pwc-archive/papers-with-abstracts huggingface dataset:
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles
Authors: Yongqian Xiao, Xinglong Zhang, Xin Xu, Xueqing Liu, Jiahang Liu
Date: 2020-07-05 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

Autonomous driving technologies have received notable attention in the past decades. In autonomous driving systems, identifying a precise dynamical model for motion control is nontrivial due to the strong nonlinearity and uncertainty in vehicle dynamics. Recent efforts have resorted to machine learning techniques for building vehicle dynamical models, but the generalization ability and interpretability of existing methods still need to be improved. In this paper, we propose a data-driven vehicle modeling approach based on deep neural networks with an interpretable Koopman operator. The main advantage of using the Koopman operator is to represent the nonlinear dynamics in a linear lifted feature space. In the proposed approach, a deep learning-based extended dynamic mode decomposition algorithm is presented to learn a finite-dimensional approximation of the Koopman operator. Furthermore, a data-driven model predictive controller with the learned Koopman model is designed for path tracking control of autonomous vehicles. Simulation results in a high-fidelity CarSim environment show that our approach exhibit a high modeling precision at a wide operating range and outperforms previously developed methods in terms of modeling performance. Path tracking tests of the autonomous vehicle are also performed in the CarSim environment and the results show the effectiveness of the proposed approach.
A BCS-GDE Algorithm for Multi-objective Optimization of Combined Cooling, Heating and Power Model
Authors: Jiaze Sun, Jiahui Deng, Yang Li, Shuaiyin Ma, Nan Han
Date: 2021-08-17 00:00:00
Tasks: Decision Making, Diversity

District energy systems can not only reduce energy consumption but also set energy supply dispatching schemes according to demand. In this paper, the combined cooling heating and power economic emission dispatch (CCHPEED) model is established with the objective of economic cost, primary energy consumption, and pollutant emissions, as well as three decision-making strategies, are proposed to meet the demand for energy supply. Besides, a generalized differential evolution with the best compromise solution processing mechanism (BCS-GDE) is proposed to solve the model, also, the best compromise solution processing mechanism is put forward in the algorithm. In the simulation, the resource dispatching is performed according to the different energy demands of hotels, offices, and residential buildings on the whole day. The simulation results show that the model established in this paper can reduce the economic cost, energy consumption, and pollutant emission, in which the maximum reduction rate of economic cost is 72%, the maximum reduction rate of primary energy consumption is 73%, and the maximum reduction rate of pollutant emission is 88%. Concurrently, BCS-GDE also has better convergence and diversity than the classic algorithms.
Iterative learning control with discrete-time nonlinear nonminimum phase models via stable inversion
Authors: Isaac A Spiegel, Nard Strijbosch, Tom Oomen, Kira Barton
Date: 2021-08-16 00:00:00

Output reference tracking can be improved by iteratively learning from past data to inform the design of feedforward control inputs for subsequent tracking attempts. This process is called iterative learning control (ILC). This article develops a method to apply ILC to systems with nonlinear discrete-time dynamical models with unstable inverses (i.e. discrete-time nonlinear non-minimum phase models). This class of systems includes piezoactuators, electric power converters, and manipulators with flexible links, which may be found in nanopositioning stages, rolling mills, and robotic arms, respectively. As these devices may be required to execute fine transient reference tracking tasks repetitively in contexts such as manufacturing, they may benefit from ILC. Specifically, this article facilitates ILC of such systems by presenting a new ILC synthesis framework that allows combination of the principles of Newton's root finding algorithm with stable inversion, a technique for generating stable trajectories from unstable models. The new framework, called Invert-Linearize ILC (ILILC), is validated in simulation on a cart-and-pendulum system with model error, process noise, and measurement noise. Where preexisting Newton-based ILC diverges, ILILC with stable inversion converges, and does so in less than one third the number of trials necessary for the convergence of a gradient-descent-based ILC technique used as a benchmark.
Analysis of Data Mining Process for Improvement of Production Quality in Industrial Sector
Authors: Hamza Saad, Nagendra Nagarur, Abdulrahman Shamsan
Date: 2021-08-17 00:00:00
Tasks: Management

