pwc-archive/papers-with-abstracts huggingface dataset:
Sequential Structure and Control Co-design of Lightweight Precision Stages with Active control of flexible modes
Authors: Jingjie Wu, Lei Zhou
Date: 2023-01-10 00:00:00

Precision motion stages are playing a prominent role in various manufacturing equipment. The drastically increasing demand for higher throughput in integrated circuit (IC) manufacturing and inspection calls for the next-generation precision stages that have light weight and high control bandwidth simultaneously. In today's design techniques, the stage's first flexible mode is limiting its achievable control bandwidth, which enforces a trade-off between the stage's acceleration and closed-loop stiffness and thus limits the system's overall performance. To overcome this challenge, this paper proposes a new hardware design and control framework for lightweight precision motion stages with the stage's low-frequency flexible modes actively controlled. Our method proposes to minimize the resonance frequency of the controlled mode to reduce the stage's weight, and to maximize that of the uncontrolled mode to enable high control bandwidth. In addition, the proposed framework determines the placement of the actuators and sensors to maximize the controllability/observability of the stage's controlled flexible mode while minimizing that of the uncontrolled mode, which effectively simplifies the controller designs. Two case studies are used to evaluate the effectiveness of the proposed framework. Simulation results show that the stage designed using the proposed method has a weight reduction of more than 55% compared to a baseline stage design. Improvement in control bandwidth was also achieved. These results demonstrate the effectiveness of the proposed method in achieving lightweight precision positioning stages with high acceleration, bandwidth, and precision.
Experimental demonstration of bandwidth enhancement in photonic time delay reservoir computing
Authors: Irene Estebanez, Apostolos Argyris, Ingo Fischer
Date: 2023-01-11 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

Time delay reservoir computing (TDRC) using semiconductor lasers (SLs) has proven to be a promising photonic analog approach for information processing. One appealing property is that SLs subject to delayed optical feedback and external optical injection, allow tuning the response bandwidth by changing the level of optical injection. Here we use strong optical injection, thereby expanding the SL's modulation response up to tens of GHz. Performing a nonlinear time series prediction task, we demonstrate experimentally that for appropriate operating conditions, our TDRC system can operate with sampling times as small as 11.72 ps, without sacrificing computational performance.
Synergistic Small Worlds that Drive Technological Sophistication
Authors: Hardik Rajpal, Omar A Guerrero
Date: 2023-01-11 00:00:00

Advanced economies exhibit a high degree of sophistication in the creation of various products. While critical to such sophistication, the nature and underlying structure of the interactions taking place inside production processes remain opaque when studying large systems such as industries or entire economies. Using partial information decomposition, we quantify the nature of these interactions, allowing us to infer how much innovation stems form specific input interactions and how they are structured. These estimates yield a novel picture of the nuanced interactions underpinning technological sophistication. By analyzing networks of synergistic interactions, we find that more sophisticated industries tend to exhibit highly modular small-world topologies; with the tertiary sector as its central connective core. Countries and industries that have a well-established connective core and specialized modules exhibit higher economic complexity and output efficiency. Similar modular networks have been found to be responsible for maintaining a balance between integration and segregation of information in the human brain, suggesting a universal principle underlying the organization of sophisticated production processes.
Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference
Authors: James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
Date: 2023-01-11 00:00:00
Tasks: regression

We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. We also propose several new variants of the wild cluster bootstrap, which involve these CRVEs, jackknife-based bootstrap data-generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially. Three empirical examples illustrate the new methods.
Testing for the appropriate level of clustering in linear regression models
Authors: James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb
Date: 2023-01-11 00:00:00
Tasks: Clustering, regression

The overwhelming majority of empirical research that uses cluster-robust inference assumes that the clustering structure is known, even though there are often several possible ways in which a dataset could be clustered. We propose two tests for the correct level of clustering in regression models. One test focuses on inference about a single coefficient, and the other on inference about two or more coefficients. We provide both asymptotic and wild bootstrap implementations. The proposed tests work for a null hypothesis of either no clustering or ``fine'' clustering against alternatives of ``coarser'' clustering. We also propose a sequential testing procedure to determine the appropriate level of clustering. Simulations suggest that the bootstrap tests perform very well under the null hypothesis and can have excellent power. An empirical example suggests that using the tests leads to sensible inferences.
Uniform Inference in Linear Error-in-Variables Models: Divide-and-Conquer
Authors: Tom Boot, Artūras Juodis
Date: 2023-01-11 00:00:00

