pwc-archive/papers-with-abstracts huggingface dataset:
Modeling Hybrid AC/DC Power Systems with the Complex Frequency Concept
Authors: Ignacio Ponce, Federico Milano
Date: 2023-10-28 00:00:00

The concept of complex frequency has been recently introduced on the IEEE Transactions on Power Systems to study bus voltage variations in magnitude and frequency and their link with complex power injections of a power system. In this paper, the complex frequency is applied to time-varying series connections, namely, RLC dynamic branches, regulating transformers and AC/DC converters. The proposed modeling approach allows deriving an explicit expression for the complex frequency of the voltage of a certain bus as a linear combination of three elements: net current injected by the devices connected to the bus, adjacent voltages, and time-varying series branches. The proposed formulation unifies the link between voltage and frequency dynamics in AC, DC, as well as hybrid AC/DC power systems. A variety of static and dynamic examples are presented to show the potential of the proposed formulation. Relevant applications of the proposed modeling approach are outlined.
Designing a Kinetic Façade Using BB-BC Algorithm with a Focus on Enhancing Building Energy Efficiency
Authors: Marzieh Soltani, Arash Atashi
Date: 2023-10-28 00:00:00

In order to increase energy efficiency in buildings, optimizing the parameters of the facade form can be challenging due to the dynamic nature of solar radiation. One effective solution is the use of kinetic facades as a second skin, which can control energy consumption. This study proposes a parametric kinetic facade to increase building energy efficiency, along with a framework to optimize its form using the Bang-Big Crunch (BB-BC) optimization algorithm. The study involved modeling a two-story office building in Shiraz city and calculating the energy consumption resulting from building operation over a three-day period without considering the second skin of the facade. In the second stage of the study, the second skin was optimized for the same three-day interval and calculated as a parametric, static facade. In the last step, the parameters of the second skin were optimized for three one-day intervals, assuming the possibility of kinematic changes each day. The total energy consumption of building operation for the three days was then calculated and analyzed.The Python programming language was used to develop the optimization algorithm, while Rhino software, and Grasshopper, Ladybug, and Honeybee plugins were used for building modeling and simulation of light, energy, and weather parameters.The results of the study demonstrate the effectiveness of the proposed kinetic facade and the proper performance of the proposed algorithm for solving similar problems. The study found that the use of the second skin with kinetic function reduced energy consumption by 28%. Additionally, the results from the second and third stages of the study showed that the use of the second facade shell with kinematic function, compared to its function in static mode, reduced energy consumption by 4%.Overall...
Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization
Date: 2023-10-28 00:00:00
Proceeding: NeurIPS 2023 11

The storage of continuous variables in working memory is hypothesized to be sustained in the brain by the dynamics of recurrent neural networks (RNNs) whose steady states form continuous manifolds. In some cases, it is thought that the synaptic connectivity supports multiple attractor manifolds, each mapped to a different context or task. For example, in hippocampal area CA3, positions in distinct environments are represented by distinct sets of population activity patterns, each forming a continuum. It has been argued that the embedding of multiple continuous attractors in a single RNN inevitably causes detrimental interference: quenched noise in the synaptic connectivity disrupts the continuity of each attractor, replacing it by a discrete set of steady states that can be conceptualized as lying on local minima of an abstract energy landscape. Consequently, population activity patterns exhibit systematic drifts towards one of these discrete minima, thereby degrading the stored memory over time. Here we show that it is possible to dramatically attenuate these detrimental interference effects by adjusting the synaptic weights. Synaptic weight adjustments are derived from a loss function that quantifies the roughness of the energy landscape along each of the embedded attractor manifolds. By minimizing this loss function, the stability of states can be dramatically improved, without compromising the capacity.
Transport-of-Intensity Model for Single-Mask X-ray Differential Phase Contrast Imaging
Authors: Jingcheng Yuan, Mini Das
Date: 2023-10-29 00:00:00
Tasks: Retrieval

