pwc-archive/papers-with-abstracts huggingface dataset:
Joint Chance-Constrained Economic Dispatch Involving Joint Optimization of Frequency-related Inverter Control and Regulation Reserve Allocation
Authors: Ye Tian, Zhengshuo Li, Wenchuan Wu, Miao Fan
Date: 2023-03-07 00:00:00

The issues of uncertainty and frequency security could become significantly serious in power systems with the high penetration of volatile inverter-based renewables (IBRs). These issues make it necessary to consider the uncertainty and frequency-related constraints in the economic dispatch (ED) programs. However, existing ED studies rarely proactively optimize the control parameters of inverter-based resources related to fast regulation (e.g., virtual inertia and droop coefficients) in cooperation with other dispatchable resources to improve the system frequency security and dispatch reliability. This paper first proposes a joint chance-constrained economic dispatch model that jointly optimizes the frequency-related inverter control, the system up/down reserves, and base-point power for the minimal total operational cost. In the proposed model, multiple dispatchable resources including thermal units, dispatchable IBRs and energy storage are considered, and the (virtual) inertias, the regulation reserve allocations and the base-point power are coordinated. To ensure the system reliability, the joint chance-constraint formulation is also adopted. Additionally, since the traditional sample average approximation (SAA) method cost much computational burden, a novel mix-SAA (MSAA) method is proposed to transform the original intractable model into a linear model that can be efficiently solved via commercial solvers. The case studies validated the satisfactory efficacy of the proposed ED model and the efficiency of the MSAA.
Kernel-based Regularized Iterative Learning Control of Repetitive Linear Time-varying Systems
Authors: Xian Yu, Xiaozhu Fang, Biqiang Mu, Tianshi Chen
Date: 2023-03-07 00:00:00
Tasks: parameter estimation

For data-driven iterative learning control (ILC) methods, both the model estimation and controller design problems are converted to parameter estimation problems for some chosen model structures. It is well-known that if the model order is not chosen carefully, models with either large variance or large bias would be resulted, which is one of the obstacles to further improve the modeling and tracking performances of data-driven ILC in practice. An emerging trend in the system identification community to deal with this issue is using regularization instead of the statistical tests, e.g., AIC, BIC, and one of the representatives is the so-called kernel-based regularization method (KRM). In this paper, we integrate KRM into data-driven ILC to handle a class of repetitive linear time-varying systems, and moreover, we show that the proposed method has ultimately bounded tracking error in the iteration domain. The numerical simulation results show that in contrast with the least squares method and some existing data-driven ILC methods, the proposed one can give faster convergence speed, better accuracy and robustness in terms of the tracking performance.
Exploration of For-Purpose Decentralized Algorithmic Cyber Attacks in EV Charging Control
Authors: Mahan Fakouri Fard, Xiang Hou, Mingxi Liu
Date: 2023-03-06 00:00:00

Distributed and decentralized multi-agent optimization (DMAO) algorithms enable the control of large-scale grid-edge resources, such as electric vehicles (EV), to provide power grid services. Despite its great scalability, DMAO is fundamentally prone to cyber attacks as it is highly dependent on frequent peer-to-peer communications. Existing cyber-security research in this regard mainly focuses on \emph{broad-spectrum} attacks aiming at jeopardizing the entire control system while losing the possibility of achieving specific attacking purposes. This paper, for the first time, explores novel \emph{for-purpose} algorithmic attacks that are launched by participating agents and interface with DMAO to achieve self-interest attack purposes. A decentralized EV charging control problem is formulated as an illustrative use case. Theoretical \emph{for-purpose} attack vectors with and without the stealthy feature are devised. Simulations on EV charging control show the practicability of the proposed algorithmic \emph{for-purpose} attacks and the impacts of such attacks on distribution networks.
Safe Importance Sampling in Model Predictive Path Integral Control
Authors: Manan Gandhi, Hassan Almubarak, Evangelos Theodorou
Date: 2023-03-06 00:00:00

