pwc-archive/papers-with-abstracts huggingface dataset:
Time-Varying Multivariate Causal Processes
Authors: Jiti Gao, Bin Peng, Wei Biao Wu, Yayi Yan
Date: 2022-06-01 00:00:00

In this paper, we consider a wide class of time-varying multivariate causal processes which nests many classic and new examples as special cases. We first prove the existence of a weakly dependent stationary approximation for our model which is the foundation to initiate the theoretical development. Afterwards, we consider the QMLE estimation approach, and provide both point-wise and simultaneous inferences on the coefficient functions. In addition, we demonstrate the theoretical findings through both simulated and real data examples. In particular, we show the empirical relevance of our study using an application to evaluate the conditional correlations between the stock markets of China and U.S. We find that the interdependence between the two stock markets is increasing over time.
Religiosity and Innovation Attitudes: An Instrumental Variables Analysis
Authors: Duygu Buyukyazici, Francesco Serti
Date: 2022-06-01 00:00:00
Tasks: Diagnostic

Estimating the influence of religion on innovation is challenging because of both complexness and endogeneity. In order to untangle these issues, we use several measures of religiosity, adopt an individual-level approach to innovation and employ the instrumental variables method. We analyse the effect of religiosity on individual attitudes that are either favourable or unfavourable to innovation, presenting an individual's propensity to innovate. We instrument one's religiosity with the average religiosity of people of the same sex, age range, and religious affiliation who live in countries with the same dominant religious denomination. The results strongly suggest that each measure of religiosity has a somewhat negative effect on innovation attitudes. The diagnostic test results and sensitivity analyses support the main findings. We propose three causality channels from religion to innovation: time allocation, the fear of uncertainty, and conventional roles reinforced by religion.
A nation-wide experiment: fuel tax cuts and almost free public transport for three months in Germany -- Report 1 Study design, recruiting and participation
Authors: Allister Loder, Fabienne Cantner, Lennart Adenaw, Markus Siewert, Sebastian Goerg, Markus Lienkamp, Klaus Bogenberger
Date: 2022-06-01 00:00:00

In spring 2022, the German federal government agreed on a set of measures that aim at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These measures include among others, a nation-wide public transport ticket for 9 EUR per month and a fuel tax cut that reduces fuel prices by more than 15% . In transportation research this is an almost unprecedented behavioral experiment. It allows to study not only behavioral responses in mode choice and induced demand but also to assess the effectiveness of transport policy instruments. We observe this natural experiment with a three-wave survey and an app-based travel diary on a sample of hundreds of participants as well as an analysis of traffic counts. In this first report, we inform about the study design, recruiting and initial participation of study participants.
Measurement of carbon finance level and exploration of its influencing factors
Authors: Peng Zhang, Yuwei Zhang, Nuo Xu
Date: 2022-06-01 00:00:00

Faced with increasingly severe environmental problems, carbon trading markets and related financial activities aiming at limiting carbon dioxide emissions are booming. Considering the complexity and urgency of carbon market, it is necessary to construct an effective evaluation index system. This paper selected carbon finance index as a composite indicator. Taking Beijing, Shanghai, and Guangdong as examples, we adopted the classic method of multiple criteria decision analysis (MCDA) to analyze the composite indicator. Potential impact factors were screened extensively and calculated through normalization, weighting by coefficient of variation and different aggregation methods. Under the measurement of Shannon-Spearman Measure, the method with the least loss of information was used to obtain the carbon finance index (CFI) of the pilot areas. Through panel model analysis, we found that company size, the number of patents per 10,000 people and the proportion of new energy generation were the factors with significant influence. Based on the research, corresponding suggestions were put forward for different market entities. Hopefully, this research will contribute to the steady development of the national carbon market.
Preliminary Results on the Employment Effect of Tourism. A meta-analysis
Authors: Giotis Georgios
Date: 2022-06-01 00:00:00
Tasks: Selection bias

