pwc-archive/papers-with-abstracts huggingface dataset:
Image denoising using complex-valued deep CNN
Authors: Yuhui Quan; Yixin Chen; Yizhen Shao;  Huan Teng ; Yong Xua; Hui Ji
Date: 2021-03-01 00:00:00
Proceeding: Pattern Recongition 2021 3
Tasks: Denoising, Image Denoising

While complex-valued transforms have been widely used in image processing and have their deep connections to biological vision systems, complex-valued convolutional neural networks (CNNs) have not seen their applications in image recovery. This paper aims at investigating the potentials of complex-valued CNNs for image denoising. A CNN is developed for image denoising with its key mathematical operations defined in the complex number field to exploit the merits of complex-valued operations, including the compactness of convolution given by the tensor product of 1D complex-valued filters, the nonlinear activation on phase, and the noise robustness of residual blocks. The experimental results show that, the proposed complex-valued denoising CNN performs competitively against existing state-of-the-art real-valued denoising CNNs, with better robustness to possible inconsistencies of noise models between training samples and test images. The results also suggest that complex-valued CNNs provide another promising deep-learning-based approach to image denoising and other image recovery tasks.
Should Social Robots in Retail Manipulate Customers?
Authors: Oliver Bendel, Liliana Margarida Dos Santos Alves
Date: 2022-06-17 00:00:00

Against the backdrop of structural changes in the retail trade, social robots have found their way into retail stores and shopping malls in order to attract, welcome, and greet customers; to inform them, advise them, and persuade them to make a purchase. Salespeople often have a broad knowledge of their product and rely on offering competent and honest advice, whether it be on shoes, clothing, or kitchen appliances. However, some frequently use sales tricks to secure purchases. The question arises of how consulting and sales robots should "behave". Should they behave like human advisors and salespeople, i.e., occasionally manipulate customers? Or should they be more honest and reliable than us? This article tries to answer these questions. After explaining the basics, it evaluates a study in this context and gives recommendations for companies that want to use consulting and sales robots. Ultimately, fair, honest, and trustworthy robots in retail are a win-win situation for all concerned.
LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments
Authors: Apoorv Malik, Liang Zhang, Milan Gautam, Ning Dai, Sizhen Li, He Zhang, David H. Mathews, Liang Huang
Date: 2022-06-29 00:00:00
Tasks: Prediction

Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics. However, the most commonly used tool for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, taking over a day on 400 SARS-CoV-2 and SARS-related genomes (~30,000nt). We present LinearAlifold, a much faster alternative that scales linearly with both the sequence length and the number of sequences, based on our work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (0.7 hours on the above 400 genomes, or ~36$\times$ speedup) and achieves higher accuracies when compared to a database of known structures. More interestingly, LinearAlifold's prediction on SARS-CoV-2 correlates well with experimentally determined structures, substantially outperforming RNAalifold. Finally, LinearAlifold supports two energy models (Vienna and BL*) and four modes: minimum free energy (MFE), maximum expected accuracy (MEA), ThreshKnot, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants. Our resource is at: https://github.com/LinearFold/LinearAlifold (code) and http://linearfold.org/linear-alifold (server).
Social Media and Democracy
Authors: Ronen Gradwohl, Yuval Heller, Arye Hillman
Date: 2022-06-29 00:00:00

We study the ability of a social media platform with a political agenda to influence voting outcomes. Our benchmark is Condorcet's jury theorem, which states that the likelihood of a correct decision under majority voting increases with the number of voters. We show how information manipulation by a social media platform can overturn the jury theorem, thereby undermining democracy. We also show that sometimes the platform can do so only by providing information that is biased in the opposite direction of its preferred outcome. Finally, we compare manipulation of voting outcomes through social media to manipulation through traditional media.
Molecular information theory meets protein folding
Authors: Ignacio E. Sánchez, Ezequiel A. Galpern, Martín M. Garibaldi, Diego U. Ferreiro
Date: 2022-06-29 00:00:00
Tasks: Protein Folding