We introduce the notion of importance sampling under embedded barrier state control, titled Safety Controlled Model Predictive Path Integral Control (SC-MPPI). For robotic systems operating in an environment with multiple constraints, hard constraints are often encoded utilizing penalty functions when performing optimization. Alternative schemes utilizing optimization-based techniques, such as Control Barrier Functions, can be used as a safety filter to ensure the system does not violate the given hard constraints. In contrast, this work leverages the principle of a safety filter but applies it during forward sampling for Model Predictive Path Integral Control. The resulting set of forward samples can remain safe within the domain of the safety controller, increasing sample efficiency and allowing for improved exploration of the state space. We derive this controller through information theoretic principles analogous to Information Theoretic MPPI. We empirically demonstrate both superior sample efficiency, exploration, and system performance of SC-MPPI when compared to Model-Predictive Path Integral Control (MPPI) and Differential Dynamic Programming (DDP) optimizing the barrier state.
Selecting Features for Markov Modeling: A Case Study on HP35
Authors: Daniel Nagel, Sofia Sartore, Gerhard Stock
Date: 2023-03-07 00:00:00

Markov state models represent a popular means to interpret molecular dynamics trajectories in terms of memoryless transitions between metastable conformational states. To provide a mechanistic understanding of the considered biomolecular process, these states should reflect structurally distinct conformations and ensure a timescale separation between fast intrastate and slow interstate dynamics. Adopting the folding of villin headpiece (HP35) as a well-established model problem, here we discuss the selection of suitable input coordinates or `features', such as backbone dihedral angles and interresidue distances. We show that dihedral angles account accurately for the structure of the native energy basin of HP35, while the unfolded region of the free energy landscape and the folding process are best described by tertiary contacts of the protein. To construct a contact-based model, we consider various ways to define and select contact distances, and introduce a low-pass filtering of the feature trajectory as well as a correlation-based characterization of states. Relying on input data that faithfully account for the mechanistic origin of the studied process, the states of the resulting Markov model are clearly discriminated by the features, describe consistently the hierarchical structure of the free energy landscape, and$\unicode{0x2014}$as a consequence$\unicode{0x2014}$correctly reproduce the slow timescales of the process.
Active Deformable Cells Undergo Cell Shape Transition Associated with Percolation of Topological Defects
Authors: Nen Saito, Shuji Ishihara
Date: 2023-03-07 00:00:00

Cell deformability is an essential determinant for tissue-scale mechanical nature, such as fluidity and rigidity, and is thus crucial for understanding tissue homeostasis and stable developmental processes. However, numerical simulations for the collective dynamics of cells with arbitral cell deformations akin to mesenchymal, ameboid, and epithelial cells in a non-confluent situation need high computational costs and are still challenging. Here we propose a new method that allows us to study significantly larger numbers of cells than existing methods. Using the method, we investigated the densely packed active cell population interacting via excluded volume interactions, and discovered the emergence of two fluid phases in deformable cell populations, a soft-fluid phase with drastically deformed cell shapes and a fluid phase with circular cell shapes. The transition between these two phases is characterized by the percolation of topological defects, which is experimentally testable.
The Critical Brain Hypothesis? Meet The Metastable Brain~Mind
Authors: J. A. Scott Kelso
Date: 2023-02-28 00:00:00

