pwc-archive/papers-with-abstracts huggingface dataset:
Tunable Magnets: modeling and validation for dynamic and precision applications
Authors: Silvan G. Viëtor, Jo W. Spronck, S. Hassan HosseinNia
Date: 2022-09-30 00:00:00

Actuator self-heating limits the achievable force and can cause unwanted structural deformations. This is especially apparent in quasi-static actuation systems that require the actuator to maintain a stable position over an extended period. As a solution, we use the concept of a Tunable Magnet. Tunable magnets rely on in-situ magnetization state tuning of AlNico to create an infinitely adjustable magnetic flux. They consist of an AlNiCo low coercivity permanent magnet together with a magnetizing coil. After tuning, the AlNiCo retains its magnetic field without further energy input, which eliminates the static heat dissipation. To enable implementation in actuation systems, the AlNiCo needs to be robustly tunable in the presence of a varying system air-gap. We achieve this by implementing a magnetization state tuning method, based on a magnetic circuit model of the actuator, measured AlNiCo BH data and air-gap flux feedback control. The proposed tuning method consists of 2 main steps. The prediction step, during which the required magnet operating point is determined, and the demagnetization step, where a feedback controller drives a demagnetization current to approach this operating point. With this method implemented for an AlNiCo 5 tunable magnet in a reluctance actuator configuration, we achieve tuning with a maximum error of 15.86 "mT" and a minimum precision of 0.67 "mT" over an air-gap range of 200 "{\mu}m". With this tuning accuracy, actuator heating during static periods is almost eliminated. Only a small bias current is needed to compensate for the tuning error.
Distributed Implementation of Minimax Adaptive Controller For Finite Set of Linear Systems
Authors: Venkatraman Renganathan, Anders Rantzer, Olle Kjellqvist
Date: 2022-09-30 00:00:00

This paper deals with a distributed implementation of minimax adaptive control algorithm for networked dynamical systems modeled by a finite set of linear models. To hedge against the uncertainty arising out of finite number of possible dynamics in each node in the network, it collects only the historical data of its neighboring nodes to decide its control action along its edges. This makes our proposed distributed approach scalable. Numerical simulations demonstrate that once each node has sufficiently estimated the uncertain parameters, the distributed minimax adaptive controller behaves like the optimal distributed H-infinity controller in hindsight.
Utility of the Koopman operator in output regulation of disturbed nonlinear systems
Authors: Bart Kieboom, Maria Bartzioka, Matin Jafarian
Date: 2022-09-30 00:00:00

This paper studies the problem of output regulation for a class of nonlinear systems experiencing matched input disturbances. It is assumed that the disturbance signal is generated by an external autonomous dynamical system. First, we show that for a class of nonlinear systems admitting a finite-dimensional Koopman representation, the problem is equivalent to a bilinear output regulation. We then prove that a linear dynamic output feedback controller, inspired by the linear output regulation framework, locally solves the original nonlinear problem. Numerical results validate our analysis.
Context-aware deep model compression for edge cloud computing
Authors: Lingdong Wang, Liyao Xiang, Jiayu Xu, Jiaju Chen, Xing Zhao, Dixi Yao, Xinbing Wang, Baochun Li
Date: 2020-11-29 00:00:00
Proceeding: International Conference on Distributed Computing Systems 2020 11
Tasks: Cloud Computing, Image Classification, Model Compression

While deep neural networks (DNNs) have led to a paradigm shift, its exorbitant computational requirement has always been a roadblock in its deployment to the edge, such as wearable devices and smartphones. Hence a hybrid edge-cloud computational framework is proposed to transfer part of the computation to the cloud, by naively partitioning the DNN operations under the constant network condition assumption. However, real-world network state varies greatly depending on the context, and DNN partitioning only has limited strategy space. In this paper, we explore the structural flexibility of DNN to fit the edge model to varying network contexts and different deployment platforms. Specifically, we designed a reinforcement learning-based decision engine to search for model transformation strategies in response to a combined objective of model accuracy and computation latency. The engine generates a context-aware model tree so that the DNN can decide the model branch to switch to at runtime. By the emulation and field experimental results, our approach enjoys a 30% − 50% latency reduction while retaining the model accuracy.
quEEGNet: Quantum AI for Biosignal Processing
Authors: Toshiaki Koike-Akino, Ye Wang
Date: 2022-09-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Quantum Machine Learning

In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we propose a hybrid quantum-classical neural network model that integrates a variational quantum circuit (VQC) into a deep neural network (DNN) for electroencephalogram (EEG), electromyogram (EMG), and electrocorticogram (ECoG) analysis. We demonstrate that the proposed quantum neural network (QNN) achieves state-of-the-art performance while the number of trainable parameters is kept small for VQC.
Conditional Distribution Model Specification Testing Using Chi-Square Goodness-of-Fit Tests
Authors: Miguel A. Delgado, Julius Vainora
Date: 2022-10-02 00:00:00