We propose an application of molecular information theory to analyze the folding of single domain proteins. We analyze results from various areas of protein science, such as sequence-based potentials, reduced amino acid alphabets, backbone configurational entropy, secondary structure content, residue burial layers, and mutational studies of protein stability changes. We found that the average information contained in the sequences of evolved proteins is very close to the average information needed to specify a fold ~2.2 $\pm$ 0.3 bits/(site operation). The effective alphabet size in evolved proteins equals the effective number of conformations of a residue in the compact unfolded state at around 5. We calculated an energy-to-information conversion efficiency upon folding of around 50%, lower than the theoretical limit of 70%, but much higher than human built macroscopic machines. We propose a simple mapping between molecular information theory and energy landscape theory and explore the connections between sequence evolution, configurational entropy and the energetics of protein folding.
A wideband generalization of the near-field region for extremely large phased-arrays
Authors: Nitish Deshpande, Miguel R. Castellanos, Saeed R. Khosravirad, Jinfeng Du, Harish Viswanathan, Robert W. Heath Jr
Date: 2022-06-28 00:00:00

The narrowband and far-field assumption in conventional wireless system design leads to a mismatch with the optimal beamforming required for wideband and near-field systems. This discrepancy is exacerbated for larger apertures and bandwidths. To characterize the behavior of near-field and wideband systems, we derive the beamforming gain expression achieved by a frequency-flat phased array designed for plane-wave propagation. To determine the far-field to near-field boundary for a wideband system, we propose a frequency-selective distance metric. The proposed far-field threshold increases for frequencies away from the center frequency. The analysis results in a fundamental upper bound on the product of the array aperture and the system bandwidth. We present numerical results to illustrate how the gain threshold affects the maximum usable bandwidth for the n260 and n261 5G NR bands.
A light-weight full-band speech enhancement model
Authors: Qinwen Hu, Zhongshu Hou, Xiaohuai Le, Jing Lu
Date: 2022-06-29 00:00:00
Tasks: Speech Enhancement

Deep neural network based full-band speech enhancement systems face challenges of high demand of computational resources and imbalanced frequency distribution. In this paper, a light-weight full-band model is proposed with two dedicated strategies, i.e., a learnable spectral compression mapping for more effective high-band spectral information compression, and the utilization of the multi-head attention mechanism for more effective modeling of the global spectral pattern. Experiments validate the efficacy of the proposed strategies and show that the proposed model achieves competitive performance with only 0.89M parameters.
Comparing Conventional Pitch Detection Algorithms with a Neural Network Approach
Authors: Anja Kroon
Date: 2022-06-29 00:00:00

Despite much research, traditional methods to pitch prediction are still not perfect. With the emergence of neural networks (NNs), researchers hope to create a NN-based pitch predictor that outperforms traditional methods. Three pitch detection algorithms (PDAs), pYIN, YAAPT, and CREPE are compared in this paper. pYIN and YAAPT are conventional approaches considering time domain and frequency domain processing. CREPE utilizes a data-trained deep convolutional neural network to estimate pitch. It involves 6 densely connected convolutional hidden layers and determines pitch probabilities for a given input signal. The performance of CREPE representing neural network pitch predictors is compared to more classical approaches represented by pYIN and YAAPT. The figure of merit (FOM) will include the amount of unvoiced-to-voiced errors, voiced-to-voiced errors, gross pitch errors, and fine pitch errors.
Decomposition of Industrial Systems for Energy Efficiency Optimization with OptTopo
Authors: Gregor Thiele, Theresa Johanni, David Sommer, Jörg Krüger
Date: 2022-06-29 00:00:00

The operation of industrial facilities is a broad field for optimization. Industrial plants are often a) composed of several components, b) linked using network technology, c) physically interconnected and d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization but also to a high complexity of the emerging optimization problems. The decomposition of complex systems allows the modeling of individual models which can be structured according to the physical topology. A method for energy performance indicators (EnPI) helps to formulate an optimization problem. The optimization algorithm OptTopo achieves efficient set-points by traversing a graph representation of the overall system.
Prespecified-time observer-based distributed control of battery energy storage systems
Authors: Wu Yang, Shu-Ming Liang, Yan-Wu Wang, Zhi-Wei Liu
Date: 2022-06-29 00:00:00