Unemployment is one of the most important issues in every country. Tourism industry is a dynamic sector which is labor augmented and can create jobs, increase consumption expenditures and offer employment opportunities. In the analysis of this work, the empirical literature on this issue is presented, which indicates that tourism can play a vital and beneficial role to increase employment. This paper uses meta-analysis techniques to investigate the effect of tourism on employment, and finds that the vast majority of the studies reveal a positive relationship. The mean effect of the 36 studies of the meta-sample, using Partial Correlations, is 0.129. This effect is similar to the regression results which is around 0.9, which is positive and statistically significant, clearly indicating that tourism is effective in the creation of jobs and offers employment opportunities. Moreover, evidence of selection bias in the literature in favor of studies which produce positive estimates is found, and once this bias is corrected the true effect is 0.85-0.97.
Disclosure of Investment Advisor and Broker-Dealer Relationships: Impact on Comprehension and Decision Making
Authors: Xiaoqing Wan, Nichole R. Lighthall
Date: 2022-05-31 00:00:00
Tasks: Decision Making, Form

Recently enacted regulations aimed to enhance retail investors' understanding about different types of investment accounts. Toward this goal, the Securities and Exchange Commission (SEC) mandated that SEC-registered investment advisors and broker-dealers provide a brief relationship summary (Form CRS) to retail investors. The present study examines the impact of this regulation on investors and considers its market implications. The effects of Form CRS were evaluated based on three outcome variables: perceived helpfulness, comprehension, and decision making. The study also examined whether personal characteristics, such as investment experience, influenced the disclosure's impact on decision making. Results indicated that participants perceived the disclosure as helpful and it significantly enhanced comprehension about the two types of investment accounts. Critically, participants also showed increased preference and choice for broker-dealers after the disclosure. Increased preference for broker-dealers was associated with greater investment experience, greater comprehension gains, and access to more information from a longer disclosure. These findings suggest that Form CRS may promote informed decision making among retail investors while simultaneously increasing the selection of broker-dealer accounts.
Confidence Intervals for Recursive Journal Impact Factors
Authors: Johannes Konig, David I. Stern, Richard S. J. Tol
Date: 2022-05-31 00:00:00

We compute confidence intervals for recursive impact factors, that take into account that some citations are more prestigious than others, as well as for the associated ranks of journals, applying the methods to the population of economics journals. The Quarterly Journal of Economics is clearly the journal with greatest impact, the confidence interval for its rank only includes one. Based on the simple bootstrap, the remainder of the Top5 journals are in the top 6 together with the Journal of Finance, while the Xie et al. (2009), and Mogstad et al. (2022) methods generally broaden estimated confidence intervals, particularly for mid-ranking journals. All methods agree that most apparent differences in journal quality are, in fact, mostly insignificant.
Comparative statics with adjustment costs and the Le Chatelier principle
Authors: Eddie Dekel, John K. -H. Quah, Ludvig Sinander
Date: 2022-06-01 00:00:00
Tasks: valid

We develop a theory of monotone comparative statics for models with adjustment costs. We show that comparative-statics conclusions may be drawn under the usual ordinal complementarity assumptions on the objective function, assuming very little about costs: only a mild monotonicity condition is required. We use this insight to prove a general Le Chatelier principle: under the ordinal complementarity assumptions, if short-run adjustment is subject to a monotone cost, then the long-run response to a shock is greater than the short-run response. We extend these results to a fully dynamic model of adjustment over time: the Le Chatelier principle remains valid, and under slightly stronger assumptions, optimal adjustment follows a monotone path. We apply our results to models of saving, production, pricing, labor supply and investment.
Uniqueness of Equilibria in Interactive Networks
Authors: Chien-Hsiang Yeh
Date: 2022-06-01 00:00:00
Tasks: Sensitivity

This paper extends the unified network model, proposed by Acemoglu et al. (2016b), such that interaction functions can be heterogeneous, and the sensitivity matrix has less than or equal to one spectral radius. We show the existence and (almost surely) uniqueness of equilibrium under both eventually contracting and noncontracting assumptions. Applying the equilibrium in the study of systemic risk, we provide a measure to determine the key player who causes the most significant impact if removed from the network.
Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image
Authors: Lucy Nwosu, Xiangfang Li, Lijun Qian, Seungchan Kim, Xishuang Dong
Date: 2022-05-27 00:00:00
Tasks: Computed Tomography (CT), Deep Learning, Image Segmentation, Prediction, Segmentation, Semantic Segmentation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainties.
Characterization of 3D Printers and X-Ray Computerized Tomography
Authors: Sunita Khod, Akshay Dvivedi, Mayank Goswami
Date: 2022-05-27 00:00:00