Background and Objective: Different industries go through high-precision and complex processes that need to analyze their data and discover defects before growing up. Big data may contain large variables with missed data that play a vital role to understand what affect the quality. So, specialists of the process might be struggling to defined what are the variables that have direct effect in the process. Aim of this study was to build integrated data analysis using data mining and quality tools to improve the quality of production and process. Materials and Methods: Data collected in different steps to reduce missed data. The specialists in the production process recommended to select the most important variables from big data and then predictor screening was used to confirm 16 of 71 variables. Seven important variables built the output variable that called textile quality score. After testing ten algorithms, boosted tree and random forest were evaluated to extract knowledge. In the voting process, three variables were confirmed to use as input factors in the design of experiments. The response of design was estimated by data mining and the results were confirmed by the quality specialists. Central composite (surface response) has been run 17 times to extract the main effects and interactions on the textile quality score. Results: Current study found that a machine productivity has negative effect on the quality, so this validated by the management. After applying changes, the efficiency of production has improved 21%. Conclusion: Results confirmed a big improvement in quality processes in industrial sector. The efficiency of production improved to 21%, weaving process improved to 23% and the overall process improved to 17.06%.
Choice by Rejection
Authors: Bhavook Bhardwaj, Kriti Manocha
Date: 2021-08-17 00:00:00

We propose a boundedly rational model of choice where agents eliminate dominated alternatives using a transitive rationale before making a choice using a complete rationale. This model is related to the seminal two-stage model of Manzini and Mariotti (2007), the Rational Shortlist Method (RSM). We analyze the model through reversals in choice and provide its behavioral characterization. The procedure satisfies a weaker version of the Weak Axiom of Revealed Preference (WARP) allowing for at most two reversals in choice in terms of set inclusion for any pair of alternatives. We show that the underlying rationales can be identified from the observable reversals in the choice. We also characterize a variant of this model in which both the rationales are transitive
A technique to enable frequency dependent power savings in a level crossing analog-to-digital converter
Authors: Lucas Moura Santana, Duarte Lopes de Oliveira, Lester de Abreu Faria
Date: 2021-08-17 00:00:00

The level crossing analog-to-digital converters are meant for the effective conversion of sparse signals by construction. In these converters, the bandwidth-power trade-off requires a re-design of the comparators which takes a lot of time and effort to reach the application optimum point. Inspired by synchronous converters that have a dynamic power component that can be traded with bandwidth with the change of a clock frequency, a technique to allow such trade-off in the level crossing converter was developed. The resulting level crossing ADC has an input signal dependent dynamic power which can reach up to 42\% OFF time during the conversion of sine waves, achieving 45.5% power reduction in the simulated design with TSMC 180nm PDK.
0.8% Nyquist computational ghost imaging via non-experimental deep learning
Authors: Haotian Song, Xiaoyu Nie, Hairong Su, Hui Chen, Yu Zhou, Xingchen Zhao, Tao Peng, Marlan O. Scully
Date: 2021-08-17 00:00:00
Tasks: 3D Face Modelling, Deep Learning, Domain Generalization, Image Reconstruction, Language Modelling, Low-Light Image Enhancement, Open-Domain Question Answering, Overlapped 19-1, Question Answering

We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained merely by simulation. To demonstrate the sub-Nyquist level in our work, the conventional computational ghost imaging results, reconstructed imaging results using white noise and pink noise via deep learning are compared under multiple sampling rates at different noise conditions. We show that the proposed scheme can provide high-quality images with a sampling rate of 0.8% even when the object is outside the training dataset, and it is robust to noisy environments. This method is excellent for various applications, particularly those that require a low sampling rate, fast reconstruction efficiency, or experience strong noise interference.
Near-field Wireless Power Transfer for 6G Internet-of-Everything Mobile Networks: Opportunities and Challenges
Authors: Haiyang Zhang, Nir Shlezinger, Francesco Guidi, Davide Dardari, Mohammadreza F. Imani, Yonina C. Eldar
Date: 2021-08-17 00:00:00

Radiating wireless power transfer (WPT) brings forth the possibility to cost-efficiently charge wireless devices without requiring a wiring infrastructure. As such, it is expected to play a key role in the deployment of limited-battery communicating devices, as part of the 6G enabled Internet-of-Everything (IoE) vision. To date, radiating WPT technologies are mainly studied and designed assuming that the devices are located in the far-field region of the power radiating antenna, resulting in a relatively low energy transfer efficiency. However, with the transition of 6G systems to mmWave frequencies combined with the usage of large-scale antennas, future WPT devices are likely to operate in the radiating near-field (Fresnel) region. In this article, we provide an overview of the opportunities and challenges which arise from radiating near-field WPT. In particular, we discuss about the possibility to realize beam focusing in near-field radiating conditions, and highlight its possible implications for WPT in future {IoE} networks. Besides, we overview some of the design challenges and research directions which arise from this emerging paradigm, including its simultaneous operation with wireless communications, radiating waveform considerations, hardware aspects, and operation with typical antenna architectures.
A 1V 5-bits Low Power Level Crossing ADC with OFF state in idle time for bio-medical applications in 0.18um CMOS
Authors: Lucas Moura Santana, Duarte Lopes de Oliveira, Lester de Abreu Faria
Date: 2021-08-17 00:00:00