This paper introduces chi-square goodness-of-fit tests to check for conditional distribution model specification. The data is cross-classified according to the Rosenblatt transform of the dependent variable and the explanatory variables, resulting in a contingency table with expected joint frequencies equal to the product of the row and column marginals, which are independent of the model parameters. The test statistics assess whether the difference between observed and expected frequencies is due to chance. We propose three types of test statistics: the classical trinity of tests based on the likelihood of grouped data, and two statistics based on the efficient raw data estimator -- namely, a Chernoff-Lehmann and a generalized Wald statistic. The asymptotic distribution of these statistics is invariant to sample-dependent partitions. Monte Carlo experiments demonstrate the good performance of the proposed tests.
Large-Scale Allocation of Personalized Incentives
Authors: Lucas Javaudin, Andrea Araldo, André de Palma
Date: 2022-10-02 00:00:00

We consider a regulator willing to drive individual choices towards increasing social welfare by providing incentives to a large population of individuals. For that purpose, we formalize and solve the problem of finding an optimal personalized-incentive policy: optimal in the sense that it maximizes social welfare under an incentive budget constraint, personalized in the sense that the incentives proposed depend on the alternatives available to each individual, as well as her preferences. We propose a polynomial time approximation algorithm that computes a policy within few seconds and we analytically prove that it is boundedly close to the optimum. We then extend the problem to efficiently calculate the Maximum Social Welfare Curve, which gives the maximum social welfare achievable for a range of incentive budgets (not just one value). This curve is a valuable practical tool for the regulator to determine the right incentive budget to invest. Finally, we simulate a large-scale application to mode choice in a French department (about 200 thousands individuals) and illustrate the effectiveness of the proposed personalized-incentive policy in reducing CO2 emissions.
Forecasting Cryptocurrencies Log-Returns: a LASSO-VAR and Sentiment Approach
Authors: Federico D'Amario, Milos Ciganovic
Date: 2022-09-22 00:00:00

Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Social Media in many fields and, more specifically, for financial markets and economics. In this paper, we leverage the predictive power of Twitter and Reddit sentiment together with Google Trends indexes and volume to forecast the log returns of ten cryptocurrencies. Specifically, we consider $Bitcoin$, $Ethereum$, $Tether$, $Binance Coin$, $Litecoin$, $Enjin Coin$, $Horizen$, $Namecoin$, $Peercoin$, and $Feathercoin$. We evaluate the performance of LASSO-VAR using daily data from January 2018 to January 2022. In a 30 days recursive forecast, we can retrieve the correct direction of the actual series more than 50% of the time. We compare this result with the main benchmarks, and we see a 10% improvement in Mean Directional Accuracy (MDA). The use of sentiment and attention variables as predictors increase significantly the forecast accuracy in terms of MDA but not in terms of Root Mean Squared Errors. We perform a Granger causality test using a post-double LASSO selection for high-dimensional VARs. Results show no "causality" from Social Media sentiment to cryptocurrencies returns
Nature-nurture interplay in educational attainment
Authors: Dilnoza Muslimova, Hans van Kippersluis, Cornelius A. Rietveld, Stephanie von Hinke, S. Fleur W. Meddens
Date: 2020-12-09 00:00:00

This paper shows how nature (i.e., one's genetic endowments) and nurture (i.e., one's environment) interact in producing educational attainment. Genetic endowments are measured using a polygenic score for educational attainment, while we use birth order as an important environmental determinant of educational attainment. Since genetic endowments are randomly assigned within-families and orthogonal to one's birth order, our family fixed effects approach exploits exogenous variation in genetic endowments as well as environments. We find that those with higher genetic endowments benefit disproportionally more from being firstborn compared to those with lower genetic endowments.
Zero-Ending Prices, Cognitive Convenience, and Price Rigidity
Authors: Avichai Snir, Haipeng, Chen, Daniel Levy
Date: 2022-10-02 00:00:00

