but remain at nascent stages of development.