This paper studies the state-of-charge (SoC) balancing and the total charging/discharging power tracking issues for battery energy storage systems (BESSs) with multiple distributed heterogeneous battery units. Different from the traditional cooperative control strategies based on the asymptotical or finite-time distributed observers, two distributed prespecified-time observers are proposed to estimate average battery units state and average desired power, respectively, which can be determined in advance and independent of initial states or control parameters. Finally, two simulation examples are given to verify the effectiveness and superiority of the proposed control strategy.
Observer-Based Coordinated Tracking Control for Nonlinear Multi-Agent Systems with Intermittent Communication under Heterogeneous Coupling Framework
Authors: Yuhang Zhang, Yulian Jiang, Shenquan Wang
Date: 2022-06-29 00:00:00
Tasks: LEMMA

In this article, the observer-based coordinated tracking control problem for a class of nonlinear multi-agent systems(MASs) with intermittent communication and information constraints is studied under dynamic switching topology. First, a state observer is designed to estimate the unmeasurable actual state information in the system. Second, adjustable heterogeneous coupling weighting parameters are introduced in the dynamic switching topology, and the distributed coordinated tracking control protocol under heterogeneous coupling framework is proposed. Then, a new Lemma is constructed to realize the cooperative design of observer gain, state feedback gain and heterogeneous coupling gain matrices. Furthermore, the stability of the system is further proved, and the range of communication rate is obtained. On this basis, the intermittent communication mode is extended to three time interval cases, namely normal communication, leader-follower communication interruption and all agents communication interruption, and then the distributed coordinated tracking control method is improved to solve this problem. Finally, simulation experiments are conducted with nonlinear MASs to verify the correctness of methods.
Continuous Switch Model and Heuristics for Mixed-Integer Problems in Power Systems
Authors: Aayushya Agarwal, Amritanshu Pandey, Larry Pillegi
Date: 2022-06-29 00:00:00

Many power systems operation and planning computations (e.g., transmission and generation switching and placement) solve a mixed-integer nonlinear problem (MINLP) with binary variables representing the decision to connect devices to the grid. Binary variables with nonlinear AC network constraints make this problem NP-hard. For large real-world networks, obtaining an AC feasible optimum solution for these problems is computationally challenging and often unattainable with state-of-the-art tools today. In this work, we map the MINLP decision problem into a set of equivalent circuits by representing binary variables with a circuit-based continuous switch model. We characterize the continuous switch model by a controlled nonlinear impedance that more closely mimics the physical behavior of a real-world switch. This mapping effectively transforms the MINLP problem into an NLP problem. We mathematically show that this transformation is a tight relaxation of the MINLP problem. For fast and robust convergence, we develop physics-driven homotopy and Newton-Raphson damping methods. To validate this approach, we empirically show robust convergences for large, realistic systems ($>$ 70,000 buses) in a practical wall-clock time to an AC-feasible optimum. We compare our results and show improvement over industry-standard tools and other binary relaxation methods.
A contraction theory approach to observer-based controller design for glucose regulation in type 1 diabetes with intra-patient variability
Authors: Bhabani Shankar Dey, Anirudh Nath, Abhilash Patel, Indra Narayan Kar
Date: 2022-06-29 00:00:00

