pwc-archive/papers-with-abstracts huggingface dataset:
EV Charging Station Wholesale Market Participation: A Strategic Bidding and Pricing Approach
Authors: Mohammad Mousavi, Li "Lisa" Qi, Alexander Brissette, Meng Wu
Date: 2022-09-13 00:00:00

This paper presents a framework for simultaneous bidding and pricing strategy for wholesale market participation of electric vehicle (EV) charging stations aggregator. The proposed framework incorporates the EV charging stations' technical constraints as well as EV owners' preferences. A bi-level optimization is adopted to model the problem. In the upper level, the total profit of the EV charging station aggregator is maximized. In the lower-level problem, the EV owner's utility function is maximized. The EV owners' preferences are modeled using the quadratic utility function. The bi-level optimization problem which is non-convex and hard to solve is converted to a mixed-integer convex quadratic programming model by writing the optimal conditions of the lower-level problem that is solvable with commercial solvers. The effectiveness of the proposed framework is investigated by implementing simulation results.
A SOCP-based ACOPF for Operational Scheduling of Three-phase Unbalanced Distribution Systems and Coordination of PV Smart Inverters
Authors: Mingyue He, Zahra Soltani, Mohammad Ghaljehei, Masoud Esmaili, Shanshan Ma, Mengxi Chen, Mojdeh Khorsand, Raja Ayyanar, Vijay Vittal
Date: 2022-09-12 00:00:00
Tasks: Scheduling

The proliferation of distributed energy resources (DERs) im-poses new challenges to distribution system operation, e.g., power quality issues. To overcome these challenges and enhance system operation, it is critical to effectively utilize all available resources and accurately characterize unbalanced distribution networks in operational tools. This paper proposes a convex second-order-cone programming (SOCP)-based AC optimal power flow (ACOPF) model for three-phase unbalanced distribution net-works, including smart inverters and Volt-VAr controller (VVC) devices. Reactive power-voltage (Q-V) characteristics of smart inverters of solar photovoltaic (PV) units are also modeled. More-over, the settings of Q-V characteristics of VVC are co-optimized within the proposed ACOPF, considering the allowable range of the IEEE 1547-2018 standard. Furthermore, dynamic analyses are conducted to verify the stability of optimal settings of VVC. The proposed models are tested on an actual 1747-node primary distribution feeder in Arizona. The results illustrate the effective-ness of the proposed ACOPF for unbalanced systems in provid-ing a global optimal solution while capturing the non-linearity and non-convexity of ACOPF. By co-optimizing settings, system operation is improved due to the flexibility of adjusting reactive power output from PV units with VVC. The time-domain simu-lations show that the optimal settings result in a stable system
Improved Fuzzy $H_{\infty}$ Filter Design Method for Nonlinear Systems with Time-Varing Delay
Authors: Qianqian Ma, Li Li, Junhui Shen, Haowei Guan, Guangcheng Ma, Hongwei Xia
Date: 2022-09-12 00:00:00

This paper investigates the fuzzy $H_{\infty}$ filter design issue for nonlinear systems with time-varying delay. In order to obtain less conservative fuzzy $H_{\infty}$ filter design method, a novel integral inequality is employed to replace the conventional Lebniz-Newton formula to analyze the stability conditions of the filtering error system. Besides, the information of the membership functions is introduced in the criterion to further relax the derived results. The proposed delay dependent filter design method is presented as LMI-based conditions, and corresponding definite expressions of fuzzy $H_{\infty}$ filter are given as well. Finally, a simulation example is provided to prove the effectiveness and superiority of the designed fuzzy $H_{\infty}$ filter.
Tackling the subsampling problem to infer collective properties from limited data
Authors: Anna Levina, Viola Priesemann, Johannes Zierenberg
Date: 2022-09-12 00:00:00

Complex systems are fascinating because their rich macroscopic properties emerge from the interaction of many simple parts. Understanding the building principles of these emergent phenomena in nature requires assessing natural complex systems experimentally. However, despite the development of large-scale data-acquisition techniques, experimental observations are often limited to a tiny fraction of the system. This spatial subsampling is particularly severe in neuroscience, where only a tiny fraction of millions or even billions of neurons can be individually recorded. Spatial subsampling may lead to significant systematic biases when inferring the collective properties of the entire system naively from a subsampled part. To overcome such biases, powerful mathematical tools have been developed in the past. In this perspective, we overview some issues arising from subsampling and review recently developed approaches to tackle the subsampling problem. These approaches enable one to assess, e.g., graph structures, collective dynamics of animals, neural network activity, or the spread of disease correctly from observing only a tiny fraction of the system. However, our current approaches are still far from having solved the subsampling problem in general, and hence we conclude by outlining what we believe are the main open challenges. Solving these challenges alongside the development of large-scale recording techniques will enable further fundamental insights into the working of complex and living systems.
Statistical models of complex brain networks: a maximum entropy approach
Authors: Vito Dichio, Fabrizio De Vico Fallani
Date: 2022-09-13 00:00:00