X-ray phase contrast imaging holds great promise for improving the visibility of light-element materials such as soft tissues and tumors. Single-mask differential phase contrastnimaging method stands out as a simple and effective approach to yield differential phase contrast. In this work, we introduce a novel model for a single-mask phase imaging system based on the transport-of-intensity equation. Our model provides an accessible understanding of signal and contrast formation in single-mask X-ray phase imaging, offering a clear perspective on the image formation process, for example, the origin of alternate bright and dark fringes in phase contrast intensity images. Aided by our model, we present an efficient retrieval method that yields differential phase contrast imagery in a single acquisition step. Our model gives insight into the contrast generation and its dependence on the system geometry and imaging parameters in both the initial intensity image as well as in retrieved images. The model validity as well as the proposed retrieval method is demonstrated via both experimental results on a system developed in-house as well as with Monte Carlo simulations. In conclusion, our work not only provides a model for an intuitive visualization of image formation but also offers a method to optimize differential phase imaging setups, holding tremendous promise for advancing medical diagnostics and other applications.
A Synopsis of Stent Graft Technology Development
Authors: Umme Hafsa Momy
Date: 2023-10-30 00:00:00

Coronary artery disease (CAD) is a leading cause of death worldwide. Treatments have evolved, with stenting becoming the primary approach over bypass surgery. This article reviews the evolution of coronary stent technology, starting from the first angioplasty in 1977. Pioneers like Forssmann, Dotter, and Gruentzig established the foundation. The late 1980s saw the introduction of bare metal stents (BMS) to address angioplasty limitations. However, BMS had issues, leading to the development of first-generation drug-eluting stents (DES) in the early 2000s, which reduced restenosis but had safety concerns. Subsequent innovations introduced second-generation DES with better results and the latest bioresorbable vascular scaffolds (BVS) that dissolve over time. Clinical trials have been crucial in validating each stent's effectiveness. Despite progress, challenges remain in stent selection, approval processes, and minimizing risks. The future may see personalized stenting based on patient needs, highlighting the significant advancements in stent technology and its impact on patient care.
Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques
Authors: Xiong Xiong, Fan Yang, Li Su
Date: 2023-10-29 00:00:00
Tasks: Explainable artificial intelligence, Feature Importance

Livestreaming commerce, a hybrid of e-commerce and self-media, has expanded the broad spectrum of traditional sales performance determinants. To investigate the factors that contribute to the success of livestreaming commerce, we construct a longitudinal firm-level database with 19,175 observations, covering an entire livestreaming subsector. By comparing the forecasting accuracy of eight machine learning models, we identify a random forest model that provides the best prediction of gross merchandise volume (GMV). Furthermore, we utilize explainable artificial intelligence to open the black-box of machine learning model, discovering four new facts: 1) variables representing the popularity of livestreaming events are crucial features in predicting GMV. And voice attributes are more important than appearance; 2) popularity is a major determinant of sales for female hosts, while vocal aesthetics is more decisive for their male counterparts; 3) merits and drawbacks of the voice are not equally valued in the livestreaming market; 4) based on changes of comments, page views and likes, sales growth can be divided into three stages. Finally, we innovatively propose a 3D-SHAP diagram that demonstrates the relationship between predicting feature importance, target variable, and its predictors. This diagram identifies bottlenecks for both beginner and top livestreamers, providing insights into ways to optimize their sales performance.
Covariate Balancing and the Equivalence of Weighting and Doubly Robust Estimators of Average Treatment Effects
Authors: Tymon Słoczyński, S. Derya Uysal, Jeffrey M. Wooldridge
Date: 2023-10-28 00:00:00

