pwc-archive/papers-with-abstracts huggingface dataset:
Impact of COVID-19 type events on the economy and climate under the stochastic DICE model
Authors: Pavel V. Shevchenko, Daisuke Murakami, Tomoko Matsui, Tor A. Myrvoll
Date: 2021-11-01 00:00:00

The classical DICE model is a widely accepted integrated assessment model for the joint modeling of economic and climate systems, where all model state variables evolve over time deterministically. We reformulate and solve the DICE model as an optimal control dynamic programming problem with six state variables (related to the carbon concentration, temperature, and economic capital) evolving over time deterministically and affected by two controls (carbon emission mitigation rate and consumption). We then extend the model by adding a discrete stochastic shock variable to model the economy in the stressed and normal regimes as a jump process caused by events such as the COVID-19 pandemic. These shocks reduce the world gross output leading to a reduction in both the world net output and carbon emission. The extended model is solved under several scenarios as an optimal stochastic control problem, assuming that the shock events occur randomly on average once every 100 years and last for 5 years. The results show that, if the world gross output recovers in full after each event, the impact of the COVID-19 events on the temperature and carbon concentration will be immaterial even in the case of a conservative 10\% drop in the annual gross output over a 5-year period. The impact becomes noticeable, although still extremely small (long-term temperature drops by $0.1^\circ \mathrm{C}$), in a presence of persistent shocks of a 5\% output drop propagating to the subsequent time periods through the recursively reduced productivity. If the deterministic DICE model policy is applied in a presence of stochastic shocks (i.e. when this policy is suboptimal), then the drop in temperature is larger (approximately $0.25^\circ \mathrm{C}$), that is, the lower economic activities owing to shocks imply that more ambitious mitigation targets are now feasible at lower costs.
The Role of Global Value Chains in Carbon Intensity Convergence: A Spatial Econometrics Approach
Authors: Kazem Biabany Khameneh, Reza Najarzadeh, Hassan Dargahi, Lotfali Agheli
Date: 2021-10-31 00:00:00
Tasks: Econometrics

The expansion of trade agreements has provided a potential basis for trade integration and economic convergence of different countries. Moreover, developing and expanding global value chains (GVCs) have provided more opportunities for knowledge and technology spillovers and the potential convergence of production techniques. This can result in conceivable environmental outcomes in developed and developing countries. This study investigates whether GVCs can become a basis for the carbon intensity (CI) convergence of different countries. To answer this question, data from 101 countries from 1997 to 2014 are analyzed using spatial panel data econometrics. The results indicate a spatial correlation between GVCs trade partners in terms of CI growth, and they confirm the GVCs-based conditional CI convergence of the countries. Moreover, estimates indicate that expanding GVCs even stimulates bridging the CI gap between countries, i.e., directly and indirectly through spillover effects. According to the results, GVCs have the potential capacity to improve the effectiveness of carbon efficiency policies. Therefore, different dimensions of GVCs and their benefits should be taken into account when devising environmental policies.
The Politics of (No) Compromise: Information Acquisition, Policy Discretion, and Reputation
Authors: Liqun Liu
Date: 2021-10-31 00:00:00
Tasks: Decision Making

Precise information is essential for making good policies, especially those regarding reform decisions. However, decision-makers may hesitate to gather such information if certain decisions could have negative impacts on their future careers. We model how decision-makers with career concerns may acquire policy-relevant information and carry out reform decisions when their policy discretion can be limited ex ante. Typically, decision-makers with career concerns have weaker incentives to acquire information compared to decision-makers without such concerns. In this context, we demonstrate that the public can encourage information acquisition by eliminating either the "moderate policy" or the status quo from decision-makers' discretion. We also analyze when reform decisions should be strategically delegated to decision-makers with or without career concerns.
Skill Downgrading Among Refugees and Economic Immigrants in Germany
Authors: Plamen Nikolov, Leila Salarpour, David Titus
Date: 2021-10-30 00:00:00