The brain is a highly complex system. Most of such complexity stems from the intermingled connections between its parts, which give rise to rich dynamics and to the emergence of high-level cognitive functions. Disentangling the underlying network structure is crucial to understand the brain functioning under both healthy and pathological conditions. Yet, analyzing brain networks is challenging, in part because their structure represents only one possible realization of a generative stochastic process which is in general unknown. Having a formal way to cope with such intrinsic variability is therefore central for the characterization of brain network properties. Addressing this issue entails the development of appropriate tools mostly adapted from network science and statistics. Here, we focus on a particular class of maximum entropy models for networks, i.e. exponential random graph models (ERGMs), as a parsimonious approach to identify the local connection mechanisms behind observed global network structure. Efforts are reviewed on the quest for basic organizational properties of human brain networks, as well as on the identification of predictive biomarkers of neurological diseases such as stroke. We conclude with a discussion on how emerging results and tools from statistical graph modeling, associated with forthcoming improvements in experimental data acquisition, could lead to a finer probabilistic description of complex systems in network neuroscience.
Estimation of Average Derivatives of Latent Regressors: With an Application to Inference on Buffer-Stock Saving
Authors: Hao Dong, Yuya Sasaki
Date: 2022-09-13 00:00:00
Tasks: Unity

This paper proposes a density-weighted average derivative estimator based on two noisy measures of a latent regressor. Both measures have classical errors with possibly asymmetric distributions. We show that the proposed estimator achieves the root-n rate of convergence, and derive its asymptotic normal distribution for statistical inference. Simulation studies demonstrate excellent small-sample performance supporting the root-n asymptotic normality. Based on the proposed estimator, we construct a formal test on the sub-unity of the marginal propensity to consume out of permanent income (MPCP) under a nonparametric consumption model and a permanent-transitory model of income dynamics with nonparametric distribution. Applying the test to four recent waves of U.S. Panel Study of Income Dynamics (PSID), we reject the null hypothesis of the unit MPCP in favor of a sub-unit MPCP, supporting the buffer-stock model of saving.
Testing Endogeneity of Spatial Weights Matrices in Spatial Dynamic Panel Data Models
Authors: Jieun Lee
Date: 2022-09-12 00:00:00

I propose Robust Rao's Score (RS) test statistic to determine endogeneity of spatial weights matrices in a spatial dynamic panel data (SDPD) model (Qu, Lee, and Yu, 2017). I firstly introduce the bias-corrected score function since the score function is not centered around zero due to the two-way fixed effects. I further adjust score functions to rectify the over-rejection of the null hypothesis under a presence of local misspecification in contemporaneous dependence over space, dependence over time, or spatial time dependence. I then derive the explicit forms of our test statistic. A Monte Carlo simulation supports the analytics and shows nice finite sample properties. Finally, an empirical illustration is provided using data from Penn World Table version 6.1.
Evidence and Strategy on Economic Distance in Spatially Augmented Solow-Swan Growth Model
Authors: Jieun Lee
Date: 2022-09-12 00:00:00

Economists' interests in growth theory have a very long history (Harrod, 1939; Domar, 1946; Solow, 1956; Swan 1956; Mankiw, Romer, and Weil, 1992). Recently, starting from the neoclassical growth model, Ertur and Koch (2007) developed the spatially augmented Solow-Swan growth model with the exogenous spatial weights matrices ($W$). While the exogenous $W$ assumption could be true only with the geographical/physical distance, it may not be true when economic/social distances play a role. Using Penn World Table version 7.1, which covers year 1960-2010, I conducted the robust Rao's score test (Bera, Dogan, and Taspinar, 2018) to determine if $W$ is endogeonus and used the maximum likelihood estimation (Qu and Lee, 2015). The key finding is that the significance and positive effects of physical capital externalities and spatial externalities (technological interdependence) in Ertur and Koch (2007) were no longer found with the exogenous $W$, but still they were with the endogenous $W$ models. I also found an empirical strategy on which economic distance to use when the data recently has been under heavy shocks of the worldwide financial crises during year 1996-2010.
Interpreting and predicting the economy flows: A time-varying parameter global vector autoregressive integrated the machine learning model
Authors: Yukang Jiang, Xueqin Wang, Zhixi Xiong, Haisheng Yang, Ting Tian
Date: 2022-07-31 00:00:00
Tasks: Model Selection