The ubiquitous use of sensing and signal processing is increasing exponentially with the advance of the Internet of Everything (IoE). In this context, the design of every time more power efficient sensor nodes is a must. Within these nodes, one of the most power-hungry components are the analog-to-digital converters (ADC). These components are used everywhere to translate real-world analog signals into computer intelligible digital signals. One of the promising architecture for the sensing of physiological signals is the level crossing ADC due to the sparse characteristics of those signals. One of the challenges to improve the power efficiency of this type of ADC lies in the use of continuous comparators to keep track of the input signal within the voltage references. The aim of this work is to investigate the impact of using continuous comparator which can be turned off without incurring error to the conversion of the level crossing ADC. New boundaries will be set for the correct behavior of the level crossing ADC together with the conditions for power saving with the proposed architecture. A 1V 5-bits level crossing ADC was implemented using the TSMC 0.18um process and fabricated for laboratory measurements. The ADC consumes 12.2uW during tracking state and with the proposed technique, the reduction of the average power can go from 4.2% to 45.5% depending on the activity and the type of the input signal.
Task-Oriented Multi-User Semantic Communications for VQA Task
Authors: Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li
Date: 2021-08-16 00:00:00
Tasks: Question Answering, Semantic Communication, Visual Question Answering, Visual Question Answering (VQA)

Semantic communications focus on the transmission of semantic features. In this letter, we consider a task-oriented multi-user semantic communication system for multimodal data transmission. Particularly, partial users transmit images while the others transmit texts to inquiry the information about the images. To exploit the correlation among the multimodal data from multiple users, we propose a deep neural network enabled semantic communication system, named MU-DeepSC, to execute the visual question answering (VQA) task as an example. Specifically, the transceiver for MU-DeepSC is designed and optimized jointly to capture the features from the correlated multimodal data for task-oriented transmission. Simulation results demonstrate that the proposed MU-DeepSC is more robust to channel variations than the traditional communication systems, especially in the low signal-to-noise (SNR) regime.
New views of old proteins: clarifying the enigmatic proteome
Authors: Participants in a NIH Workshop on Functional, Integrative Proteomics, :, Kristin E. Burnum Johnson, Thomas P. Conrads, Richard R. Drake, Amy E. Herr, Ravi Iyengar, Ryan T. Kelly, Emma Lundberg, Michael J. MacCoss, Alexandra Naba, Garry P. Nolan, Pavel A. Pevzner, Karin D. Rodland, Salvatore Sechi, Nikolai Slavov, Jeffrey M. Spraggins, Jennifer E. Van Eyk, Marc Vidal, Christine Vogel, David R. Walt, Neil L. Kelleher
Date: 2021-08-17 00:00:00

All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide provocative projections for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we offer potential futures for a next generation of disruptive approaches to define, quantify and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.
Bayesian and Algebraic Strategies to Design in Synthetic Biology
Authors: Robyn P. Araujo, Sean T. Vittadello, Michael P. H. Stumpf
Date: 2021-08-17 00:00:00
Tasks: Ingenuity

Innovation in synthetic biology often still depends on large-scale experimental trial-and-error, domain expertise, and ingenuity. The application of rational design engineering methods promise to make this more efficient, faster, cheaper and safer. But this requires mathematical models of cellular systems. And for these models we then have to determine if they can meet our intended target behaviour. Here we develop two complementary approaches that allow us to determine whether a given molecular circuit, represented by a mathematical model, is capable of fulfilling our design objectives. We discuss algebraic methods that are capable of identifying general principles guaranteeing desired behaviour; and we provide an overview over Bayesian design approaches that allow us to choose from a set of models, that model which has the highest probability of fulfilling our design objectives. We discuss their uses in the context of biochemical adaptation, and then consider how robustness can and should affect our design approach.
Biologists need modern data infrastructure on campus
Authors: Andrey Andreev, Tom Morrell, Kristin Briney, Sandra Gesing, Uri Manor
Date: 2021-08-17 00:00:00
Tasks: Management

Modern tools for biological research, especially microscopy, have rapidly advanced in recent years, which has led to the generation of increasingly large amounts of data on a regular basis. The result is that scientists desperately need state-of-the-art technical infrastructure for raw data storage, transfer, and processing. These scientists currently rely on outdated ways to move and store data, costing valuable time and risking loss of valuable data. While the community is aware of modern approaches to data management and high-level principles (including FAIR), highly-trained and highly-paid scientists are forced to spend time dealing with technical problems, which can ultimately costs more than providing storage and a fast network on campus. Here we provide concise arguments for better infrastructure, blueprints of possible solutions, and advice in navigating the political process of solving this issue. We suggest, as a broad solution, separate NIH-managed fund for supporting universities and institutes in deployment of data storage and long-term data sharing for all funded projects. This position statement is open for more contributors from imaging, life sciences, and other disciplines. Please contact us with your experience and perspective.
Recognizing and prevention of probable regime shift in density regulated and Allee type stochastic harvesting model with application to herring conservation
Authors: Anurag Sau, Sabyasachi Bhattacharya, Bapi Saha
Date: 2021-08-17 00:00:00