Upon arrival to a new country, many immigrants face job downgrading, a phenomenon describing workers being in jobs below the ones they have based on the skills they possess. Moreover, in the presence of downgrading immigrants receiving lower wage returns to the same skills compared to natives. The level of downgrading could depend on the immigrant type and numerous other factors. This study examines the determinants of skill downgrading among two types of immigrants - refugees and economic immigrants - in the German labor markets between 1984 and 2018. We find that refugees downgrade more than economic immigrants, and this discrepancy between the two groups persists over time. We show that language skill improvements exert a strong influence on subsequent labor market outcomes of both groups.
Surplus Extraction with Behavioral Types
Authors: Nicolas Pastrian
Date: 2021-10-29 00:00:00

We examine the surplus extraction problem in a mechanism design setting with behavioral types. In our model behavioral types always perfectly reveal their private information. We characterize the sufficient conditions that guarantee full extraction in a finite version of the reduced form environment of McAfee and Reny (1992). We found that the standard convex independence condition identified in Cremer and McLean (1988) is required only among the beliefs of strategic types, while a weaker condition is required for the beliefs of behavioral types.
Surreal Decisions
Authors: Eddy Keming Chen, Daniel Rubio
Date: 2021-10-23 00:00:00

Although expected utility theory has proven a fruitful and elegant theory in the finite realm, attempts to generalize it to infinite values have resulted in many paradoxes. In this paper, we argue that the use of John Conway's surreal numbers shall provide a firm mathematical foundation for transfinite decision theory. To that end, we prove a surreal representation theorem and show that our surreal decision theory respects dominance reasoning even in the case of infinite values. We then bring our theory to bear on one of the more venerable decision problems in the literature: Pascal's Wager. Analyzing the wager showcases our theory's virtues and advantages. To that end, we analyze two objections against the wager: Mixed Strategies and Many Gods. After formulating the two objections in the framework of surreal utilities and probabilities, our theory correctly predicts that (1) the pure Pascalian strategy beats all mixed strategies, and (2) what one should do in a Pascalian decision problem depends on what one's credence function is like. Our analysis therefore suggests that although Pascal's Wager is mathematically coherent, it does not deliver what it purports to, a rationally compelling argument that people should lead a religious life regardless of how confident they are in theism and its alternatives.
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings
Authors: Hua-Liang Wei
Date: 2021-11-01 00:00:00
Tasks: Anomaly Detection, Dynamic Time Warping, Time Series, Time Series Analysis

ECGs objectively reflects the working conditions of the hearts as these signals contain vast physiological and pathological information. In this work, in order to improve the efficiency and accuracy of "best so far" time series analysis-based ECG anomaly detection methods, a novel method, comprising a modified dynamic time warping (MDTW) and an innovative average non-self match distance (ANSD) measure, is proposed for ECG anomaly detection. To evaluate the performance of the proposed method, the proposed method is applied to real ECG data selected from the MIT-BIH heartbeat database. To provide a reference for comparison, two existing anomaly detection methods, namely, brute force discord discovery (BFDD) and adaptive window discord discovery (AWDD), are also applied to the same data. The experimental results show that our proposed method outperforms BFDD and AWD.
Energy-efficient Cooperative Offloading for Edge Computing-enabled Vehicular Networks
Authors: Hewon Cho, Ying Cui, Jemin Lee
Date: 2021-11-01 00:00:00
Tasks: Edge-computing

Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, it is still a challenge to fully unleash the potential of edge computing in edge computing-enabled vehicular networks. In this paper, we develop the energy-efficient cooperative offloading scheme for edge computing-enabled vehicular networks, which splits the task into multiple subtasks and offloads them to different roadside units (RSUs) located ahead along the route of the vehicle. We first establish novel cooperative offloading models for the offline and online scenarios in edge computing-enabled vehicular networks. In each offloading scenario, we formulate the total energy minimization with respect to the task splitting ratio, computation resource, and communication resource. In the offline scenario, we equivalently transform the original problem to a convex problem and obtain optimal solutions for multi-vehicle case and single-vehicle case, respectively. Furthermore, we show that the method proposed for the offline scenario can also be applied to solve the optimization problem in the online scenario. Finally, through numerical results, by analyzing the impact of network parameters on the total energy consumption, we verify that our proposed solution consumes lower energy than baseline schemes.
A Matched-filter based method in the Synthetic Aperture Radar Images Using FMCW radar
Authors: Moein Movafagh, Avik Santra, Daniel Oloumi
Date: 2021-10-31 00:00:00