The paper proposes a time-varying parameter global vector autoregressive (TVP-GVAR) framework for predicting and analysing developed region economic variables. We want to provide an easily accessible approach for the economy application settings, where a variety of machine learning models can be incorporated for out-of-sample prediction. The LASSO-type technique for numerically efficient model selection of mean squared errors (MSEs) is selected. We show the convincing in-sample performance of our proposed model in all economic variables and relatively high precision out-of-sample predictions with different-frequency economic inputs. Furthermore, the time-varying orthogonal impulse responses provide novel insights into the connectedness of economic variables at critical time points across developed regions. We also derive the corresponding asymptotic bands (the confidence intervals) for orthogonal impulse responses function under standard assumptions.
Workplace Breastfeeding Legislation and Female Labor Force Participation in the United States
Authors: Julia Hatamyar
Date: 2022-09-13 00:00:00
Tasks: Survey

This paper studies the effects of legislation mandating the provision of workplace breastfeeding amenities on the labor force participation of women in the United States. Using both the American Community Survey and the Panel Study of Income Dynamics, in a staggered difference-in-differences framework, I find evidence that workplace breastfeeding legislation significantly increases the likelihood of female labor force participation (FLFP) across both datasets and multiple specifications, by at least 1.5 percentage points. The timing and magnitude of the post-law increases in FLFP differ across the two datasets. I bolster the analyses using the CDC's Infant Feeding Practices Survey and the Childhood and Adoption Supplement to the PSID, which further suggest an influence of the laws on breastfeeding women. Heterogeneity analysis indicates the presence of substantial treatment effect heterogeneity across subgroups, but the findings are specific to the separate datasets. Across both datasets, the legislation appears to be more effective in states where average pre-law FLFP was comparatively low. I also find evidence of a negative spillover effect, whereby women without children and women with older children may have reduced their LFP in response to the legislation.
Moral Hazard on Productivity Among Work-From-Home Workers Amid the COVID-19 Pandemic
Authors: Jieun Lee
Date: 2022-09-13 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

After the outbreak of COVID 19, firms appear to monitor Work From Home (WFH) workers more than ever out of anxiety that workers may shirk at home or implement moral hazard at home. Using the Survey of Working Arrangements and Attitudes (SWAA, Barrero et al., 2021), the evidence of WFH workers' ex post moral hazard as well as its specific aspects are examined. The results show that the ex post moral hazard among the WFH workers is generally found. Interestingly, however, the moral hazard on specific type of productivity, efficiency, is not detected for the workers at firms with WFH friendly policy for long term. Moreover, the advantages & challenges for the WFH culture report that workers with health or disability issues improve their productivity, whereas certain conditions specific to the WFH environment must be met.
Impact of interpersonal influences on Employee engagement and Psychological contract: Effects of guanxi, wasta, jeitinho, blat and pulling strings
Authors: Elizabeth Kassab Sfeir
Date: 2022-09-12 00:00:00

This study puts forward a conceptual model linking interpersonal influences' impact on Employee Engagement, Psychological contracts, and Human Resource Practices. It builds on human and social capital, as well as the social exchange theory (SET), projecting how interpersonal influences can impact the psychological contract (PC) and employee engagement (EE) of employees. This research analyzes the interpersonal influences of Wasta in the Middle East, Guanxi in China, Jeitinho in Brazil, Blat in Russia, and Pulling Strings in England. Interpersonal influences draw upon nepotism, favoritism, and corruption in organizations in many countries. This paper draws on the qualitative methods of analyzing previous theories. It uses the Model Paper method of predicting relationships by examining the question of how do interpersonal influences impact employee engagement and psychological contract?. It is vital to track the effects of interpersonal influences on PC and EE, acknowledging that the employer can either empower or disengage our human capital.
Attention Capture
Authors: Andrew Koh, Sivakorn Sanguanmoo
Date: 2022-09-12 00:00:00

We develop a unified analysis of how information captures attention. A decision maker (DM) faces a dynamic information structure and decides when to stop paying attention. We characterize the convex$\unicode{x2013}$order frontier and extreme points of feasible stopping times, as well as dynamic information structures which implement them. This delivers the form of optimal attentional capture as a function of the designer and DM's relative time preferences. Intertemporal commitment is unnecessary: sequentially optimal information structures always exist by inducing stochastic interim beliefs. We further analyze optimal attention capture under non instrumental value of information. Our results speak directly to the attention economy.
Sparse Video Representation Using Steered Mixture-of-Experts With Global Motion Compensation
Authors: Rolf Jongebloed, Erik Bochinski, Thomas Sikora
Date: 2022-09-13 00:00:00
Tasks: Mixture-of-Experts, Motion Compensation