It is customary to estimate error-in-variables models using higher-order moments of observables. This moments-based estimator is consistent only when the coefficient of the latent regressor is assumed to be non-zero. We develop a new estimator based on the divide-and-conquer principle that is consistent for any value of the coefficient of the latent regressor. In an application on the relation between investment, (mismeasured) Tobin's $q$ and cash flow, we find time periods in which the effect of Tobin's $q$ is not statistically different from zero. The implausibly large higher-order moment estimates in these periods disappear when using the proposed estimator.
The Impact of National Culture on Innovation A Comparative Analysis between Developed and Developing Nations during the Pre and Post Crisis Period 2007_2021
Authors: Han-Sol Lee, Sergey U. Chernikov, Szabolcs Nagy, Ekaterina A. Degtereva
Date: 2022-12-26 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

This empirical study investigates the impact of the Hofstede cultural dimensions (HCD) on the Global Innovation Index (GII) scores in four different years (2007, 2009, 2019 and 2021) to compare the impacts during the pre- and post-crisis (financial and COVID-19) period by employing ordinary least square (OLS) and robust least square (Robust) analyses. The purpose of this study is to identify the impact of cultural factors on the innovation development for different income groups during the pre- and post-crisis period. We found that, in general, the same cultural properties were required for countries to enhance innovation inputs and outputs regardless of pre- and post-crisis periods and time variances. The significant cultural factors (driving forces) of the innovation performance do not change over time. However, our empirical results revealed that not the crisis itself but the income group (either developed or developing) is the factor that influences the relationship between cultural properties and innovation. It is also worth noting that cultural properties have lost much of their impact on innovation, particularly in developing countries, during recent periods. It is highly likely that in terms of innovation, no cultural development or change can significantly impact the innovation output of developing countries without the construction of the appropriate systems.
The Effects of Hofstede's Cultural Dimensions on Pro-Environmental Behaviour: How Culture Influences Environmentally Conscious Behaviour
Authors: Szabolcs Nagy, Csilla Konyha Molnarne
Date: 2022-12-26 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The need for a more sustainable lifestyle is a key focus for several countries. Using a questionnaire survey conducted in Hungary, this paper examines how culture influences environmentally conscious behaviour. Having investigated the direct impact of Hofstedes cultural dimensions on pro-environmental behaviour, we found that the culture of a country hardly affects actual environmentally conscious behaviour. The findings indicate that only individualism and power distance have a significant but weak negative impact on pro-environmental behaviour. Based on the findings, we can state that a positive change in culture is a necessary but not sufficient condition for making a country greener.
Analysis of displacement compensation methods for wavelet lifting of medical 3-D thorax CT volume data
Authors: Wolfgang Schnurrer, Jürgen Seiler, Eugen Wige, André Kaup
Date: 2023-01-11 00:00:00
Tasks: Computed Tomography (CT), Video Compression

A huge advantage of the wavelet transform in image and video compression is its scalability. Wavelet-based coding of medical computed tomography (CT) data becomes more and more popular. While much effort has been spent on encoding of the wavelet coefficients, the extension of the transform by a compensation method as in video coding has not gained much attention so far. We will analyze two compensation methods for medical CT data and compare the characteristics of the displacement compensated wavelet transform with video data. We will show that for thorax CT data the transform coding gain can be improved by a factor of 2 and the quality of the lowpass band can be improved by 8 dB in terms of PSNR compared to the original transform without compensation.
Efficient lossless coding of highpass bands from block-based motion compensated wavelet lifting using JPEG 2000
Authors: Wolfgang Schnurrer, Tobias Tröger, Thomas Richter, Jürgen Seiler, André Kaup
Date: 2023-01-11 00:00:00

Lossless image coding is a crucial task especially in the medical area, e.g., for volumes from Computed Tomography or Magnetic Resonance Tomography. Besides lossless coding, compensated wavelet lifting offers a scalable representation of such huge volumes. While compensation methods increase the details in the lowpass band, they also vary the characteristics of the wavelet coefficients, so an adaption of the coefficient coder should be considered. We propose a simple invertible extension for JPEG 2000 that can reduce the filesize for lossless coding of the highpass band by 0.8% on average with peak rate saving of 1.1%.
On the influence of clipping in lossless predictive and wavelet coding of noisy images
Authors: Wolfgang Schnurrer, Jürgen Seiler, Michael Schöberl, André Kaup
Date: 2023-01-11 00:00:00