The stretch processing architecture is commonly used for frequency modulated continuous wave (FMCW) radar due to its inexpensive hardware, low sampling rate, and simple architecture. However, the stretch processing architecture is not able to achieve optimal Signal to Noise ratio (SNR) in comparison to the matched-filter architecture. In this paper, we aim to propose a method whereby stretch processing can achieve optimal SNR. Hence, we develop a novel processing method to enable applying a matched filter to the output of the stretch processing. The proposed architecture achieves optimal SNR while it can operate on a low sampling rate. In addition, the combination of the proposed radar architecture and SAR technique can generate high-quality images. To evaluate the performance of the proposed architecture, four scenarios are considered. Simulation is carried out based on these scenarios. The simulation results show that the proposed radar demonstrates the ability to generate an image with higher quality over stretch processing. This proposed radar can also bring a bigger gain compression.
Wireless Federated Learning over MIMO Networks: Joint Device Scheduling and Beamforming Design
Authors: Shaoming Huang, Pengfei Zhang, Yijie Mao, Lixiang Lian, Yuanming Shi
Date: 2021-10-31 00:00:00
Tasks: Federated Learning, Scheduling

Federated learning (FL) is recognized as a key enabling technology to support distributed artificial intelligence (AI) services in future 6G. By supporting decentralized data training and collaborative model training among devices, FL inherently tames privacy leakage and reduces transmission costs. Whereas, the performance of the wireless FL is typically restricted by the communication latency. Multiple-input multiple-output (MIMO) technique is one promising solution to build up a communication-efficient edge FL system with limited radio resources. In this paper, we propose a novel joint device scheduling and receive beamforming design approach to reduce the FL convergence gap over shared wireless MIMO networks. Specifically, we theoretically establish the convergence analysis of the FL process, and then apply the proposed device scheduling policy to maximize the number of weighted devices under the FL system latency and sum power constraints. Numerical results verify the theoretical analysis of the FL convergence and exhibit the appealing learning performance of the proposed approach.
Reconfigurable Intelligent Surface-induced Randomness for mmWave Key Generation
Authors: Shubo Yang, Han Han, Yihong Liu, Weisi Guo, Zhibo Pang, Lei Zhang
Date: 2021-10-31 00:00:00
Tasks: Quantization

Secret key generation in physical layer security exploits the unpredictable random nature of wireless channels. The millimeter-wave (mmWave) channels have limited multipath and channel randomness in static environments. In this paper, for mmWave secret key generation of physical layer security, we use a reconfigurable intelligent surface (RIS) to induce randomness directly in wireless environments, without adding complexity to transceivers. We consider RIS to have continuous individual phase shifts (CIPS) and derive the RIS-assisted reflection channel distribution with its parameters. Then, we propose continuous group phase shifts (CGPS) to increase the randomness specifically at legal parties. Since the continuous phase shifts are expensive to implement, we analyze discrete individual phase shifts (DIPS) and derive the corresponding channel distribution, which is dependent on the quantization bit. We then derive the secret key rate (SKR) to evaluate the randomness performance. With the simulation results verifying the analytical results, this work explains the mathematical principles and lays a foundation for future mmWave evaluation and optimization of artificial channel randomness.
Von Mises Tapering: A New Circular Windowing
Authors: H. M. de Oliveira, R. J. Cintra
Date: 2021-10-30 00:00:00

Discrete and continuous standard windowing are revisited and a a new taper is introduced, which is derived from the normal circular distribution by von Mises. Both the continuous-time and the discrete-time windows are considered, and their spectra obtained. A brief comparison with further classical window families is performed in terms of their properties in the spectral domain. These windows can be used in spectral analysis, and in particular, in the design of FIR (finite impulse response) filters as an alternative to the Kaiser window
Interoperable and scalable echosounder data processing with Echopype
Authors: Wu-Jung Lee, Landung Setiawan, Caesar Tuguinay, Emilio Mayorga, Valentina Staneva
Date: 2021-10-30 00:00:00
Tasks: Cloud Computing, Distributed Computing