Steered-Mixtures-of Experts (SMoE) present a unified framework for sparse representation and compression of image data with arbitrary dimensionality. Recent work has shown great improvements in the performance of such models for image and light-field representation. However, for the case of videos the straight-forward application yields limited success as the SMoE framework leads to a piece-wise linear representation of the underlying imagery which is disrupted by nonlinear motion. We incorporate a global motion model into the SMoE framework which allows for higher temporal steering of the kernels. This drastically increases its capabilities to exploit correlations between adjacent frames by only adding 2 to 8 motion parameters per frame to the model but decreasing the required amount of kernels on average by 54.25%, respectively, while maintaining the same reconstruction quality yielding higher compression gains.
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learning
Authors: Sen yang, Tao Shen, Yuqi Fang, Xiyue Wang, Jun Zhang, Wei Yang, Junzhou Huang, Xiao Han
Date: 2022-09-13 00:00:00
Tasks: Disentanglement, Drug Discovery, image-classification, Image Classification

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a persistent issue remains unsolved during experiments: the interferential technical noise caused by systematic errors (e.g., temperature, reagent concentration, and well location) is always mixed up with the real biological signals, leading to misinterpretation of any conclusion drawn. Here, we show a mean teacher based deep learning model (DeepNoise) that can disentangle biological signals from the experimental noise. Specifically, we aim to classify the phenotypic impact of 1,108 different genetic perturbations screened from 125,510 fluorescent microscopy images, which are totally unrecognizable by human eye. We validate our model by participating in the Recursion Cellular Image Classification Challenge, and our proposed method achieves an extremely high classification score (Acc: 99.596%), ranking the 2nd place among 866 participating groups. This promising result indicates the successful separation of biological and technical factors, which might help decrease the cost of treatment development and expedite the drug discovery process.
DiffeoRaptor: Diffeomorphic Inter-modal Image Registration using RaPTOR
Authors: Nima Masoumi, Hassan Rivaz, M. Omair Ahmad, Yiming Xiao
Date: 2022-09-12 00:00:00
Tasks: Image Registration

Purpose: Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. Methods: We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio (RaPTOR) metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. Results: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image normalization (SyN). Conclusions: Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor.
Spectral decomposition of atomic structures in heterogeneous cryo-EM
Authors: Carlos Esteve-Yagüe, Willem Diepeveen, Ozan Öktem, Carola-Bibiane Schönlieb
Date: 2022-09-12 00:00:00

We consider the problem of recovering the three-dimensional atomic structure of a flexible macromolecule from a heterogeneous cryo-EM dataset. The dataset contains noisy tomographic projections of the electrostatic potential of the macromolecule, taken from different viewing directions, and in the heterogeneous case, each image corresponds to a different conformation of the macromolecule. Under the assumption that the macromolecule can be modelled as a chain, or discrete curve (as it is for instance the case for a protein backbone with a single chain of amino-acids), we introduce a method to estimate the deformation of the atomic model with respect to a given conformation, which is assumed to be known a priori. Our method consists on estimating the torsion and bond angles of the atomic model in each conformation as a linear combination of the eigenfunctions of the Laplace operator in the manifold of conformations. These eigenfunctions can be approximated by means of a well-known technique in manifold learning, based on the construction of a graph Laplacian using the cryo-EM dataset. Finally, we test our approach with synthetic datasets, for which we recover the atomic model of two-dimensional and three-dimensional flexible structures from noisy tomographic projections.
Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design
Authors: Kehan Wu, Yingce Xia, Yang Fan, Pan Deng, Haiguang Liu, Lijun Wu, Shufang Xie, Tong Wang, Tao Qin, Tie-Yan Liu
Date: 2022-08-30 00:00:00
Tasks: Decoder, Drug Design, Drug Discovery

Structure-based drug design is drawing growing attentions in computer-aided drug discovery. Compared with the virtual screening approach where a pre-defined library of compounds are computationally screened, de novo drug design based on the structure of a target protein can provide novel drug candidates. In this paper, we present a generative solution named TamGent (Target-aware molecule generator with Transformer) that can directly generate candidate drugs from scratch for a given target, overcoming the limits imposed by existing compound libraries. Following the Transformer framework (a state-of-the-art framework in deep learning), we design a variant of Transformer encoder to process 3D geometric information of targets and pre-train the Transformer decoder on 10 million compounds from PubChem for candidate drug generation. Systematical evaluation on candidate compounds generated for targets from DrugBank shows that both binding affinity and drugability are largely improved. TamGent outperforms previous baselines in terms of both effectiveness and efficiency. The method is further verified by generating candidate compounds for the SARS-CoV-2 main protease and the oncogenic mutant KRAS G12C. The results show that our method not only re-discovers previously verified drug molecules , but also generates novel molecules with better docking scores, expanding the compound pool and potentially leading to the discovery of novel drugs.