Especially in lossless image coding the obtainable compression ratio strongly depends on the amount of noise included in the data as all noise has to be coded, too. Different approaches exist for lossless image coding. We analyze the compression performance of three kinds of approaches, namely direct entropy, predictive and wavelet-based coding. The results from our theoretical model are compared to simulated results from standard algorithms that base on the three approaches. As long as no clipping occurs with increasing noise more bits are needed for lossless compression. We will show that for very noisy signals it is more advantageous to directly use an entropy coder without advanced preprocessing steps.
Learned Disentangled Latent Representations for Scalable Image Coding for Humans and Machines
Authors: Ezgi Ozyilkan, Mateen Ulhaq, Hyomin Choi, Fabien Racape
Date: 2023-01-10 00:00:00
Tasks: object-detection, Object Detection

As an increasing amount of image and video content will be analyzed by machines, there is demand for a new codec paradigm that is capable of compressing visual input primarily for the purpose of computer vision inference, while secondarily supporting input reconstruction. In this work, we propose a learned compression architecture that can be used to build such a codec. We introduce a novel variational formulation that explicitly takes feature data relevant to the desired inference task as input at the encoder side. As such, our learned scalable image codec encodes and transmits two disentangled latent representations for object detection and input reconstruction. We note that compared to relevant benchmarks, our proposed scheme yields a more compact latent representation that is specialized for the inference task. Our experiments show that our proposed system achieves a bit rate savings of 40.6% on the primary object detection task compared to the current state-of-the-art, albeit with some degradation in performance for the secondary input reconstruction task.
A Generalized Semantic Communication System: from Sources to Channels
Authors: Zhijin Qin, Feifei Gao, Bo Lin, Xiaoming Tao, Guangyi Liu, Chengkang Pan
Date: 2023-01-11 00:00:00
Tasks: Semantic Communication

Semantic communication is regarded as the breakthrough beyond the Shannon paradigm, which transmits only semantic information to significantly improve communication efficiency. This article introduces a framework for generalized semantic communication system, which exploits the semantic information in both the multimodal source and the wireless channel environment. Subsequently, the developed deep learning enabled end-to-end semantic communication and environment semantics aided wireless communication techniques are demonstrated through two examples. The article concludes with several research challenges to boost the development of such a generalized semantic communication system.
Time of Arrival Error Estimation for Positioning Using Convolutional Neural Networks
Authors: Anil Kirmaz, Taylan Şahin, Diomidis S. Michalopoulos, Muhammad Ikram Ashraf, Wolfgang Gerstacker
Date: 2023-01-11 00:00:00
Tasks: Autonomous Driving

Wireless high-accuracy positioning has recently attracted growing research interest due to diversified nature of applications such as industrial asset tracking, autonomous driving, process automation, and many more. However, obtaining a highly accurate location information is hampered by challenges due to the radio environment. A major source of error for time-based positioning methods is inaccurate time-of-arrival (ToA) or range estimation. Existing machine learning-based solutions to mitigate such errors rely on propagation environment classification hindered by a low number of classes, employ a set of features representing channel measurements only to a limited extent, or account for only device-specific proprietary methods of ToA estimation. In this paper, we propose convolutional neural networks (CNNs) to estimate and mitigate the errors of a variety of ToA estimation methods utilizing channel impulse responses (CIRs). Based on real-world measurements from two independent campaigns, the proposed method yields significant improvements in ranging accuracy (up to 37%) of the state-of-the-art ToA estimators, often eliminating the need of optimizing the underlying conventional methods.
Good continuation in 3D: the neurogeometry of stereo vision
Authors: Maria Virginia Bolelli, Giovanna Citti, Alessandro Sarti, Steven W. Zucker
Date: 2023-01-11 00:00:00
Tasks: Position

Classical good continuation for image curves is based on $2D$ position and orientation. It is supported by the columnar organization of cortex, by psychophysical experiments, and by rich models of (differential) geometry. Here we extend good continuation to stereo. We introduce a neurogeometric model, in which the parametrizations involve both spatial and orientation disparities. Our model provides insight into the neurobiology, suggesting an implicit organization for neural interactions and a well-defined $3D$ association field. Our model sheds light on the computations underlying the correspondence problem, and illustrates how good continuation in the world generalizes good continuation in the plane.
Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient
Authors: Puya Latafat, Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos
Date: 2023-01-11 00:00:00

