pwc-archive/papers-with-abstracts huggingface dataset:
Specific Aspects of Intellectual Property Management in the Knowledge-Based Economy
Authors: Aurel Mihail Titu, Alina Bianca Pop, Camelia Oprean-Stan, Sebastian Emanuel Stan
Date: 2025-01-14 00:00:00
Tasks: Management

This paper addresses the issue of intellectual property management in the knowledge-based economy. The starting point in carrying out the study is the presentation of some concepts regarding in the first phase, the intellectual capital. Arguments are made that the knowledge-based economy is a challenge for the current century. The subject of intellectual property is approached through the prism of a topical concept operationalized in the current global economic context. The main institutions that are directly related to this concept are mentioned. The topic of patents related to WOS indexed scientific papers is also debated, along with a series of statistics and studies on the state of patent protection worldwide in the top fields. The last part of the paper contains the conclusions and own points of view on the debated topic.
Estimation of the Effect of Carbon Tax Implementation on Household Income Distribution in Indonesia: Quantitative Analysis with Miyazawa Input- Output Approach
Authors: Syahrituah Siregar
Date: 2025-01-14 00:00:00

Climate change is a global challenge caused by greenhouse gas emissions from fossil fuel use. Indonesia, as a developing country, faces major challenges in implementing carbon tax policies to reduce emissions, especially related to their regressive impacts on low-income households. Currently, there is little in-depth research on how carbon tax policies impact household income distribution in Indonesia. This study uses a quantitative approach with the Input- Output model to analyze the impact of carbon tax on household income based on 10 income groups, both in urban and rural areas. The results show that carbon tax policies have a regressive impact, where low-income households bear a proportionally greater burden. Household income in Class - 10 decreased by IDR 19,144.85 million in urban areas and IDR 8,819.13 million in rural areas, while households in Class - 1 decreased by IDR 954.23 million. Therefore, mitigation policies such as cross subsidies are needed to reduce the impact on vulnerable groups. These findings are important for policy makers in formulating fair and effective fiscal policies, as well as ensuring social justice in the context of sustainable development. This study has limitations in the scope of analysis of long-term energy consumption behavior and certain sectors, so further research is needed to deepen these aspects.
An Approach on the Modelling of Long Economic Cycles in the Context of Sustainable Development
Authors: Cristina Tanasescu, Amelia Bucur, Camelia Oprean-Stan
Date: 2025-01-14 00:00:00

One of the themes that have been approached more and more within the specialised literature is being represented by economic cycles. The analysis of these is very useful in the long term predictions, in finding solutions for the economic raise and for detecting the economic crisis. At the same time, it is underlined in a lot of scientific and research papers, the importance of the sustainable development in the present and future society. In this paper we intend to bring contributions to the study of the cycles of a sustainable economy and we will analyse it having in mind the purpose of creating the sustainable economy. We will demonstrate the fact that curves that represent graphically all these, are not simple logistics anymore, bi-logistics or multilogistics curves, but curves in plan that are obtained by composing logistics functions with the function of the sustainable development or with the function that shapes the economic component of it mathematically. We will present an interpretation of mathematic models within the frame of the sustainable development.
A novel approach to assessing corporate sustainable economic value
Authors: Camelia Oprean-Stan
Date: 2025-01-11 00:00:00

The goal of this study is to propose a new concept, Sustainable Economic Value, to define it logically, and to build a simplified model for its evaluation.
Is the Monetary Transmission Mechanism Broken? Time for People's Quantitative Easing
Authors: Sebastian Dragoe, Camelia Oprean-Stan
Date: 2025-01-11 00:00:00

The monetary transmission channel is disrupted by many factors, especially securitization and liquidity traps. In our study we try to estimate the effect of securitization on the interest elasticity and to identify if a liquidity trap occurred during 1954Q3-2019Q3. The yield curve inversion mechanism shows us that economic cycles are very sensitive to decreasing profitability of banks. However there is no evidence that restoring their profits will ensure a strong recovery. In this regard, we research the low effect of Quantitative Easing (QE) upon economic growth and analyze whether securitization and liquidity traps posed challenges to QE or is it the mainstream theory flawed. In this regard we will examine the main weaknesses of QE, respectively the speculative behavior induced by artificial low rates and its unequal distribution. We propose a new form of QE that will relief households and not reward banks for their risky behavior before recession.
Endogenous Persistence at the Effective Lower Bound
Authors: Chunbing Cai, Jordan Roulleau-Pasdeloup, Zhongxi Zheng
Date: 2025-01-11 00:00:00