While the Artificial Pancreas is effective in regulating the blood glucose in the safe range of 70-180 mg/dl in type 1 diabetic patients, the high intra-patient variability, as well as exogenous meal disturbances, poses a serious challenge. The existing control algorithms thus require additional safety algorithms and feed-forward actions. Moreover, the unavailability of insulin sensors in Artificial Pancreas makes this task more difficult. In the present work, a subcutaneous model of type 1 diabetes (T1D) is considered for observer-based controller design in the framework of contraction analysis. A variety of realistic multiple-meal scenarios for three virtual T1D patients have been investigated with +30 % and -30 % of parametric variability. The average time spent by the three T1D patients is found to be 77 %, 73 % and 76 %, respectively. A significant reduction in the time spent in hyperglycemia (>180 mg/dl) is achieved without any feed-forward action for meal compensation.
Constructing MDP Abstractions Using Data with Formal Guarantees
Authors: Abolfazl Lavaei, Sadegh Soudjani, Emilio Frazzoli, Majid Zamani
Date: 2022-06-29 00:00:00

This paper is concerned with a data-driven technique for constructing finite Markov decision processes (MDPs) as finite abstractions of discrete-time stochastic control systems with unknown dynamics while providing formal closeness guarantees. The proposed scheme is based on notions of stochastic bisimulation functions (SBF) to capture the probabilistic distance between state trajectories of an unknown stochastic system and those of finite MDP. In our proposed setting, we first reformulate corresponding conditions of SBF as a robust convex program (RCP). We then propose a scenario convex program (SCP) associated to the original RCP by collecting a finite number of data from trajectories of the system. We ultimately construct an SBF between the data-driven finite MDP and the unknown stochastic system with a given confidence level by establishing a probabilistic relation between optimal values of the SCP and the RCP. We also propose two different approaches for the construction of finite MDPs from data. We illustrate the efficacy of our results over a nonlinear jet engine compressor with unknown dynamics. We construct a data-driven finite MDP as a suitable substitute of the original system to synthesize controllers maintaining the system in a safe set with some probability of satisfaction and a desirable confidence level.
Non-local Evasive Overtaking of Downstream Incidents in Distributed Behavior Planning of Connected Vehicles
Authors: Abdul Rahman Kreidieh, Yashar Farid, Kentaro Oguchi
Date: 2022-06-29 00:00:00
Tasks: Autonomous Driving

The prevalence of high-speed vehicle-to-everything (V2X) communication will likely significantly influence the future of vehicle autonomy. In several autonomous driving applications, however, the role such systems will play is seldom understood. In this paper, we explore the role of communication signals in enhancing the performance of lane change assistance systems in situations where downstream bottlenecks restrict the mobility of a few lanes. Building off of prior work on modeling lane change incentives, we design a controller that 1) encourages automated vehicles to subvert lanes in which distant downstream delays are likely to occur, while also 2) ignoring greedy local incentives when such delays are needed to maintain a specific route. Numerical results on different traffic conditions and penetration rates suggest that the model successfully subverts a significant portion of delays brought about by downstream bottlenecks, both globally and from the perspective of the controlled vehicles.
Stability Analysis for Stochastic Hybrid Inclusions
Authors: Dandan Zhang, Hongye Su
Date: 2022-06-29 00:00:00

Stochastic hybrid inclusions (SHIs) address situations with the stochastic continuous evolution in a stochastic differential inclusions and random jumps in the difference inclusions due to the forced (the state reaching a boundary in the state space) and/or spontaneous (the state vector may occur spontaneously) transitions. An obvious characteristic of SHIs is the non-uniqueness of random solutions, which can be ensured by the mild regularity conditions, as well as nominal robustness. Basic sufficient conditions for stability/recurrence in probability are usually expressed based on different types of Lyapunov functions, including Lagrange/Lyapunov/Lyapunov-Forster functions respectively for Lagrange/Lyapunov/asymptotical stability in probability and Foster/Lagrange-Forster functions for recurrence, (weaker) relaxed Lyapunov-based sufficient conditions including Matrosov-Foster functions and the stochastic invariance principle, as well as Lyapunov-based necessary and sufficient conditions for asymptotical stability in probability or recurrence (i.e.,converse theorems), etc. The converse theorems involving smooth Lyapunov functions are guaranteed by the sequential compactness and thus robustness. In addition, the uniformity property and causality are analyzed for the stabilities in probability. Hence, serving as a partial roadmap for the theoretical development of SHIs, also serving as inspiration, we anticipate that many of the open questions, including the prediction problem, the filtering problem and the control problem, will be resolved based on the techniques of SHIs.
A biased random-key genetic algorithm for the home health care problem
Authors: Alberto F. Kummer, Olinto C. B. de Araújo, Luciana S. Buriol, Mauricio G. C. Resende
Date: 2022-06-29 00:00:00
Tasks: Attribute, Scheduling