We show that when the propensity score is estimated using a suitable covariate balancing procedure, the commonly used inverse probability weighting (IPW) estimator, augmented inverse probability weighting (AIPW) with linear conditional mean, and inverse probability weighted regression adjustment (IPWRA) with linear conditional mean are all numerically the same for estimating the average treatment effect (ATE) or the average treatment effect on the treated (ATT). Further, suitably chosen covariate balancing weights are automatically normalized, which means that normalized and unnormalized versions of IPW and AIPW are identical. For estimating the ATE, the weights that achieve the algebraic equivalence of IPW, AIPW, and IPWRA are based on propensity scores estimated using the inverse probability tilting (IPT) method of Graham, Pinto and Egel (2012). For the ATT, the weights are obtained using the covariate balancing propensity score (CBPS) method developed in Imai and Ratkovic (2014). These equivalences also make covariate balancing methods attractive when the treatment is confounded and one is interested in the local average treatment effect.
Doubly Robust Identification of Causal Effects of a Continuous Treatment using Discrete Instruments
Authors: Yingying Dong, Ying-Ying Lee
Date: 2023-10-27 00:00:00
Tasks: valid

Many empirical applications estimate causal effects of a continuous endogenous variable (treatment) using a binary instrument. Estimation is typically done through linear 2SLS. This approach requires a mean treatment change and causal interpretation requires the LATE-type monotonicity in the first stage. An alternative approach is to explore distributional changes in the treatment, where the first-stage restriction is treatment rank similarity. We propose causal estimands that are doubly robust in that they are valid under either of these two restrictions. We apply the doubly robust estimation to estimate the impacts of sleep on well-being. Our new estimates corroborate the usual 2SLS estimates.
The allocation of FIFA World Cup slots based on the ranking of confederations
Authors: László Csató, László Marcell Kiss, Zsombor Szádoczki
Date: 2023-10-29 00:00:00

Qualifications for several world championships in sports are organised such that distinct sets of teams play in their own tournament for a predetermined number of slots. Inspired by a recent work studying the problem with the tools from the literature on fair allocation, this paper provides an alternative approach based on historical matches between these sets of teams. We focus on the FIFA World Cup due to the existence of an official rating system and its recent expansion to 48 teams, as well as to allow for a comparison with the already suggested allocations. Our proposal extends the methodology of the FIFA World Ranking to compare the strengths of five confederations. Various allocations are presented depending on the length of the sample, the set of teams considered, as well as the frequency of rating updates. The results show that more European and South American teams should play in the FIFA World Cup. The ranking of continents by the number of deserved slots is different from the ranking implied by FIFA policy. We recommend allocating at least some slots transparently, based on historical performances, similar to the access list of the UEFA Champions League.
Mode substitution induced by electric mobility hubs: results from Amsterdam
Authors: Fanchao Liao, Jaap Vleugel, Gustav Bösehans, Dilum Dissanayake, Neil Thorpe, Margaret Bell, Bart van Arem, Gonçalo Homem de Almeida Correia
Date: 2023-10-29 00:00:00

Electric mobility hubs (eHUBS) are locations where multiple shared electric modes including electric cars and e-bikes are available. To assess their potential to reduce private car use, it is important to investigate to what extent people would switch to eHUBS modes after their introduction. Moreover, people may adapt their behaviour differently depending on their current travel mode. This study is based on stated preference data collected in Amsterdam. We analysed the data using mixed logit models. We found users of different modes not only have a varied general preference for different shared modes, but also have different sensitivity for attributes such as travel time and cost. Compared to car users, public transport users are more likely to switch towards the eHUBS modes. People who bike and walk have strong inertia, but the percentage choosing eHUBS modes doubles when the trip distance is longer (5 or 10 km).
Coupling Coordinated Development among Digital Economy, Regional Innovation and Talent Employment A case study of Hangzhou Metropolitan Circle, China
Authors: Luyi Qiu
Date: 2023-10-29 00:00:00