In contrast to the critical brain hypothesis in which the brain tunes itself to a critical point between $states$ of chaos and order, analysis of Coordination Dynamics suggests that a vast repertoire of $coexisting$ $tendencies$ exists for regions of the brain to integrate and segregate at the same time. Rather than teetering between order and randomness, the brain~mind lives in an immense sea of metastability where it can create functional information.
Effects of a Differentiating Therapy on Cancer-Stem-Cell-Driven Tumors
Authors: Jerónimo Fotinós, Lucas Barberis, Carlos A. Condat
Date: 2023-03-08 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The growth of many solid tumors has been found to be driven by chemo- and radiotherapy-resistant cancer stem cells (CSCs). A suitable therapeutic avenue in these cases may involve the use of a differentiating agent (DA) to force the differentiation of the CSCs and of conventional therapies to eliminate the remaining differentiated cancer cells (DCCs). To describe the effects of a DA that reprograms CSCs into DCCs, we adapt a differential equation model developed to investigate tumorspheres, which are assumed to consist of jointly evolving CSC and DCC populations. We analyze the mathematical properties of the model, finding the equilibria and their stability. We also present numerical solutions and phase diagrams to describe the system evolution and the therapy effects, denoting the DA strength by a parameter \(a_{dif}\).To obtain realistic predictions, we choose the other model parameters to be those determined previously from fits to various experimental datasets. These datasets characterize the progression of the tumor under various culture conditions. Typically, for small values of \(a_{dif}\) the tumor evolves towards a final state that contains a CSC fraction, but a strong therapy leads to the suppression of this phenotype. Nonetheless, different external conditions lead to very diverse behaviors. For some environmental conditions, the model predicts a threshold not only in the therapy strength, but also in its starting time, an early beginning being potentially crucial. In summary, our model shows how the effects of a DA depend critically not only on the dosage and timing of the drug application, but also on the tumor nature and its environment.
Effect of Adult Neurogenesis on Sparsely Synchronized Rhythms of The Granule Cells in The Hippocampal Dentate Gyrus
Authors: Sang-Yoon Kim, Woochang Lim
Date: 2023-03-08 00:00:00

We are concerned about the main encoding granule cells (GCs) in the hippocampal dentate gyrus (DG). Young immature GCs (imGCs) appear through adult neurogenesis. In comparison to the mature GCs (mGCs) (born during development), the imGCs show high activation due to lower firing threshold. On the other hand, they receive low excitatory drive from the entorhinal cortex via perforant paths and from the hilar mossy cells with lower connection probability $p_c~(=20~x~\%)$ ($x:$ synaptic connectivity fraction; $ 0 \leq x \leq 1$) than the mGCs with the connection probability $p_c~(=20~\%)$. Thus, the effect of low excitatory innervation (reducing activation degree) for the imGCs counteracts the effect of their high excitability. We consider a spiking neural network for the DG, incorporating both the mGCs and the imGCs. With decreasing $x$ from 1 to 0, we investigate the effect of young adult-born imGCs on the sparsely synchronized rhythms (SSRs) of the GCs (mGCs, imGC, and whole GCs). For each $x$, population and individual firing behaviors in the SSRs are characterized in terms of the amplitude measure ${\cal M}_a^{(X)}$ ($X=m,~im,~w$ for the mGCs, the imGCs, and the whole GCs, respectively) (representing the population synchronization degree) and the random phase-locking degree ${\cal L}_d^{(X)}$ (characterizing the regularity of individual single-cell discharges), respectively. We also note that, for $0 \leq x \leq 1,$ the mGCs and the imGCs exhibit pattern separation (i.e., a process of transforming similar input patterns into less similar output patterns) and pattern integration (making association between patterns), respectively. Quantitative relationship between SSRs and pattern separation and integration is also discussed.
From localized to well-mixed: How commuter interactions shape disease spread
Authors: Aaron Winn, Adam Konkol, Eleni Katifori
Date: 2023-03-08 00:00:00

Interactions between commuting individuals can lead to large-scale spreading of rumors, ideas, or disease, even though the commuters have no net displacement. The emergent dynamics depend crucially on the commuting distribution of a population, that is how the probability to travel to a destination decays with distance from home. Applying this idea to epidemics, we will demonstrate the qualitatively different infection dynamics emerging from populations with different commuting distributions. If the commuting distribution is exponentially localized, we recover a reaction-diffusion system and observe Fisher waves traveling at a speed proportional to the characteristic commuting distance. If the commuting distribution has a long tail, then no finite-velocity waves can form, but we show that, in some regimes, there is nontrivial spatial dependence that the well-mixed approximation neglects. We discuss how, in all cases, an initial dispersal-dominated regime can allow the disease to go undetected for a finite amount of time before exponential growth takes over. This "offset time" is a quantity of huge importance for epidemic surveillance and yet largely ignored in the literature.
Role models and revealed gender-specific costs of STEM in an extended Roy model of major choice
Authors: Marc Henry, Romuald Meango, Ismael Mourifie
Date: 2020-05-18 00:00:00