An ecological system with multiple stable equilibria is prone to undergo catastrophic change or regime shift from one steady-state to another. It should be noted that, if one of the steady states is an extinction state, the catastrophic change may lead to extinction. A suitable manual measure may control the prevention of catastrophic changes of different species from one equilibrium to another. We consider two stochastic models with linear and nonlinear harvesting terms. We inspect either density regulation or Allee type density regulated models [Saha et al., Ecological Modelling, 2013], which have substantial applications in the herring fish population's viability study. Both the deterministic models we consider here contain bi-stability under certain restrictions, and in that case, one of the stable states is the extinction state. We assume that the dynamical system under consideration is closed, i.e., immigration and emigration are absent. The demographic noise is introduced in the system by substituting an ordinary differential equation with a stochastic differential equation model, where the birth and death rates of the deterministic process are used to obtain the instantaneous mean and variance in the stochastic differential equation. Our study reveals that, the catastrophic changes can be avoided manually by a suitable choice of handling time that will eventually help to prevent the sudden extinction of the harvested population. The entire study is illustrated through the herring population size data obtained from the Global Population Dynamics Database (GPDD) and simulation experiment.
Elephants or Goldfish? An Empirical Analysis of Carrier Reciprocity in Dynamic Freight Markets
Authors: Angela Acocella, Chris Caplice, Yossi Sheffi
Date: 2021-08-04 00:00:00

Dynamic macroeconomic conditions and non-binding truckload freight contracts enable both shippers and carriers to behave opportunistically. We present an empirical analysis of carrier reciprocity in the US truckload transportation sector to demonstrate whether consistent performance and fair pricing by shippers when markets are in their favor result in maintained primary carrier tender acceptance when markets turn. The results suggest carriers have short memories; they do not remember shippers' previous period pricing, tendering behavior, or performance when making freight acceptance decisions. However, carriers appear to be myopic and respond to shippers' current market period behaviors, ostensibly without regard to shippers' previous behaviors.
Dynamic Multi-scale Convolution for Dialect Identification
Authors: Tianlong Kong, Shouyi Yin, Dawei Zhang, Wang Geng, Xin Wang, Dandan song, Jinwen Huang, Huiyu Shi, Xiaorui Wang
Date: 2021-08-02 00:00:00
Tasks: Dialect Identification

Time Delay Neural Networks (TDNN)-based methods are widely used in dialect identification. However, in previous work with TDNN application, subtle variant is being neglected in different feature scales. To address this issue, we propose a new architecture, named dynamic multi-scale convolution, which consists of dynamic kernel convolution, local multi-scale learning, and global multi-scale pooling. Dynamic kernel convolution captures features between short-term and long-term context adaptively. Local multi-scale learning, which represents multi-scale features at a granular level, is able to increase the range of receptive fields for convolution operation. Besides, global multi-scale pooling is applied to aggregate features from different bottleneck layers in order to collect information from multiple aspects. The proposed architecture significantly outperforms state-of-the-art system on the AP20-OLR-dialect-task of oriental language recognition (OLR) challenge 2020, with the best average cost performance (Cavg) of 0.067 and the best equal error rate (EER) of 6.52%. Compared with the known best results, our method achieves 9% of Cavg and 45% of EER relative improvement, respectively. Furthermore, the parameters of proposed model are 91% fewer than the best known model.
Mitigating harm in language models with conditional-likelihood filtration
Authors: Helen Ngo, Cooper Raterink, João G. M. Araújo, Ivan Zhang, Carol Chen, Adrien Morisot, Nicholas Frosst
Date: 2021-08-04 00:00:00
Tasks: Language Modeling, Language Modelling

Language models trained on large-scale unfiltered datasets curated from the open web acquire systemic biases, prejudices, and harmful views from their training data. We present a methodology for programmatically identifying and removing harmful text from web-scale datasets. A pretrained language model is used to calculate the log-likelihood of researcher-written trigger phrases conditioned on a specific document, which is used to identify and filter documents from the dataset. We demonstrate that models trained on this filtered dataset exhibit lower propensity to generate harmful text, with a marginal decrease in performance on standard language modeling benchmarks compared to unfiltered baselines. We provide a partial explanation for this performance gap by surfacing examples of hate speech and other undesirable content from standard language modeling benchmarks. Finally, we discuss the generalization of this method and how trigger phrases which reflect specific values can be used by researchers to build language models which are more closely aligned with their values.