pwc-archive/papers-with-abstracts huggingface dataset:
zonoLAB: A MATLAB toolbox for set-based control systems analysis using hybrid zonotopes
Authors: Justin Koeln, Trevor J. Bird, Jacob Siefert, Justin Ruths, Herschel Pangborn, Neera Jain
Date: 2023-10-24 00:00:00
Tasks: Model Predictive Control

This paper introduces zonoLAB, a MATLAB-based toolbox for set-based control system analysis using the hybrid zonotope set representation. Hybrid zonotopes have proven to be an expressive set representation that can exactly represent the reachable sets of mixed-logical dynamical systems and tightly approximate the reachable sets of nonlinear dynamic systems. Moreover, hybrid zonotopes can exactly represent the continuous piecewise linear control laws associated with model predictive control and the input-output mappings of neural networks with piecewise linear activation functions. The hybrid zonotope set representation is also highly exploitable, where efficient methods developed for mixed-integer linear programming can be directly used for set operation and analysis. The zonoLAB toolbox is designed to make these capabilities accessible to the dynamic systems and controls community, with functionality spanning fundamental operations with hybrid zonotope, constrained zonotope, and zonotope set representations, powerful set analysis tools, and general-purpose algorithms for reachability analysis of open- and closed-loop systems.
A Semantic-driven Approach for Maintenance Digitalization in the Pharmaceutical Industry
Authors: Ju Wu, Xiaochen Zheng, Marco Madlena, Dimitrios Kyritsis
Date: 2023-10-24 00:00:00

The digital transformation of pharmaceutical industry is a challenging task due to the high complexity of involved elements and the strict regulatory compliance. Maintenance activities in the pharmaceutical industry play an essential role in ensuring product quality and integral functioning of equipment and premises. This paper first identifies the key challenges of digitalization in pharmaceutical industry and creates the corresponding problem space for key involved elements. A literature review is conducted to investigate the mainstream maintenance strategies, digitalization models, tools and official guidance from authorities in pharmaceutical industry. Based on the review result, a semantic-driven digitalization framework is proposed aiming to improve the digital continuity and cohesion of digital resources and technologies for maintenance activities in the pharmaceutical industry. A case study is conducted to verify the feasibility of the proposed framework based on the water sampling activities in Merck Serono facility in Switzerland. A tool-chain is presented to enable the functional modules of the framework. Some of the key functional modules within the framework are implemented and have demonstrated satisfactory performance. As one of the outcomes, a digital sampling assistant with web-based services is created to support the automated workflow of water sampling activities. The implementation result proves the potential of the proposed framework to solve the identified problems of maintenance digitalization in the pharmaceutical industry.
Sensor Attacks and Resilient Defense on HVAC Systems for Energy Market Signal Tracking
Authors: Guanyu Tian, Qun Zhou Sun, Yiyuan Qiao
Date: 2023-10-24 00:00:00
Tasks: Model Predictive Control, Stochastic Optimization

The power flexibility from smart buildings makes them suitable candidates for providing grid services. The building automation system (BAS) that employs model predictive control (MPC) for grid services relies heavily on sensor data gathered from IoT-based HVAC systems through communication networks. However, cyber-attacks that tamper sensor values can compromise the accuracy and flexibility of HVAC system power adjustment. Existing studies on grid-interactive buildings mainly focus on the efficiency and flexibility of buildings' participation in grid operations, while the security aspect is lacking. In this paper, we investigate the effects of cyber-attacks on HVAC systems in grid-interactive buildings, specifically their power-tracking performance. We design a stochastic optimization-based stealthy sensor attack and a corresponding defense strategy using a resilient control framework. The attack and its defense are tested in a physical model of a test building with a single-chiller HVAC system. Simulation results demonstrate that minor falsifications caused by a stealthy sensor attack can significantly alter the power profile, leading to large power tracking errors. However, the resilient control framework can reduce the power tracking error by over 70% under such attacks without filtering out compromised data.
Finite-Time Adaptive Fuzzy Tracking Control for Nonlinear State Constrained Pure-Feedback Systems
Authors: Ju Wu, Tong Wang, Min Ma
Date: 2023-10-23 00:00:00

This paper investigates the finite-time adaptive fuzzy tracking control problem for a class of pure-feedback system with full-state constraints. With the help of Mean-Value Theorem, the pure-feedback nonlinear system is transformed into strict-feedback case. By employing finite-time-stable like function and state transformation for output tracking error, the output tracking error converges to a predefined set in a fixed finite interval. To tackle the problem of state constraints, integral Barrier Lyapunov functions are utilized to guarantee that the state variables remain within the prescribed constraints with feasibility check. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions. In addition, all the signals in the closed-loop system are guaranteed to be semi-global ultimately uniformly bounded. Finally, two simulation examples are given to show the effectiveness of the proposed control strategy.
A Comparative Study between Silicon Carbide and Silicon Nitride based Single Cell CMUT
Authors: Rakesh Kanjilal, Reshmi Maity
Date: 2023-10-23 00:00:00