We derive sharp bounds on the non consumption utility component in an extended Roy model of sector selection. We interpret this non consumption utility component as a compensating wage differential. The bounds are derived under the assumption that potential utilities in each sector are (jointly) stochastically monotone with respect to an observed selection shifter. The research is motivated by the analysis of women's choice of university major, their under representation in mathematics intensive fields, and the impact of role models on choices and outcomes. To illustrate our methodology, we investigate the cost of STEM fields with data from a German graduate survey, and using the mother's education level and the proportion of women on the STEM faculty at the time of major choice as selection shifters.
Gender Segregation: Analysis across Sectoral-Dominance in the UK Labour Market
Authors: Riccardo Leoncini, Mariele Macaluso, Annalivia Polselli
Date: 2023-03-08 00:00:00

This paper aims to evaluate how changing patterns of sectoral gender segregation play a role in accounting for women's employment contracts and wages in the UK between 2005 and 2020. We then study wage differentials in gender-specific dominated sectors. We found that the propensity of women to be distributed differently across sectors is a major factor contributing to explaining the differences in wages and contract opportunities. Hence, the disproportion of women in female-dominated sectors implies contractual features and lower wages typical of that sector, on average, for all workers. This difference is primarily explained by "persistent discriminatory constraints", while human capital-related characteristics play a minor role. However, wage differentials would shrink if workers had the same potential and residual wages as men in male-dominated sectors. Moreover, this does not happen at the top of the wage distribution, where wage differentials among women working in female-dominated sectors are always more pronounced than those of men.
Financing Costs, Per-Shipment Costs and Shipping Frequency: Firm-Level Evidence from Bangladesh
Authors: Md Deluair Hossen
Date: 2023-03-07 00:00:00

In international trade, firms face lengthy ordering-producing-delivery times and make shipping frequency decisions based on the per-shipment costs and financing costs. In this paper, I develop a model of importer-exporter procurement where the importer procures international inputs from exporting firms in developing countries. The exporters are credit constrained for working capital, incur the per-shipment fixed costs, and get paid after goods are delivered to the importer. The model shows that the shipping frequency increases for high financing costs in origin and destination. Furthermore, longer delivery times increase shipping frequency as well as procurement costs. The model also shows that the higher per-shipment fixed costs reduce the shipping frequency, in line with previous literature. Reduced transaction costs lower the exporter's demand for financial services through shipping frequency adjustment, mitigating the financial frictions of the firm. Then, I empirically investigate whether the conclusions regarding the effect of per-shipment fixed costs on shipping frequency from the theoretical model and in the existing literature extend to developing countries. My estimation method addresses several biases. First, I deal with aggregation bias with the firm, product, and country-level analysis. Second, I consider the Poisson Pseudo Maximum Likelihood (PPML) estimation method to deal with heteroscedasticity bias from the OLS estimation of log-linear models. Third, I fix the distance non-linearity of Bangladeshi exports. Finally, I consider the effect of financing cost on shipping frequency to address omitted variable bias. Using transaction-level export data from Bangladesh, I find that 10% higher per-shipment costs reduce the shipping frequency by 3.45%. The findings are robust to different specifications and subsamples.
Exchange Rate Pass-Through and Data Frequency: Firm-Level Evidence from Bangladesh
Authors: Md Deluair Hossen
Date: 2023-03-07 00:00:00

