pwc-archive/papers-with-abstracts huggingface dataset:
Synthetic Controls with spillover effects: A comparative study
Authors: Andrii Melnychuk
Date: 2024-05-02 00:00:00

Iterative Synthetic Control Method is introduced in this study, a modification of the Synthetic Control Method (SCM) designed to improve its predictive performance by utilizing control units affected by the treatment in question. This method is then compared to other SCM modifications: SCM without any modifications, SCM after removing all spillover-affected units, Inclusive SCM, and the SP SCM model. For the comparison, Monte Carlo simulations are utilized, generating artificial datasets with known counterfactuals and comparing the predictive performance of the methods. Generally, the Inclusive SCM performed best in all settings and is relatively simple to implement. The Iterative SCM, introduced in this paper, was in close seconds, with a small difference in performance and a simpler implementation.
Uncertainty of Supply Chains: Risk and Ambiguity
Authors: d'Artis Kancs
Date: 2024-05-06 00:00:00
Tasks: Decision Making

Motivated by the recently experienced systemic shocks (the COVID-19 pandemic and the full-fledged Russia's war of aggression against Ukraine) - that have created new forms of uncertainties to our supplies - this paper explores the supply chain robustness under risk aversion and ambiguity aversion. We aim to understand the potential consequences of deeply uncertain systemic events on the supply chain resilience and how does the information precision affect individual agents' choices and the chain-level preparedness to aggregate shocks. Augmenting a parsimonious supply chain model with uncertainty, we analyse the relationship between the upstream sourcing decisions and the supply chain survival probability. Both risk-averse and ambiguity-averse individually-optimising agents' upstream sourcing paths are efficient but can become vulnerable to aggregate shocks. In contrast, a chain-level coordination of downstream firm sourcing decisions can qualitatively improve the robustness of the entire supply chain compared to the individual decision-making baseline. Such a robust decision making ensures that in the presence of an aggregate shock - independently of its realisation - part of upstream suppliers will survive and the final goods' supply will be ensured even under the most demanding circumstances. Our results also indicate that an input source diversification extracts a cost in foregone efficiency.
Monetary Policies on Green Financial Markets: Evidence from a Multi-Moment Connectedness Network
Authors: Tingguo Zheng, Hongyin Zhang, Shiqi Ye
Date: 2024-05-04 00:00:00
Tasks: Decision Making, Management

This paper introduces a novel multi-moment connectedness network approach for analyzing the interconnectedness of green financial market. Focusing on the impact of monetary policy shocks, our study reveals that connectedness within the green bond and equity markets varies with different moments (returns, volatility, skewness, and kurtosis) and changes significantly around Federal Open Market Committee (FOMC) events. Static analysis shows a decrease in connectedness with higher moments, while dynamic analysis highlights increased sensitivity to event-driven shocks. We find that both tight and loose monetary policy shocks initially elevate connectedness within the first six months. However, the effects of tight shocks gradually fade, whereas loose shocks may reduce connectedness after one year. These results offer insight to policymakers in regulating sustainable economies and investment managers in strategizing asset allocation and risk management, especially in environmentally focused markets. Our study contributes to understanding the complex dynamics of the green financial market in response to monetary policies, helping in decision-making for sustainable economic development and financial stability.
Crypto Market Analysis & Real-Estate Business Protocol Proposal | Application of Ethereum Blockchain
Authors: Sid Bhatia, Samuel Gedal, Himaya Jeyakumar Grace Lee, Ravinder Chopra, Daniel Roman, Shrijani Chakroborty
Date: 2024-05-04 00:00:00

This paper examines the dynamics of the cryptocurrency market and proposes a novel blockchain-based protocol for real estate transactions. Our analysis includes a detailed review of price trends, volatility, and correlations within the cryptocurrency market, focusing on major assets like Bitcoin, Ethereum, and Tether. We provide a critical assessment of the impact of significant market events, such as the FTX bankruptcy, highlighting the vulnerabilities and resilience of the crypto market. The study also explores the potential of blockchain technology to innovate real estate transactions by enabling the secure and transparent handling of property deeds without traditional intermediaries. We introduce a blockchain protocol that reduces transaction costs, enhances security, and increases transparency, making real estate transactions more accessible and efficient. Our proposal aims to leverage the inherent benefits of blockchain to address real-world challenges in real estate transactions, providing a scalable and secure platform for property sales in a global market.
Energy Price and Workload Related Dispatching Rule: Balancing Energy and Production Logistics Costs
Authors: Balwin Bokor, Wolfgang Seiringer, Klaus Altendorfer, Thomas Felberbauer
Date: 2024-05-03 00:00:00
Tasks: Decision Making