This research explores the design and conducts a comparative analysis of a noninsulated Capacitive Micromachined Ultrasonic Transducer (CMUT) featuring an innovative asymmetric electrode configuration to improve the performance of the device. Specifically, this configuration involves the utilization of a top electrode with a smaller radius in comparison to the bottom electrode. The study encompasses an investigation into the effects of varying biasing voltage within the range of 40 V to 100 V. The materials employed in this study are carefully selected to optimize the CMUT's performance. The substrate material is silicon, and the bottom and top electrodes are made from aluminium. Additionally, silicon dioxide is utilized as the foundation material within the device's structure.
Spectral-based detection of chromatin loops in multiplexed super-resolution FISH data
Authors: Michaël Liefsoens, Timothy Földes, Maria Barbi
Date: 2023-10-24 00:00:00
Tasks: Super-Resolution

Involved in mitotic condensation, interaction of transcriptional regulatory elements or isolation of structural domains, understanding loop formation is becoming a paradigm in the deciphering of chromatin architecture and its functional role. Despite the emergence of increasingly powerful genome visualization techniques, the high variability in cell populations and the randomness of conformations still make loop detection a challenge. We introduce a new approach for determining the presence and frequency of loops in a collection of experimental conformations obtained by multiplexed super-resolution imaging. Based on a spectral approach, in conjunction with neural networks, this method offers a powerful tool to detect loops in large experimental data sets, both at the population and single cell level. The method's performance is confirmed by applying it to recently published experimental data, where it provides a detailed and statistically quantified description of the global architecture of the chromosomal region under study.
Elaboration and characterization of bioplastic films based on bitter cassava starch (Manihot esculenta) reinforced by chitosan extracted from crab (Shylla seratta) shells
Authors: Julie Tantely Mitantsoa, Pierre Hervé Ravelonandro, Fara Arimalala Andrianony, Rajaona Rafihavanana Andrianaivoravelona
Date: 2023-09-24 00:00:00

Bioplastics are polymer plastics which are derived from renewable biomass resources. In this study, bioplastic films based on two different polysaccharides such as bitter cassava starch and chitosan extracted from crab shells were produced by casting technique, using glycerol as plasticizer. The purposes of this research are to characterize and to figure out the effect of additional chitosan concentrations (0; 10; 20; 30; 50% by weight of starch) on the physicochemical, mechanical and water barrier properties of bioplastic films. The film's solubility in water (S), water absorption capacity (WAP), water vapor permeability (WVP), tensile strength (TS), elongation at break (E), Young's modulus (YM) and biodegradability were investigated. The possible interactions between starch and chitosan molecules were evaluated by Fourier transform infrared spectroscopy (FTIR). From the analysis, the incorporation of the chitosan shows improved results on the water barrier properties of the bioplastic films. Optimum solubility in water, water absorption capacity, and water vapor permeability are obtained on the composition of starch/chitosan was 50/50. Actually, the addition of chitosan increased tensile strength, and elongation at break. The characterization of optimum mechanical proprieties also occurred on the 50/50 composition of cassava-starch and chitosan. At this ratio, tensile strength obtained were 6,3000 MPa; and the elongation at break were 62,8571%. It was found that cassava-starch/chitosan-based films have a stable structure compared to native cassava-starch films.
Phenotype selection due to mutational robustness
Authors: Macoto Kikuchi
Date: 2023-10-24 00:00:00
Tasks: Selection bias

The mutation-selection mechanism of Darwinian evolution gives rise not only to adaptation to environmental conditions but also to the enhancement of robustness against mutations. When two or more phenotypes have the same fitness value, the robustness distribution for different phenotypes can vary. Thus, we expect that some phenotypes are favored in evolution and that some are hardly selected because of a selection bias for mutational robustness. In this study, we investigated this selection bias for phenotypes in a model of gene regulatory networks (GRNs) using numerical simulations. The model had one input gene accepting a signal from the outside and one output gene producing a target protein, and the fitness was high if the output for the full signal was much higher than that for no signal. The model exhibited three types of responses to changes in the input signal: monostable, toggle switch, and one-way switch. We regarded these three response types as three distinguishable phenotypes. We constructed a randomly generated set of GRNs using the multicanonical Monte Carlo method originally developed in statistical physics and compared it to the outcomes of evolutionary simulations. One-way switches were strongly suppressed during evolution because of their lack of mutational robustness. By examining one-way switch GRNs in detail, we found that mutationally robust GRNs obtained by evolutionary simulations and non-robust GRNs obtained by McMC have different network structures. While robust GRNs have a common core motif, non-robust GRNs lack this motif. The bistability of non-robust GRNs is considered to be realized cooperatively by many genes, and these cooperative genotypes have been suppressed by evolution.
Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach
Authors: Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
Date: 2023-10-19 00:00:00
Tasks: regression