The 3D printing process flow requires several inputs for the best printing quality. These settings may vary from sample to sample, printer to printer, and depend upon users' previous experience. The involved operational parameters for 3D Printing are varied to test the optimality. Thirty-eight samples are printed using four commercially available 3D printers, namely: (a) Ultimaker 2 Extended+, (b) Delta Wasp, (c) Raise E2, and (d) ProJet MJP. The sample profiles contain uniform and non-uniform distribution of the assorted size of cubes and spheres with a known amount of porosity. These samples are scanned using X-Ray Computed Tomography system. Functional Imaging analysis is performed using AI-based segmentation codes to (a) characterize these 3D printers and (b) find Three-dimensional surface roughness of three teeth and one sandstone pebble (from riverbed) with naturally deposited layers is also compared with printed sample values. Teeth has best quality. It is found that ProJet MJP gives the best quality of printed samples with the least amount of surface roughness and almost near to the actual porosity value. As expected, 100% infill density value, best spatial resolution for printing or Layer height, and minimum nozzle speed give the best quality of 3D printing.
Spiking Neural Network Equalization on Neuromorphic Hardware for IM/DD Optical Communication
Authors: Elias Arnold, Georg Böcherer, Eric Müller, Philipp Spilger, Johannes Schemmel, Stefano Calabrò, Maxim Kuschnerov
Date: 2022-06-01 00:00:00

A spiking neural network (SNN) non-linear equalizer model is implemented on the mixed-signal neuromorphic hardware system BrainScaleS-2 and evaluated for an IM/DD link. The BER 2e-3 is achieved with a hardware penalty less than 1 dB, outperforming numeric linear equalization.
Graph Signal Sampling Under Stochastic Priors
Authors: Junya Hara, Yuichi Tanaka, Yonina C. Eldar
Date: 2022-06-01 00:00:00

We propose a generalized sampling framework for stochastic graph signals. Stochastic graph signals are characterized by graph wide sense stationarity (GWSS) which is an extension of wide sense stationarity (WSS) for standard time-domain signals. In this paper, graph signals are assumed to satisfy the GWSS conditions and we study their sampling as well as recovery procedures. In generalized sampling, a correction filter is inserted between sampling and reconstruction operators to compensate for non-ideal measurements. We propose a design method for the correction filters to reduce the mean-squared error (MSE) between original and reconstructed graph signals. We derive the correction filters for two cases: The reconstruction filter is arbitrarily chosen or predefined. The proposed framework allows for arbitrary sampling methods, i.e., sampling in the vertex or graph frequency domain. We also show that the graph spectral response of the resulting correction filter parallels that for generalized sampling for WSS signals if sampling is performed in the graph frequency domain. Furthermore, we reveal the theoretical connection between the proposed and existing correction filters. The effectiveness of our approach is validated via experiments by comparing its MSE with existing approaches.
A Unified Multi-Task Semantic Communication System with Domain Adaptation
Authors: Guangyi Zhang, Qiyu Hu, Zhijin Qin, Yunlong Cai, Guanding Yu
Date: 2022-06-01 00:00:00
Tasks: Domain Adaptation, Semantic Communication

The task-oriented semantic communication systems have achieved significant performance gain, however, the paradigm that employs a model for a specific task might be limited, since the system has to be updated once the task is changed or multiple models are stored for serving various tasks. To address this issue, we firstly propose a unified deep learning enabled semantic communication system (U-DeepSC), where a unified model is developed to serve various transmission tasks. To jointly serve these tasks in one model with fixed parameters, we employ domain adaptation in the training procedure to specify the task-specific features for each task. Thus, the system only needs to transmit the task-specific features, rather than all the features, to reduce the transmission overhead. Moreover, since each task is of different difficulty and requires different number of layers to achieve satisfactory performance, we develop the multi-exit architecture to provide early-exit results for relatively simple tasks. In the experiments, we employ a proposed U-DeepSC to serve five tasks with multi-modalities. Simulation results demonstrate that our proposed U-DeepSC achieves comparable performance to the task-oriented semantic communication system designed for a specific task with significant transmission overhead reduction and much less number of model parameters.
EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction
Authors: Md Masud Rana, Duc DUy Nguyen
Date: 2022-06-01 00:00:00

Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical and biological properties encoders. Those low-dimensional surface-based descriptors are ready to be paired with any advanced machine learning algorithms to explore the essential structure-activity relationships that give rise to the element interactive surface area-based scoring functions (EISA-score). The newly developed EISA-score has outperformed many state-of-the-art models, including various well-established surface-related representations, in standard PDBbind benchmarks.
Intra-strand symmetries and asymmetries in bacterial DNA: Evolutive features or relics of primordial genomes?
Authors: Marcelo Sobottka
Date: 2022-06-01 00:00:00