In response to the escalating need for sustainable manufacturing practices amid fluctuating energy prices, this study introduces a novel dispatching rule that integrates energy price and workload considerations with Material Requirement Planning (MRP) to optimize production logistics and energy costs. The dispatching rule effectively adjusts machine operational states, i.e. turn the machine on or off, based on current energy prices and workload. By developing a stochastic multi-item multi-stage job shop simulation model, this research evaluates the performance of the dispatching rule through a comprehensive full-factorial simulation. Findings indicate a significant enhancement in shop floor decision-making through reduced overall costs. Moreover, the analysis of the Pareto front reveals trade-offs between minimizing energy and production logistics costs, aiding decision-makers in selecting optimal configurations.
Evaluating Production Planning and Control Systems in Different Environments: A Comparative Simulation Study
Authors: Wolfgang Seiringer, Balwin Bokor, Klaus Altendorfer
Date: 2024-05-03 00:00:00

Selecting the appropriate production planning and control systems (PPCS) presents a significant challenge for many companies, as their performance, i.e., overall costs, depends on the production system environment. Key environmental characteristics include the system's structure, i.e., flow shop, hybrid shop, or job shop, and the planned shop load. Besides selecting a suitable PPCS, its parameterization significantly influences the performance. This publication investigates the performance and the optimal parametrization of Material Requirement Planning (MRP), Reorder Point System (RPS), and Constant Work In Progress (ConWIP) at different stochastic multi-item multi-stage production system environments by conducting a comprehensive full factorial simulation study. The results indicate that MRP and ConWIP generally outperform RPS in all observed environments. Moreover, when comparing MRP with ConWIP, the performance clearly varies depending on the specific production system environment.
Manipulation of Belief Aggregation Rules
Authors: Christopher P. Chambers, Federico Echenique, Takashi Hayashi
Date: 2024-05-02 00:00:00

This paper studies manipulation of belief aggregation rules in the setting where the society first collects individual's probabilistic opinions and then solves a public portfolio choice problem with common utility based on the aggregate belief. First, we show that belief reporting in Nash equilibrium under the linear opinion pool and log utility is identified as the profile of state-contingent wealth shares in parimutuel equilibrium with risk-neutral preference. Then we characterize belief aggregation rules which are Nash-implementable. We provide a necessary and essentially sufficient condition for implementability, which is independent of the common risk attitude.
Automatic Assessment of Dysarthria Using Audio-visual Vowel Graph Attention Network
Authors: Xiaokang Liu, Xiaoxia Du, Juan Liu, Rongfeng Su, Manwa Lawrence Ng, Yumei Zhang, Yudong Yang, Shaofeng Zhao, Lan Wang, Nan Yan
Date: 2024-05-06 00:00:00
Tasks: Deep Learning, Graph Attention

Automatic assessment of dysarthria remains a highly challenging task due to high variability in acoustic signals and the limited data. Currently, research on the automatic assessment of dysarthria primarily focuses on two approaches: one that utilizes expert features combined with machine learning, and the other that employs data-driven deep learning methods to extract representations. Research has demonstrated that expert features are effective in representing pathological characteristics, while deep learning methods excel at uncovering latent features. Therefore, integrating the advantages of expert features and deep learning to construct a neural network architecture based on expert knowledge may be beneficial for interpretability and assessment performance. In this context, the present paper proposes a vowel graph attention network based on audio-visual information, which effectively integrates the strengths of expert knowledges and deep learning. Firstly, various features were combined as inputs, including knowledge based acoustical features and deep learning based pre-trained representations. Secondly, the graph network structure based on vowel space theory was designed, allowing for a deep exploration of spatial correlations among vowels. Finally, visual information was incorporated into the model to further enhance its robustness and generalizability. The method exhibited superior performance in regression experiments targeting Frenchay scores compared to existing approaches.
MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition
Authors: Bingshen Mu, Yangze Li, Qijie Shao, Kun Wei, Xucheng Wan, Naijun Zheng, Huan Zhou, Lei Xie
Date: 2024-05-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Multi-Task Learning, Reading Comprehension, Specificity, speech-recognition, Speech Recognition