Echosounders are high-frequency sonar systems used to sense fish and zooplankton underwater. Their deployment on a variety of ocean observing platforms is generating vast amounts of data at an unprecedented speed from the oceans. Efficient and integrative analysis of these data, whether across different echosounder instruments or in combination with other oceanographic datasets, is crucial for understanding marine ecosystem response to the rapidly changing climate. Here we present Echopype, an open-source Python software library designed to address this need. By standardizing data as labeled, multi-dimensional arrays encoded in the widely embraced netCDF data model following a community convention, Echopype enhances the interoperability of echosounder data, making it easier to explore and use. By leveraging scientific Python libraries optimized for distributed computing, Echopype achieves computational scalability, enabling efficient processing in both local and cloud computing environments. Echopype's modularized package structure further provides a unified framework for expanding support for additional instrument raw data formats and incorporating new analysis functionalities. We plan to continue developing Echopype by supporting and collaborating with the echosounder user community, and envision that the growth of this package will catalyze the integration of echosounder data into broader regional and global ocean observation strategies.
Large and moderate deviations for importance sampling in the Heston model
Authors: Marc Geha, Antoine Jacquier, Zan Zuric
Date: 2021-10-30 00:00:00

We provide a detailed importance sampling analysis for variance reduction in stochastic volatility models. The optimal change of measure is obtained using a variety of results from large and moderate deviations: small-time, large-time, small-noise. Specialising the results to the Heston model, we derive many closed-form solutions, making the whole approach easy to implement. We support our theoretical results with a detailed numerical analysis of the variance reduction gains.
Statistical Consequences of Dueling Bandits
Authors: Nayan Saxena, Pan Chen, Emmy Liu
Date: 2021-10-16 00:00:00

Multi-Armed-Bandit frameworks have often been used by researchers to assess educational interventions, however, recent work has shown that it is more beneficial for a student to provide qualitative feedback through preference elicitation between different alternatives, making a dueling bandits framework more appropriate. In this paper, we explore the statistical quality of data under this framework by comparing traditional uniform sampling to a dueling bandit algorithm and find that dueling bandit algorithms perform well at cumulative regret minimisation, but lead to inflated Type-I error rates and reduced power under certain circumstances. Through these results we provide insight into the challenges and opportunities in using dueling bandit algorithms to run adaptive experiments.
Bioremediation of Contaminated Soil with Crude Oil Using Consortium of bacteria
Authors: Ahmed Mohamed Taher, Ibrahim Omar Saeed
Date: 2021-10-31 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

This study included isolate petroleum hydrocarbons degradable bacteria and develops a consortium or a mixture of bacteria with high biodegradation capabilities which can be used in biological treatment units of the contaminated soils before release. In this studyt ten bacterial strains were isolated from soils contaminated with crude oil, by primary and secondary screening, and by using the sterile saline solution with 1% of crude oil, five bacterial isolates that can degrade oil were identified. The bacterial isolates were isolated from polluted soils with crude oil, the samples were collected from different areas of the Baiji refinery, and samples of heavy crude oil extracted from Qayyarah fields were used in designing the biodegradation experiments, the five isolates were diagnosed based on phenotypic, culture and biochemical characterizes, and detection of the gene sequence of bacterial isolates (16SrRNA). The (16SrRNA) gene sequence of the bacterial isolates was recorded under the accession numbers LC596402, LC596403, LC596406, LC596404, LC596405, for the genus (AM-I-1, AM-I-2, AM-I-5) and the species (AM-I-3, AM-I-4) that following Bacillus Sp respectively, in the NCBI's GenBank, the efficiency of the five isolates were examined for utilizing petroleum hydrocarbons using a sterile mineral salt medium MS supplemented with crude oil as the sole source of carbon with different concentrations, the results showed that the decomposition of petroleum hydrocarbons at the concentrations (0.5, 1, 1.5, 2, 3) % reached (57.32, 69.36,63.71, 75.20, 68.60) %, respectively, for mixed isolates, depending on the results of the gas chromatography (GC) analysis.
Fragment-based Sequential Translation for Molecular Optimization
Authors: Benson Chen, Xiang Fu, Regina Barzilay, Tommi Jaakkola
Date: 2021-10-26 00:00:00
Proceeding: NeurIPS Workshop AI4Scien 2021 12
Tasks: Drug Discovery, Reinforcement Learning (RL), Translation