In this work we analyze some models used to explain the origins of intra-strand parity and strand compositional asymmetries in bacterial genomes. Due to the particular way that these two features emerge in bacterial DNA, we performed our analysis from the perspective that they are complementary phenomena that should be addressed together. Although most of the models for these features try to explain them as consequence of evolutionary mechanisms, recently it was proposed that they could be `relics' of some primordial genome that were conserved thorough out the genome evolution. We shall pay special attention to the S-H model, which is, up to the date, the unique model proposed as a possible explanation for intra-strand parity and strand compositional asymmetries in primordial genomes as mere consequence of randomness under chemical/physical constraints. In particular, we shall discuss possible directions to test some of the hypotheses of the S-H model, and we will present a possible formulation of the S-H model as an evolutive model too.
The Evolution of Investor Activism in Japan
Authors: Ryo Sakai
Date: 2022-06-01 00:00:00

Activist investors have gradually become a catalyst for change in Japanese companies. This study examines the impact of activist board representation on firm performance in Japan. I focus on the only two Japanese companies with activist board representation: Kawasaki Kisen Kaisha, Ltd. ("Kawasaki") and Olympus Corporation ("Olympus"). Overall, I document significant benefits from the decision to engage with activists at these companies. The target companies experience greater short- and long-term abnormal stock returns following the activist engagement. Moreover, I show operational improvements as measured by return on assets and return on equity. Activist board members also associate with important changes in payout policy that help explain the positive stock returns. My findings support the notion that Japanese companies should consider engagements with activist investors to transform and improve their businesses. Such interactions can lead to innovative and forward-thinking policies that create value for Japanese businesses and their stakeholders.
sub-region localized hashing for fine-grained image retrieval
Authors: Xinguang Xiang, YaJie Zhang, Lu Jin, Zechao Li, Jinhui Tang
Date: 2021-12-06 00:00:00
Proceeding: IEEE Transactions on Image Processing 2021 12
Tasks: Image Retrieval, Retrieval

Fine-grained image hashing is challenging due to the difficulties of capturing discriminative local information to generate hash codes. On the one hand, existing methods usually extract local features with the dense attention mechanism by focusing on dense local regions, which cannot contain diverse local information for fine-grained hashing. On the other hand, hash codes of the same class suffer from large intra-class variation of fine-grained images. To address the above problems, this work proposes a novel sub-Region Localized Hashing (sRLH) to learn intra-class compact and inter-class separable hash codes that also contain diverse subtle local information for efficient fine-grained image retrieval. Specifically, to localize diverse local regions, a sub-region localization module is developed to learn discriminative local features by locating the peaks of non-overlap sub-regions in the feature map. Different from localizing dense local regions, these peaks can guide the sub-region localization module to capture multifarious local discriminative information by paying close attention to dispersive local regions. To mitigate intra-class variations, hash codes of the same class are enforced to approach one common binary center. Meanwhile, the gram-schmidt orthogonalization is performed on the binary centers to make the hash codes inter-class separable. Extensive experimental results on four widely used fine-grained image retrieval datasets demonstrate the superiority of sRLH to several state-of-the-art methods. The source code of sRLH will be released at https://github.com/ZhangYajie-NJUST/sRLH.git .
Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring
Authors: Mohamed-Ali Tnani, Michael Feil, Klaus Diepold
Date: 2022-06-29 00:00:00
Proceeding: Procedia CIRP 2022 6
Tasks: BIG-bench Machine Learning, Philosophy, Time Series Classification, Time Series Clustering

Manufacturing processes have undergone tremendous technological progress in recent decades. To meet the agile philosophy in industry, data-driven algorithms need to handle growing complexity, particularly in Computer Numerical Control machining. To enhance the scalability of machine learning in real-world applications, this paper presents a benchmark dataset for process monitoring of brownfield milling machines based on acceleration data. The data is collected from a real-world production plant using a smart data collection system over a two-years period. In this work, the edge-to-cloud setup is presented followed by an extensive description of the different normal and abnormal processes. An analysis of the dataset highlights the challenges of machine learning in industry caused by the environmental and industrial factors. The new dataset is published with this paper and available at: https://github.com/boschresearch/CNC_Machining.