Despite notable advancements in automatic speech recognition (ASR), performance tends to degrade when faced with adverse conditions. Generative error correction (GER) leverages the exceptional text comprehension capabilities of large language models (LLM), delivering impressive performance in ASR error correction, where N-best hypotheses provide valuable information for transcription prediction. However, GER encounters challenges such as fixed N-best hypotheses, insufficient utilization of acoustic information, and limited specificity to multi-accent scenarios. In this paper, we explore the application of GER in multi-accent scenarios. Accents represent deviations from standard pronunciation norms, and the multi-task learning framework for simultaneous ASR and accent recognition (AR) has effectively addressed the multi-accent scenarios, making it a prominent solution. In this work, we propose a unified ASR-AR GER model, named MMGER, leveraging multi-modal correction, and multi-granularity correction. Multi-task ASR-AR learning is employed to provide dynamic 1-best hypotheses and accent embeddings. Multi-modal correction accomplishes fine-grained frame-level correction by force-aligning the acoustic features of speech with the corresponding character-level 1-best hypothesis sequence. Multi-granularity correction supplements the global linguistic information by incorporating regular 1-best hypotheses atop fine-grained multi-modal correction to achieve coarse-grained utterance-level correction. MMGER effectively mitigates the limitations of GER and tailors LLM-based ASR error correction for the multi-accent scenarios. Experiments conducted on the multi-accent Mandarin KeSpeech dataset demonstrate the efficacy of MMGER, achieving a 26.72% relative improvement in AR accuracy and a 27.55% relative reduction in ASR character error rate, compared to a well-established standard baseline.
All-in-One Deep Learning Framework for MR Image Reconstruction
Authors: Geunu Jeong, Hyeonsoo Kim, Joonyoung Yang, Kyungeun Jang, Jeewook Kim
Date: 2024-05-06 00:00:00
Tasks: All, Deep Learning, Denoising, Image Reconstruction, Super-Resolution

We introduce a novel, all-in-one deep learning framework for MR image reconstruction, enabling a single model to enhance image quality across multiple aspects of k-space sampling and to be effective across a wide range of clinical and technical scenarios. This DICOM-based algorithm serves as the core of SwiftMR (AIRS Medical, Seoul, Korea), which is FDA-cleared, CE-certified, and commercially available. We first detail the comprehensive development process of the model, including data collection, training pair preparation, model architecture design, and DICOM inference. We then assess the model's capability to enhance image quality in a multi-dimensional manner, specifically across various aspects of k-space sampling. Subsequently, we evaluate several features of the multi-dimensional enhancement: the accuracy of tunable denoising, the effectiveness of super-resolution in each encoding direction, and the reduction of artifacts that become more prominent at lower spatial resolutions. Additionally, we assess its compatibility with various scan parameter sets and its generalizability across scanner vendors not seen during training. Finally, we present specific cases demonstrating the model's utility in reducing scan time across anatomical regions in conjunction with protocol optimization. The proposed model is compatible with a broad spectrum of scenarios, including various vendors, pulse sequences, scan parameters, and anatomical regions. Its DICOM-based operation particularly enhances its applicability for real-world applications. Given its demonstrated effectiveness and versatility, we expect its use to expand in the field of clinical MRI.
Infrared Polarization Imaging-based Non-destructive Thermography Inspection
Authors: Xianyu Wu, Bin Zhou, Peng Lin, Rongjin Cao, Feng Huang
Date: 2024-05-06 00:00:00
Tasks: Object

Infrared pulse thermography non-destructive testing (NDT) method is developed based on the difference in the infrared radiation intensity emitted by defective and non-defective areas of an object. However, when the radiation intensity of the defective target is similar to that of the non-defective area of the object, the detection results are poor. To address this issue, this study investigated the polarization characteristics of the infrared radiation of different materials. Simulation results showed that the degree of infrared polarization of the object surface changed regularly with changes in thermal environment radiation. An infrared polarization imaging-based NDT method was proposed and demonstrated using specimens with four different simulated defective areas, which were designed and fabricated using four different materials. The experimental results were consistent with the simulation results, thereby proving the effectiveness of the proposed method. Compared with the infrared-radiation-intensity-based NDT method, the proposed method improved the image detail presentation and detection accuracy.
Solution for Authenticity Identification of Typical Target Remote Sensing Images
Authors: Yipeng Lin, Xinger Li, Yang Yang
Date: 2024-05-03 00:00:00

In this paper, we propose a basic RGB single-mode model based on weakly supervised training under pseudo labels, which performs high-precision authenticity identification under multi-scene typical target remote sensing images. Due to the imprecision of Mask generation, we divide the task into two sub-tasks: generating pseudo-mask and fine-tuning model based on generated Masks. In generating pseudo masks, we use MM-Fusion as the base model to generate masks for large objects such as planes and ships. By manually calibrating the Mask of a small object such as a car, a highly accurate pseudo-mask is obtained. For the task of fine-tuning models based on generating masks, we use the WSCL model as the base model. It is worth noting that due to the difference between the generated pseudo-Masks and the real Masks, we discard the image feature extractors such as SRM and Noiseprint++ in WSCL, and select the unscaled original image for training alone, which greatly ensures the match between the image and the original label. The final trained model achieved a score of 90.7702 on the test set.
Technical report on target classification in SAR track
Authors: Haonan Xu, Han Yinan, Haotian Si, Yang Yang
Date: 2024-05-03 00:00:00
Tasks: Classification, Data Augmentation, Out-of-Distribution Detection