Home health care problems consist of scheduling visits to home patients by health professionals while following a series of requirements. This paper studies the Home Health Care Routing and Scheduling Problem, which comprises a multi-attribute vehicle routing problem with soft time windows. Additional route inter-dependency constraints apply for patients requesting multiple visits, either by simultaneous visits or visits with precedence. We apply a mathematical programming solver to obtain lower bounds for the problem. We also propose a biased random-key genetic algorithm, and we study the effects of additional state-of-art components recently proposed in the literature for this genetic algorithm. We perform computational experiment using a publicly available benchmark dataset. Regarding the previous local search-based methods, we find results up to 26.1% better than those of the literature. We find improvements from around 0.4% to 6.36% compared to previous results from a similar genetic algorithm.
The Benefits of Hydrogen Energy Transmission and Conversion Systems to the Renewable Power Grids: Day-ahead Unit Commitment
Authors: Jin Lu, Xingpeng Li
Date: 2022-06-28 00:00:00

The curtailment of renewable energy is more frequently observed as the renewable penetration levels are rising rapidly in modern power systems. It is a waste of free and green renewable energy and implies current power grids are unable to accommodate more renewable sources. One major reason is that higher power transmission capacity is required for higher renewable penetration level. Another major reason is the volatility of the renewable generation. The hydrogen mix or pure hydrogen pipeline can both transfer and store the energy in the form of hydrogen. However, its potential of accelerating renewable integration has not been investigated. In this paper, hydrogen pipeline networks, combined with power-to-hydrogen (P2H) and hydrogen-to-power (H2P) facilities, are organized to form a Hydrogen Energy Transmission and Conversion System (HETCS). We investigate the operation of power systems coupled with HETCS, and propose the day-ahead security-constrained unit commitment (SCUC) with HETCS. The SCUC simulation is conducted on a modified IEEE 24-bus power system with HETCS. Simulation results show HETCS can substantially reduce the renewable curtailment, CO2 emission, load payment and total operational cost. This study validates the HETCS can be a promising solution to achieve net-zero renewable grids.
On the scaling of avalanche shape and activity power spectrum in neuronal networks
Authors: Manoj K. Nandi, Alessandro Sarracino, Hans J. Herrmann, Lucilla de Arcangelis
Date: 2022-06-29 00:00:00

Many systems in Nature exhibit avalanche dynamics with scale-free features. A general scaling theory has been proposed for critical avalanche profiles in crackling noise, predicting the collapse onto a universal avalanche shape, as well as the scaling behaviour of the activity power spectrum as Brown noise. Recently, much attention has been given to the profile of neuronal avalanches, measured in neuronal systems in vitro and in vivo. Although a universal profile was evidenced, confirming the validity of the general scaling theory, the parallel study of the power spectrum scaling under the same conditions was not performed. The puzzling observation is that in the majority of healthy neuronal systems the power spectrum exhibits a behaviour close to $1/f$, rather than Brown, noise. Here we perform a numerical study of the scaling behaviour of avalanche shape and power spectrum for a model of integrate and fire neurons with a short-term plasticity parameter able to tune the system to criticality. We confirm that, at criticality, the average avalanche size and the avalanche profile fulfill the general avalanche scaling theory. However, the power spectrum consistently exhibits Brown noise behaviour, for both fully excitatory networks and systems with 30\% inhibitory networks. Conversely, a behaviour closer to $1/f$ noise is observed in systems slightly off-criticality. Results suggest that the power spectrum is a good indicator to determine how close neuronal activity is to criticality.