Searching for novel molecular compounds with desired properties is an important problem in drug discovery. Many existing frameworks generate molecules one atom at a time. We instead propose a flexible editing paradigm that generates molecules using learned molecular fragments--meaningful substructures of molecules. To do so, we train a variational autoencoder (VAE) to encode molecular fragments in a coherent latent space, which we then utilize as a vocabulary for editing molecules to explore the complex chemical property space. Equipped with the learned fragment vocabulary, we propose Fragment-based Sequential Translation (FaST), which learns a reinforcement learning (RL) policy to iteratively translate model-discovered molecules into increasingly novel molecules while satisfying desired properties. Empirical evaluation shows that FaST significantly improves over state-of-the-art methods on benchmark single/multi-objective molecular optimization tasks.
CdtGRN: Construction of qualitative time-delayed gene regulatory networks with a deep learning method
Authors: Ruijie Xu, Lin Zhang, Yu Chen
Date: 2021-10-30 00:00:00

Background:Gene regulations often change over time rather than being constant. But many of gene regulatory networks extracted from databases are static. The tumor suppressor gene $P53$ is involved in the pathogenesis of many tumors, and its inhibition effects occur after a certain period. Therefore, it is of great significance to elucidate the regulation mechanism over time points. Result:A qualitative method for representing dynamic gene regulatory network is developed, called CdtGRN. It adopts the combination of convolutional neural networks(CNN) and fully connected networks(DNN) as the core mechanism of prediction. The ionizing radiation Affymetrix dataset (E-MEXP-549) was obtained at ArrayExpress, by microarray gene expression levels predicting relations between regulation. CdtGRN is tested against a time-delayed gene regulatory network with $22,284$ genes related to $P53$. The accuracy of CdtGRN reaches 92.07$\%$ on the classification of conservative verification set, and a kappa coefficient reaches $0.84$ and an average AUC accuracy is 94.25$\%$. This resulted in the construction of. Conclusion:The algorithm and program we developed in our study would be useful for identifying dynamic gene regulatory networks, and objectively analyze the delay of the regulatory relationship by analyzing the gene expression levels at different time points. The time-delayed gene regulatory network of $P53$ is also inferred and represented qualitatively, which is helpful to understand the pathological mechanism of tumors.
Relating Eulerian and Lagrangian spatial models for vector-host diseases dynamics through a fundamental matrix
Authors: Esteban Vargas Bernal, Omar Saucedo, Joseph Hua Tien
Date: 2021-10-29 00:00:00

We explore the relationship between Eulerian and Lagrangian approaches for modeling movement in vector-borne diseases for discrete space. In the Eulerian approach we account for the movement of hosts explicitly through movement rates captured by a graph Laplacian matrix $L$. In the Lagrangian approach we only account for the proportion of time that individuals spend in foreign patches through a mixing matrix $P$. We establish a relationship between an Eulerian model and a Lagrangian model for the hosts in terms of the matrices $L$ and $P$. We say that the two modeling frameworks are consistent if for a given matrix $P$, the matrix $L$ can be chosen so that the residence times of the matrix $P$ and the matrix $L$ match. We find a sufficient condition for consistency, and examine disease quantities such as the final outbreak size and basic reproduction number in both the consistent and inconsistent cases. In the special case of a two-patch model, we observe how similar values for the basic reproduction number and final outbreak size can occur even in the inconsistent case. However, there are scenarios where the final sizes in both approaches can significantly differ by means of the relationship we propose.