Coordination development across various subsystems, particularly economic, social, cultural, and human resources subsystems, is a key aspect of urban sustainability that has a direct impact on the quality of urbanization. Hangzhou Metropolitan Circle composing Hangzhou, Huzhou, Jiaxing, Shaoxing, was the first metropolitan circle approved by National Development and Reform Commission (NDRC) as a demonstration of economic transformation. To evaluate the coupling degree of the four cities and to analyze the coordinative development in the three systems (Digital Economy System, Regional Innovation System, and Talent Employment System), panel data of these four cities during the period 2015-2022 were collected. The development level of these three systems were evaluated using standard deviation and comprehensive development index evaluation. The results are as follows: (1) the coupling coordination degree of the four cities in Hangzhou Metropolitan Circle has significant regional differences, with Hangzhou being a leader while Huzhou, Jiaxing, Shaoxing have shown steady but slow progress in the coupling development of the three systems; and (2) the development of digital economy and talent employment are the breakthrough points for construction in Huzhou, Jiaxing, Shaoxing. Related suggestions are made based on the coupling coordination results of the Hangzhou Metropolitan Circle.
Employment, labor productivity and environmental sustainability: Firm-level evidence from transition
Authors: Marjan Petreski, Stefan Tanevski, Irena Stojmenovska
Date: 2023-10-29 00:00:00

This paper examines how investment in environmentally sustainable practices impacts employment and labor productivity growth of firms in transition economies. The study considers labor skill composition and geographical differences, shedding light on sustainability dynamics. The empirical analysis relies on the World Bank-s Enterprise Survey 2019 for 24 transition economies, constructing an environmental sustainability index from various indicators through a Principal Components Analysis. To address endogeneity, a battery of fixed effects and instrumental variables are employed. Results reveal the relevance of environmental sustainability for both employment and labor productivity growth. However, the significance diminishes when addressing endogeneity comprehensively, alluding that any relation between environmentally sustainable practices and jobs growth is more complex and needs time to work. The decelerating job-creation effect of sustainability investments is however confirmed for the high-skill firms, while low-skill firms benefit from labor productivity gains spurred by such investment. Geographically, Central Europe sees more pronounced labor productivity impacts, possibly due to its higher development and sustainability-awareness levels as compared to Southeast Europe and the Commonwealth of Independent States.
Experience-weighted attraction learning in network coordination games
Authors: Fulin Guo
Date: 2023-10-28 00:00:00

This paper studies the action dynamics of network coordination games with bounded-rational agents. I apply the experience-weighted attraction (EWA) model to the analysis as the EWA model has several free parameters that can capture different aspects of agents' behavioural features. I show that the set of possible long-term action patterns can be largely different when the behavioural parameters vary, ranging from a unique possibility in which all agents favour the risk-dominant option to some set of outcomes richer than the collection of Nash equilibria. Monotonicity and non-monotonicity in the relationship between the number of possible long-term action profiles and the behavioural parameters are explored. I also study the question of influential agents in terms of whose initial predispositions are important to the actions of the whole network. The importance of agents can be represented by a left eigenvector of a Jacobian matrix provided that agents' initial attractions are close to some neutral level. Numerical calculations examine the predictive power of the eigenvector for the long-run action profile and how agents' influences are impacted by their behavioural features and network positions.
Causal effects of the Fed's large-scale asset purchases on firms' capital structure
Authors: Andrea Nocera, M. Hashem Pesaran
Date: 2023-10-28 00:00:00

We investigate the short- and long-term impacts of the Federal Reserve's large-scale asset purchases (LSAPs) on non-financial firms' capital structure using a threshold panel ARDL model. To isolate the effects of LSAPs from other macroeconomic conditions, we interact firm- and industry-specific indicators of debt capacity with measures of LSAPs. We find that LSAPs facilitated firms' access to external financing, with both Treasury and MBS purchases having positive effects. Our model also allows us to estimate the time profile of the effects of LSAPs on firm leverage providing robust evidence that they are long-lasting. These effects have a half-life of 4-5 quarters and a mean lag length of about six quarters. Nevertheless, the magnitudes are small, suggesting that LSAPs have contributed only marginally to the rise in U.S. corporate debt ratios of the past decade.
MixRep: Hidden Representation Mixup for Low-Resource Speech Recognition
Authors: Jiamin Xie, John H. L. Hansen
Date: 2023-10-27 00:00:00
Tasks: Data Augmentation, speech-recognition, Speech Recognition