The vast literature on exchange rate fluctuations estimates the exchange rate pass-through (ERPT). Most ERPT studies consider annually aggregated data for developed or large developing countries for estimating ERPT. These estimates vary widely depending on the type of country, data coverage, and frequency. However, the ERPT estimation using firm-level high-frequency export data of a small developing country is rare. In this paper, I estimate the pricing to market and the exchange rate pass-through at a monthly, quarterly, and annual level of data frequency to deal with aggregation bias. Furthermore, I investigate how delivery time-based factors such as frequent shipments and faster transport affect a firm`s pricing-to-market behavior. Using transaction-level export data of Bangladesh from 2005 to 2013 and the Poisson Pseudo Maximum Likelihood (PPML) estimation method, I find very small pricing to the markets to the exchange rates in the exporter's price. As pass-through shows how the exporters respond to macro shocks, for Bangladesh, this low export price response to the exchange rate changes indicates that currency devaluation might not have a significant effect on the exporter. The minimal price response and high pass-through contrast with the literature on incomplete pass-through at the annual level. By considering the characteristics of the firms, products, and destinations, I investigate the heterogeneity of the pass-through. The findings remain consistent with several robustness checks.
Government Guarantees and Banks' Income Smoothing
Authors: Manuela M. Dantas, Kenneth J. Merkley, Felipe B. G. Silva
Date: 2023-03-07 00:00:00

We propose four channels through which government guarantees affect banks' incentives to smooth income. Empirically, we exploit two complementary settings that represent plausible exogenous changes in government guarantees: the increase in implicit guarantees following the creation of the Eurozone and the removal of explicit guarantees granted to the Landesbanken. We show that increases (decreases) in government guarantees are associated with significant decreases (increases) in banks' income smoothing. Taken together, our results largely corroborate the predominance of a tail-risk channel, wherein government guarantees reduce banks' tail risk, thereby reducing managers' incentives to engage in income smoothing.
A Note on Invariant Extensions of Preorders
Authors: Peter Caradonna, Christopher P. Chambers
Date: 2023-03-08 00:00:00
Tasks: Relation

We consider the problem of extending an acyclic binary relation that is invariant under a given family of transformations into an invariant preference. We show that when a family of transformations is commutative, every acyclic invariant binary relation extends. We find that, in general, the set of extensions agree on the ranking of many pairs that (i) are unranked by the original relation, and (ii) cannot be ranked by invariance or transitivity considerations alone. We interpret these additional implications as the out-of-sample predictions generated by invariance, and study their structure.
Complementarity in Demand-side Variables and Educational Participation
Authors: Anjan Ray Chaudhury, Dipankar Das, Sreemanta Sarkar
Date: 2023-02-20 00:00:00

Decision to participate in education depends on the circumstances individual inherits and on the returns to education she expects as well. If one person from any socio-economically disadvantaged social group inherits poor circumstances measured in terms of family background, then she is having poor opportunities vis-\`a-vis her capability set becomes confined. Accordingly, her freedom to choose the best alternative from many is also less, and she fails to expect the potential returns from educational participation. Consequently, a complementary relationship between the circumstances one inherits and the returns to education she expects can be observed. This paper is an attempt to look at this complementarity on the basis of theoretical logic and empirical investigation, which enables us to unearth the origin of inter-group disparity in educational participation, as is existed across the groups defined by taking caste and gender together in Indian society. Furthermore, in the second piece of analysis, we assess the discrimination in the likelihood of educational participation by invoking the method of decomposition of disparity in the likelihood of educational participation applicable in the logistic regression models, which enables us to re-establish the earlier mentioned complementary relationship.
Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition
Authors: Saurabh Kataria, Jesús Villalba, Laureano Moro-Velázquez, Thomas Thebaud, Najim Dehak
Date: 2023-03-07 00:00:00
Tasks: Bandwidth Extension, Speaker Recognition, Speaker Verification, Super-Resolution, Zero-shot Generalization

Speech super-resolution/Bandwidth Extension (BWE) can improve downstream tasks like Automatic Speaker Verification (ASV). We introduce a simple novel technique called Self-FiLM to inject self-supervision into existing BWE models via Feature-wise Linear Modulation. We hypothesize that such information captures domain/environment information, which can give zero-shot generalization. Self-FiLM Conditional GAN (CGAN) gives 18% relative improvement in Equal Error Rate and 8.5% in minimum Decision Cost Function using state-of-the-art ASV system on SRE21 test. We further by 1) deep feature loss from time-domain models and 2) re-training of data2vec 2.0 models on naturalistic wideband (VoxCeleb) and telephone data (SRE Superset etc.). Lastly, we integrate self-supervision with CycleGAN to present a completely unsupervised solution that matches the semi-supervised performance.