Backtracking linesearch is the de facto approach for minimizing continuously differentiable functions with locally Lipschitz gradient. In recent years, it has been shown that in the convex setting it is possible to avoid linesearch altogether, and to allow the stepsize to adapt based on a local smoothness estimate without any backtracks or evaluations of the function value. In this work we propose an adaptive proximal gradient method, adaPG, that uses novel estimates of the local smoothness modulus which leads to less conservative stepsize updates and that can additionally cope with nonsmooth terms. This idea is extended to the primal-dual setting where an adaptive three-term primal-dual algorithm, adaPD, is proposed which can be viewed as an extension of the PDHG method. Moreover, in this setting the "essentially" fully adaptive variant adaPD$^+$ is proposed that avoids evaluating the linear operator norm by invoking a backtracking procedure, that, remarkably, does not require extra gradient evaluations. Numerical simulations demonstrate the effectiveness of the proposed algorithms compared to the state of the art.
Barriers and Solutions to the Adoption of Clinical Tools for Computational Psychiatry
Authors: David Benrimoh, Victoria Fisher, Catalina Mourgues, Andrew D. Sheldon, Ryan Smith, Albert R. Powers
Date: 2022-12-09 00:00:00
Tasks: valid

Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena. Despite significant progress in the development of tasks and how to model them, computational psychiatry methodologies have yet to be incorporated into large-scale research projects or into clinical practice. In this viewpoint, we explore some of the barriers to incorporation of computational psychiatry tasks and models into wider mainstream research directions. These barriers include the time required for participants to complete tasks, test-retest reliability, limited ecological validity, as well as practical concerns, such as lack of computational expertise and the expense and large sample sizes traditionally required to validate tasks and models. We then discuss solutions, such as the redesigning of tasks with a view toward feasibility, and the integration of tasks into more ecologically valid and standardized game platforms that can be more easily disseminated. Finally, we provide an example of how one task, the conditioned hallucinations task, might be translated into such a game. It is our hope that interest in the creation of more accessible and feasible computational tasks will help computational methods make more positive impacts on research as well as clinical practice.
SIR-Model for Households
Authors: Philipp Doenges, Thomas Götz, Tyll Krueger, Karol Niedzielewski, Viola Priesemann, Moritz Schaefer
Date: 2023-01-11 00:00:00
Tasks: model

Households play an important role in disease dynamics. Many infections happening there due to the close contact, while mitigation measures mainly target the transmission between households. Therefore, one can see households as boosting the transmission depending on household size. To study the effect of household size and size distribution, we differentiated the within and between household reproduction rate. There are basically no preventive measures, and thus the close contacts can boost the spread. We explicitly incorporated that typically only a fraction of all household members are infected. Thus, viewing the infection of a household of a given size as a splitting process generating a new, small fully infected sub-household and a remaining still susceptible sub-household we derive a compartmental ODE-model for the dynamics of the sub-households. In this setting, the basic reproduction number as well as prevalence and the peak of an infection wave in a population with given households size distribution can be computed analytically. We compare numerical simulation results of this novel household-ODE model with results from an agent--based model using data for realistic household size distributions of different countries. We find good agreement of both models showing the catalytic effect of large households on the overall disease dynamics.
Elastic Cash
Authors: Anup Rao
Date: 2023-01-10 00:00:00

Elastic Cash is a new decentralized mechanism for regulating the money supply. The mechanism operates by modifying the supply so that an interest rate determined by a public market is kept approximately fixed. It can be incorporated into the conventional monetary system to improve the elasticity of the US Dollar, and it can be used to design new elastic cryptocurrencies that remain decentralized.
Adversarial training with informed data selection
Authors: Marcele O. K. Mendonça, Javier Maroto, Pascal Frossard, Paulo S. R. Diniz
Date: 2023-01-07 00:00:00

With the increasing amount of available data and advances in computing capabilities, deep neural networks (DNNs) have been successfully employed to solve challenging tasks in various areas, including healthcare, climate, and finance. Nevertheless, state-of-the-art DNNs are susceptible to quasi-imperceptible perturbed versions of the original images -- adversarial examples. These perturbations of the network input can lead to disastrous implications in critical areas where wrong decisions can directly affect human lives. Adversarial training is the most efficient solution to defend the network against these malicious attacks. However, adversarial trained networks generally come with lower clean accuracy and higher computational complexity. This work proposes a data selection (DS) strategy to be applied in the mini-batch training. Based on the cross-entropy loss, the most relevant samples in the batch are selected to update the model parameters in the backpropagation. The simulation results show that a good compromise can be obtained regarding robustness and standard accuracy, whereas the computational complexity of the backpropagation pass is reduced.