We assess the role of cognitive convenience in the popularity and rigidity of 0 ending prices in convenience settings. Studies show that 0 ending prices are common at convenience stores because of the transaction convenience that 0 ending prices offer. Using a large store level retail CPI data, we find that 0 ending prices are popular and rigid at convenience stores even when they offer little transaction convenience. We corroborate these findings with two large retail scanner price datasets from Dominicks and Nielsen. In the Dominicks data, we find that there are more 0 endings in the prices of the items in the front end candies category than in any other category, even though these prices have no effect on the convenience of the consumers check out transaction. In addition, in both Dominicks and Nielsens datasets, we find that 0 ending prices have a positive effect on demand. Ruling out consumer antagonism and retailers use of heuristics in pricing, we conclude that 0 ending prices are popular and rigid, and that they increase demand at convenience settings, not only for their transaction convenience, but also for the cognitive convenience they offer.
The impact of SMEs on employment creation in Makurdi metropolis of Benue state
Authors: Bridget Ngodoo Mile, Victor Ushahemba Ijirshar, Mlumun Queen Ijirshar
Date: 2022-09-30 00:00:00

SMEs remain a veritable tool that generates employment opportunities. This study examined the impact of SMEs on employment creation in the Makurdi metropolis of Benue state. A sample size of 340 entrepreneurs was chosen from the population of entrepreneurs (SMEs) in the Makurdi metropolis. The study used logistic regression to analyse the impact of SME activities on employment creation or generation in the state and found that SMEs contribute significantly to employment creation in the state but are often faced with the challenges of lack of capital, absence of business planning, lack of confidence in the face of competition, unfavorable environment for the development of SMEs, high government taxes and inadequate technical knowledge. The study therefore recommended that the government should implement capital or credit enhancing programmes and an enabling environment for smooth running of the SMEs. Tax incentives should also be granted to infant enterprises, and tax administration should be monitored to avoid excessive tax rates imposed by tax collectors.
School closures and educational path: how the Covid-19 pandemic affected transitions to college
Authors: Fernanda Estevan, Lucas Finamor
Date: 2022-09-30 00:00:00
Tasks: Diversity

We investigate the impact of the Covid-19 pandemic on the transition between high school and college in Brazil. Using microdata from the universe of students that applied to a selective university, we document how the Covid-19 shock increased enrollment for students in the top 10% high-quality public and private high schools. This increase comes at the expense of graduates from relatively lower-quality schools. Furthermore, this effect is entirely driven by applicants who were at high school during the Covid pandemic. The effect is large and completely offsets the gains in student background diversity achieved by a bold quota policy implemented years before Covid. These results suggest that not only students from underprivileged backgrounds endured larger negative effects on learning during the pandemic, but they also experienced a stall in their educational paths.
Federated Model Search via Reinforcement Learning
Authors: Dixi Yao; Lingdong Wang; Jiayu Xu; Liyao Xiang; Shuo Shao; Yingqi Chen; Yanjun Tong
Date: 2021-07-07 00:00:00
Proceeding: International Conference on Distributed Computing Systems 2021 7
Tasks: Federated Learning, Image Classification, model, Model Selection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Federated Learning (FL) framework enables training over distributed datasets while keeping the data local. However, it is difficult to customize a model fitting for all unknown local data. A pre-determined model is most likely to lead to slow convergence or low accuracy, especially when the distributed data is non-i.i.d.. To resolve the issue, we propose a model searching method in the federated learning scenario, and the method automatically searches a model structure fitting for the unseen local data. We novelly design a reinforcement learning-based framework that samples and distributes sub-models to the participants and updates its model selection policy by maximizing the reward. In practice, the model search algorithm takes a long time to converge, and hence we adaptively assign sub-models to participants according to the transmission condition. We further propose delay-compensated synchronization to mitigate loss over late updates to facilitate convergence. Extensive experiments show that our federated model search algorithm produces highly accurate models efficiently, particularly on non-i.i.d. data.
Learnable Acoustic Frontends in Bird Activity Detection
Authors: Mark Anderson, Naomi Harte
Date: 2022-10-03 00:00:00
Tasks: Action Detection, Activity Detection, Bird Audio Detection

Autonomous recording units and passive acoustic monitoring present minimally intrusive methods of collecting bioacoustics data. Combining this data with species agnostic bird activity detection systems enables the monitoring of activity levels of bird populations. Unfortunately, variability in ambient noise levels and subject distance contribute to difficulties in accurately detecting bird activity in recordings. The choice of acoustic frontend directly affects the impact these issues have on system performance. In this paper, we benchmark traditional fixed-parameter acoustic frontends against the new generation of learnable frontends on a wide-ranging bird audio detection task using data from the DCASE2018 BAD Challenge. We observe that Per-Channel Energy Normalization is the best overall performer, achieving an accuracy of 89.9%, and that in general learnable frontends significantly outperform traditional methods. We also identify challenges in learning filterbanks for bird audio.
Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs
Authors: Angeliki Katsenou, Jongwewi Mao, Ioannis Mavromatis
Date: 2022-10-02 00:00:00