This report proposes a robust method for classifying oceanic and atmospheric phenomena using synthetic aperture radar (SAR) imagery. Our proposed method leverages the powerful pre-trained model Swin Transformer v2 Large as the backbone and employs carefully designed data augmentation and exponential moving average during training to enhance the model's generalization capability and stability. In the testing stage, a method called ReAct is utilized to rectify activation values and utilize Energy Score for more accurate measurement of model uncertainty, significantly improving out-of-distribution detection performance. Furthermore, test time augmentation is employed to enhance classification accuracy and prediction stability. Comprehensive experimental results demonstrate that each additional technique significantly improves classification accuracy, confirming their effectiveness in classifying maritime and atmospheric phenomena in SAR imagery.
Distributed Estimation in Blockchain-aided Internet of Things in the Presence of Attacks
Authors: Hamid Varmazyari, Yiming Jiang, Jiangfan Zhang
Date: 2024-05-06 00:00:00
Tasks: valid

Distributed estimation in a blockchain-aided Internet of Things (BIoT) is considered, where the integrated blockchain secures data exchanges across the BIoT and the storage of data at BIoT agents. This paper focuses on developing a performance guarantee for the distributed estimation in a BIoT in the presence of malicious attacks which jointly exploits vulnerabilities present in both IoT devices and the employed blockchain within the BIoT. To achieve this, we adopt the Cramer-Rao Bound (CRB) as the performance metric, and maximize the CRB for estimating the parameter of interest over the attack domain. However, the maximization problem is inherently non-convex, making it infeasible to obtain the globally optimal solution in general. To address this issue, we develop a relaxation method capable of transforming the original non-convex optimization problem into a convex optimization problem. Moreover, we derive the analytical expression for the optimal solution to the relaxed optimization problem. The optimal value of the relaxed optimization problem can be used to provide a valid estimation performance guarantee for the BIoT in the presence of attacks.
Energy-Based Optimization of Physical-Layer Challenge-Response Authentication with Drones
Authors: Francesco Ardizzon, Damiano Salvaterra, Mattia Piana, Stefano Tomasin
Date: 2024-05-06 00:00:00

Drones are expected to be used for many tasks in the future and require secure communication protocols. In this work, we propose a novel physical layer authentication (PLA)-based challenge-response (CR) protocol in which a drone Bob authenticates the sender (either on the ground or air) by exploiting his prior knowledge of the wireless channel statistic (fading, path loss, and shadowing). In particular, Bob will move to a set of positions in the space, and by estimating the attenuations of the received signals he will authenticate the sender. We take into account the energy consumption in the design and provide three solutions: a purely greedy solution (PG), an optimal Bellman iterative solution (BI), and a heuristic solution based on the evaluation of the standard deviation of the attenuations in the space. Finally, we demonstrate the effectiveness of our approach through numerical simulations.
Improving the Ranging Performance of Random ISAC Signals Through Pulse Shaping Design
Authors: Zihan Liao, Fan Liu, Shuangyang Li, Yifeng Xiong, Weijie Yuan, Marco Lops
Date: 2024-05-06 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we propose a novel pulse shaping design for single-carrier integrated sensing and communication (ISAC) transmission. Due to the communication information embedded in the ISAC signal, the resulting auto-correlation function (ACF) is determined by both the information-conveying random symbol sequence and the signaling pulse, where the former leads to random fluctuations in the sidelobes of the ACF, impairing the range estimation performance. To overcome this challenge, we first analyze the statistical characteristics of the random ACF under the symbol-wise pulse shaping (SWPS) regime. As a step further, we formulate an optimization problem to design ISAC pulse shaping filters, which minimizes the average integrated sidelobe level ratio (ISLR) while meeting the Nyquist criterion, subject to power and bandwidth constraints. We then show that the problem can be recast as a convex quadratic program by expressing it in the frequency domain, which can be readily solved through standard tools. Numerical results demonstrate that the proposed pulse shaping design achieves substantial ranging sidelobe reduction compared to the celebrated root-raised cosine (RRC) pulse shaping, given that the communication throughput is unchanged.
Distributed Adaptive Spatial Filtering with Inexact Local Solvers
Authors: Charles Hovine, Alexander Bertrand
Date: 2024-05-06 00:00:00

The Distributed Adaptive Signal Fusion (DASF) framework is a meta-algorithm for computing data-driven spatial filters in a distributed sensing platform with limited bandwidth and computational resources, such as a wireless sensor network. The convergence and optimality of the DASF algorithm has been extensively studied under the assumption that an exact, but possibly impractical solver for the local optimization problem at each updating node is available. In this work, we provide convergence and optimality results for the DASF framework when used with an inexact, finite-time solver such as (proximal) gradient descent or Newton's method. We provide sufficient conditions that the solver should satisfy in order to guarantee convergence of the resulting algorithm, and a lower bound for the convergence rate. We also provide numerical simulations to validate these theoretical results.