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or deep learning to combine different climate projections. Such approaches are inadequate when different regions require different weighting schemes for accurate and reliable sea-level rise predictions. This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale.
DeepOrientation: convolutional neural network for fringe pattern orientation map estimation
Authors: Maria Cywinska, Mikolaj Rogalski, Filip Brzeski, Krzysztof Patorski, Maciej Trusiak
Date: 2023-10-23 00:00:00
Tasks: Denoising, Period Estimation

Fringe pattern based measurement techniques are the state-of-the-art in full-field optical metrology. They are crucial both in macroscale, e.g., fringe projection profilometry, and microscale, e.g., label-free quantitative phase microscopy. Accurate estimation of the local fringe orientation map can significantly facilitate the measurement process on various ways, e.g., fringe filtering (denoising), fringe pattern boundary padding, fringe skeletoning (contouring/following/tracking), local fringe spatial frequency (fringe period) estimation and fringe pattern phase demodulation. Considering all of that the accurate, robust and preferably automatic estimation of local fringe orientation map is of high importance. In this paper we propose novel numerical solution for local fringe orientation map estimation based on convolutional neural network and deep learning called DeepOrientation. Numerical simulations and experimental results corroborate the effectiveness of the proposed DeepOrientation comparing it with the representative of the classical approach to orientation estimation called combined plane fitting/gradient method. The example proving the effectiveness of DeepOrientation in fringe pattern analysis, which we present in this paper is the application of DeepOrientation for guiding the phase demodulation process in Hilbert spiral transform. In particular, living HeLa cells quantitative phase imaging outcomes verify the method as an important asset in label-free microscopy.
Testing for equivalence of pre-trends in Difference-in-Differences estimation
Authors: Holger Dette, Martin Schumann
Date: 2023-10-24 00:00:00

The plausibility of the ``parallel trends assumption'' in Difference-in-Differences estimation is usually assessed by a test of the null hypothesis that the difference between the average outcomes of both groups is constant over time before the treatment. However, failure to reject the null hypothesis does not imply the absence of differences in time trends between both groups. We provide equivalence tests that allow researchers to find evidence in favor of the parallel trends assumption and thus increase the credibility of their treatment effect estimates. While we motivate our tests in the standard two-way fixed effects model, we discuss simple extensions to settings in which treatment adoption is staggered over time.
Inference for Rank-Rank Regressions
Authors: Denis Chetverikov, Daniel Wilhelm
Date: 2023-10-24 00:00:00
Tasks: regression

The slope coefficient in a rank-rank regression is a popular measure of intergenerational mobility. In this article, we first show that commonly used inference methods for this slope parameter are invalid. Second, when the underlying distribution is not continuous, the OLS estimator and its asymptotic distribution may be highly sensitive to how ties in the ranks are handled. Motivated by these findings we develop a new asymptotic theory for the OLS estimator in a general class of rank-rank regression specifications without imposing any assumptions about the continuity of the underlying distribution. We then extend the asymptotic theory to other regressions involving ranks that have been used in empirical work. Finally, we apply our new inference methods to two empirical studies on intergenerational mobility, highlighting the practical implications of our theoretical findings.
Long-Term Employment Effects of the Minimum Wage in Germany: New Data and Estimators
Authors: Marco Caliendo, Nico Pestel, Rebecca Olthaus
Date: 2023-10-24 00:00:00

We study the long-term effects of the 2015 German minimum wage introduction and its subsequent increases on regional employment. Using data from two waves of the Structure of Earnings Survey allows us to estimate models that account for changes in the minimum wage bite over time. While the introduction mainly affected the labour market in East Germany, the raises are also increasingly affecting low-wage regions in West Germany, such that around one third of regions have changed their (binary) treatment status over time. We apply different specifications and extensions of the classic difference-in-differences approach as well as a set of new estimators that enables for unbiased effect estimation with a staggered treatment adoption and heterogeneous treatment effects. Our results indicate a small negative effect on dependent employment of 0.5 percent, no significant effect on employment subject to social security contributions, and a significant negative effect of about 2.4 percent on marginal employment until the first quarter of 2022. The extended specifications suggest additional effects of the minimum wage increases, as well as stronger negative effects on total dependent and marginal employment for those regions that were strongly affected by the minimum wage in 2015 and 2019.
A necessary and sufficient condition for the existence of chaotic dynamics in an overlapping generations model
Authors: Tomohiro Uchiyama
Date: 2023-10-24 00:00:00