The adoption of video conferencing and video communication services, accelerated by COVID-19, has driven a rapid increase in video data traffic. The demand for higher resolutions and quality, the need for immersive video formats, and the newest, more complex video codecs increase the energy consumption in data centers and display devices. In this paper, we explore and compare the energy consumption across optimized state-of-the-art video codecs, SVT-AV1, VVenC/VVdeC, VP9, and x.265. Furthermore, we align the energy usage with various objective quality metrics and the compression performance for a set of video sequences across different resolutions. The results indicate that from the tested codecs and configurations, SVT-AV1 provides the best tradeoff between energy consumption and quality. The reported results aim to serve as a guide towards sustainable video streaming while not compromising the quality of experience of the end user.
Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI
Authors: Zhongsen Li, Aiqi Sun, Chuyu Liu, Haining Wei, Shuai Wang, Mingzhu Fu, Rui Li
Date: 2022-10-02 00:00:00
Tasks: Dimensionality Reduction, Image Reconstruction

Objective. Imaging dynamic object with high temporal resolution is challenging in magnetic resonance imaging (MRI). Partial separable (PS) model was proposed to improve the imaging quality by reducing the degrees of freedom of the inverse problem. However, PS model still suffers from long acquisition time and even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously. Approach. We proposed to fully exploit the dimension reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term, and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on public dataset and prospective experiments on in-vivo data. The proposed method was compared with four competing algorithms based on PS model, and two non-PS model methods. Main results. The proposed method has robust performance against shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+Sparse method, enabling image reconstruction to be finished in just a few seconds. Significance. Accelerated PS model has the potential to save much time for clinical dynamic MRI examination, and is promising for real-time MRI applications.
Robust Optimization of RIS in Terahertz under Extreme Molecular Re-radiation Manifestations
Authors: Anish Pradhan, Mohamed A. Abd-Elmagid, Harpreet S. Dhillon, Andreas F. Molisch
Date: 2022-10-02 00:00:00

Terahertz (THz) communication signals are susceptible to severe degradation because of the molecular interaction with the atmosphere in the form of subsequent absorption and re-radiation. Recently, reconfigurable intelligent surface (RIS) has emerged as a potential technology to assist in THz communications by boosting signal power or providing virtual line-of-sight paths. However, the re-radiated energy has either been modeled as a non-line-of-sight scattering component or as additive Gaussian noise in the literature. Since the precise characterization is still a work in progress, this paper presents the first comparative investigation of the performance of an RIS-aided THz system under these two extreme re-radiation models. In particular, we first develop a novel parametric channel model that encompasses both models of the re-radiation through a simple parameter change, and then utilize that to design a robust block-coordinate descent (BCD) algorithmic framework which maximizes a lower bound on channel capacity considering imperfect channel state information. In this framework, the original problem is split into two sub-problems: a) receive beamformer optimization, and b) RIS phase-shift optimization. We also analytically demonstrate the limited interference suppression capability of a passive RIS. Our numerical results also demonstrate that slightly better throughput is achieved when the re-radiation manifests as scattering.
Opportunities for Intelligent Reflecting Surfaces in 6G-Empowered V2X Communications
Authors: Wali Ullah Khan, Asad Mahmood, Arash Bozorgchenani, Muhammad Ali Jamshed, Ali Ranjha, Eva Lagunas, Haris Pervaiz, Symeon Chatzinotas, Björn Ottersten, Petar Popovski
Date: 2022-10-02 00:00:00
Tasks: Edge-computing

This paper first describes the introduction of 6G-empowered V2X communications and IRS technology. Then it discusses different use case scenarios of IRS enabled V2X communications and reports recent advances in the existing literature. Next, we focus our attention on the scenario of vehicular edge computing involving IRS enabled drone communications in order to reduce vehicle computational time via optimal computational and communication resource allocation. At the end, this paper highlights current challenges and discusses future perspectives of IRS enabled V2X communications in order to improve current work and spark new ideas.
Wireless Semantic Transmission via Revising Modules in Conventional Communications
Authors: Peiwen Jiang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li
Date: 2022-10-02 00:00:00
Tasks: Semantic Communication

Semantic communication has become a popular research area due its high spectrum efficiency and error-correction performance. Some studies use deep learning to extract semantic features, which usually form end-to-end semantic communication systems and are hard to address the varying wireless environments. Therefore, the novel semantic-based coding methods and performance metrics have been investigated and the designed semantic systems consist of various modules as in the conventional communications but with improved functions. This article discusses recent achievements in the state-of-art semantic communications exploiting the conventional modules in wireless systems. We demonstrate through two examples that the traditional hybrid automatic repeat request and modulation methods can be redesigned for novel semantic coding and metrics to further improve the performance of wireless semantic communications. At the end of this article, some open issues are identified.