In this paper, we present MixRep, a simple and effective data augmentation strategy based on mixup for low-resource ASR. MixRep interpolates the feature dimensions of hidden representations in the neural network that can be applied to both the acoustic feature input and the output of each layer, which generalizes the previous MixSpeech method. Further, we propose to combine the mixup with a regularization along the time axis of the input, which is shown as complementary. We apply MixRep to a Conformer encoder of an E2E LAS architecture trained with a joint CTC loss. We experiment on the WSJ dataset and subsets of the SWB dataset, covering reading and telephony conversational speech. Experimental results show that MixRep consistently outperforms other regularization methods for low-resource ASR. Compared to a strong SpecAugment baseline, MixRep achieves a +6.5\% and a +6.7\% relative WER reduction on the eval92 set and the Callhome part of the eval'2000 set.
Subjective Quality Evaluation of Point Clouds Using a Head Mounted Display
Authors: Joao Prazeres, Rafael Rodrigues, Manuela Pereira, Antonio M. G. Pinheiro
Date: 2023-10-29 00:00:00

This paper reports on a subjective quality evaluation of static point clouds encoded with the MPEG codecs V-PCC and G-PCC, the deep learning-based codec RS-DLPCC, and the popular Draco codec. 18 subjects visualized 3D representations of distorted point clouds using a Head Mounted Display, which allowed for a direct comparison with their reference. The Mean Opinion Scores (MOS) obtained in this subjective evaluation were compared with the MOS from two previous studies, where the same content was visualized either on a 2D display or a 3D stereoscopic display, through the Pearson Correlation, Spearman Rank Order Correlation, Root Mean Square Error, and the Outlier Ratio. The results indicate that the three studies are highly correlated with one another. Moreover, a statistical analysis between all evaluations showed no significant differences between them.
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis
Authors: Ming Li, Guang Yang
Date: 2023-10-22 00:00:00
Tasks: Federated Learning, Privacy Preserving

Thorax disease analysis in large-scale, multi-centre, and multi-scanner settings is often limited by strict privacy policies. Federated learning (FL) offers a potential solution, while traditional parameter-based FL can be limited by issues such as high communication costs, data leakage, and heterogeneity. Distillation-based FL can improve efficiency, but it relies on a proxy dataset, which is often impractical in clinical practice. To address these challenges, we introduce a data-free distillation-based FL approach FedKDF. In FedKDF, the server employs a lightweight generator to aggregate knowledge from different clients without requiring access to their private data or a proxy dataset. FedKDF combines the predictors from clients into a single, unified predictor, which is further optimized using the learned knowledge in the lightweight generator. Our empirical experiments demonstrate that FedKDF offers a robust solution for efficient, privacy-preserving federated thorax disease analysis.
Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals
Authors: Mohamad H. Alizade, Aref Einizade, Jhony H. Giraldo
Date: 2023-10-29 00:00:00
Tasks: Clustering, Graph Learning

Within the context of Graph Signal Processing (GSP), Graph Learning (GL) is concerned with the inference of the graph's underlying structure from nodal observations. However, real-world data often contains diverse information, necessitating the simultaneous clustering and learning of multiple graphs. In practical applications, valuable node-specific covariates, represented as kernels, have been underutilized by existing graph signal clustering methods. In this letter, we propose a new framework, named Kernel-based joint Multiple GL and clustering of graph signals (KMGL), that leverages a multi-convex optimization approach. This allows us to integrate node-side information, construct low-pass filters, and efficiently solve the optimization problem. The experiments demonstrate that KMGL significantly enhances the robustness of GL and clustering, particularly in scenarios with high noise levels and a substantial number of clusters. These findings underscore the potential of KMGL for improving the performance of GSP methods in diverse, real-world applications.