In this paper, we study economic dynamics in a standard overlapping generations model without production. In particular, using numerical methods, we obtain a necessary and sufficient condition for the existence of a topological chaos. This is a new application of a recent result characterising the existence of a topological chaos for a unimodal interval map by Deng, Khan, Mitra (2022).
Reducing residential emissions: carbon pricing vs. subsidizing retrofits
Authors: Alkis Blanz, Beatriz Gaitan
Date: 2023-10-24 00:00:00

In this paper, we compare different mitigation policies when housing investments are irreversible. We use a general equilibrium model with non-homothetic preferences and an elaborate setup of the residential housing and energy production sector. In the first-best transition, the energy demand plays only a secondary role. However, this changes when optimal carbon taxes are not available. While providing subsidies for retrofits results in the lowest direct costs for households, it ultimately leads to the highest aggregate costs and proves to be an ineffective way to decarbonize the economy. In the second-best context, a phased-in carbon price outperforms the subsidy-based transition.
Social Learning of General Rules
Authors: Enrique Urbano Arellano, Xinyang Wang
Date: 2023-10-24 00:00:00

Why do agents adopt a particular general behavioral rule among a collection of possible alternatives? To address this question, we introduce a dynamic social learning framework, where agents rely on general rules of thumb and imitate the behavioral rules of successful peers. We find the social learning outcome can be characterized independent of the initial rule distribution. When one dominant general rule consistently yields superior problem-specific outcomes, social learning almost surely leads all agents to adopt this dominant rule; otherwise, provided the population is sufficiently large, the better rule for the more frequent problem becomes the consensus rule with arbitrarily high probability. As a result, the behavioral rule selected by the social learning process need not maximize social welfare. We complement our theoretical analysis with an application to the market sentiment selection in a stochastic production market.
Pre-training Music Classification Models via Music Source Separation
Authors: Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
Date: 2023-10-24 00:00:00
Tasks: Classification, Genre classification, Music Auto-Tagging, Music Classification, Music Genre Classification, Music Source Separation, Representation Learning

In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks. To this end, we first pre-train U-Net networks under various music source separation objectives, such as the isolation of vocal or instrumental sources from a musical piece; afterwards, we attach a classification network to the pre-trained U-Net and jointly finetune the whole network. The features learned by the separation network are also propagated to the tail network through a convolutional feature adaptation module. Experimental results in two widely used and publicly available datasets indicate that pre-training the U-Nets with a music source separation objective can improve performance compared to both training the whole network from scratch and using the tail network as a standalone in two music classification tasks, music auto-tagging and music genre classification. We also show that our proposed framework can be successfully integrated into both convolutional and Transformer-based backends, highlighting its modularity.
A Comparative Study of Variational Autoencoders, Normalizing Flows, and Score-based Diffusion Models for Electrical Impedance Tomography
Authors: Huihui Wang, Guixian Xu, Qingping Zhou
Date: 2023-10-24 00:00:00
Tasks: Image Reconstruction

Electrical Impedance Tomography (EIT) is a widely employed imaging technique in industrial inspection, geophysical prospecting, and medical imaging. However, the inherent nonlinearity and ill-posedness of EIT image reconstruction present challenges for classical regularization techniques, such as the critical selection of regularization terms and the lack of prior knowledge. Deep generative models (DGMs) have been shown to play a crucial role in learning implicit regularizers and prior knowledge. This study aims to investigate the potential of three DGMs-variational autoencoder networks, normalizing flow, and score-based diffusion model-to learn implicit regularizers in learning-based EIT imaging. We first introduce background information on EIT imaging and its inverse problem formulation. Next, we propose three algorithms for performing EIT inverse problems based on corresponding DGMs. Finally, we present numerical and visual experiments, which reveal that (1) no single method consistently outperforms the others across all settings, and (2) when reconstructing an object with 2 anomalies using a well-trained model based on a training dataset containing 4 anomalies, the conditional normalizing flow model (CNF) exhibits the best generalization in low-level noise, while the conditional score-based diffusion model (CSD*) demonstrates the best generalization in high-level noise settings. We hope our preliminary efforts will encourage other researchers to assess their DGMs in EIT and other nonlinear inverse problems.