We develop a perfect foresight method to solve models with an interest rate lower bound constraint that nests OccBin/DynareOBC and \cite{Eggertsson2010}'s as well as \cite{Mertens2014}'s pen and paper solutions as special cases. Our method generalizes the pen-and-paper solutions by allowing for endogenous persistence while maintaining tractability and interpretability. We prove that our method necessarily gives stable multipliers. We use it to solve a New Keynesian model with habit formation and government spending, which we match to expectations data from the Great Recession. We find an output multiplier of government spending close to 1 for the US and Japan.
Assessing Co-Authored Papers in Tenure Decisions: Implications for Research Independence and Career Strategies in Economics
Authors: Lekang Ren, Danyang Xie
Date: 2025-01-10 00:00:00

In tenure decisions, the treatment of co-authored papers often raises questions about a candidate's research independence. This study examines the effects of solo versus collaborative authorship in high-profile Economics journals on long-term academic success. Our findings confirms the traditional belief that solo-authored publications significantly enhance long-term research output and citation impact compared to collaborative efforts. However, relative to solo-authored papers, international collaborations have a less negative impact on long-term success than national and institutional collaborations. Temporal trends highlight the increasing importance of diverse and international collaborations. These insights provide actionable guidance for tenure committees on evaluating co-authored work and for researchers on optimizing their publication strategies.
Optimizing Supply Chain Networks with the Power of Graph Neural Networks
Authors: Chi-Sheng Chen, Ying-Jung Chen
Date: 2025-01-07 00:00:00
Tasks: Demand Forecasting, Graph Representation Learning, Management, Representation Learning, Scheduling

Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data, offering unprecedented capabilities in tasks like forecasting and optimization. This study investigates the application of GNNs to demand forecasting within supply chain networks using the SupplyGraph dataset, a benchmark for graph-based supply chain analysis. By leveraging advanced GNN methodologies, we enhance the accuracy of forecasting models, uncover latent dependencies, and address temporal complexities inherent in supply chain operations. Comparative analyses demonstrate that GNN-based models significantly outperform traditional approaches, including Multilayer Perceptrons (MLPs) and Graph Convolutional Networks (GCNs), particularly in single-node demand forecasting tasks. The integration of graph representation learning with temporal data highlights GNNs' potential to revolutionize predictive capabilities for inventory management, production scheduling, and logistics optimization. This work underscores the pivotal role of forecasting in supply chain management and provides a robust framework for advancing research and applications in this domain.
A robust measure of complexity
Authors: Egor Bronnikov, Elias Tsakas
Date: 2025-01-15 00:00:00

We introduce a robust belief-based measure of complexity. The idea is that task A is deemed more complex than task B if the probability of solving A correctly is smaller than the probability of solving B correctly regardless of the reward. We fully characterize the corresponding order over the set of tasks. The main characteristic of this relation is that it depends, not only on difficulty (like most complexity definitions in the literature) but also on ex ante uncertainty. Finally, we show that for every task for which information is optimally acquired, there exists a more complex task which always induces less effort regardless of the reward.
On the Dominance of Truth-Telling in Gradual Mechanisms
Authors: Wenqian Wang, Zhiwen Zheng
Date: 2025-01-15 00:00:00

Recent literature highlights the advantages of implementing social rules via dynamic game forms. We characterize when truth-telling remains a dominant strategy in gradual mechanisms implementing strategy-proof social rules, where agents gradually reveal their private information while acquiring information about others in the process. Our first characterization hinges on the incentive-preservation of a basic transformation on gradual mechanisms called illuminating that partitions information sets. The second relies on a single reaction-proofness condition. We demonstrate the usefulness of both characterizations through applications to second-price auctions and the top-trading cycles algorithm.
Bursting Bubbles in a Macroeconomic Model
Authors: Tomohiro Hirano, Keiichi Kishi, Alexis Akira Toda
Date: 2025-01-14 00:00:00
Tasks: model

This paper identifies the conditions and mechanisms that give rise to stochastic bubbles that are expected to collapse. To illustrate the essence of the emergence of stochastic bubbles, we first present a toy model, and then we present a full-fledged macro-finance model of intangible capital and show that stochastic stock bubbles attached to intangible capital emerge in the process of spillover of technological innovation. We show that the dynamics with stochastic bubbles, which is characterized by unbalanced growth, is a temporary deviation from a balanced growth path in which asset prices equal the fundamentals.
Entry deterrence by exploiting economies of scope in data aggregation
Authors: Luis Guijarro, José-Ramón Vidal, Vicent Pla
Date: 2025-01-13 00:00:00

We model a market for data where an incumbent and a challenger compete for data from a producer. The incumbent has access to an exclusive data producer, and it uses this exclusive access, together with economies of scope in the aggregation of the data, as a strategy against the potential entry by the challenger. We assess the incumbent incentives to either deter or accommodate the entry of the challenger. We show that the incumbent will accommodate when the exclusive access is costly and when the economies of scope are low, and it will blockade or deter otherwise. The results would justify an access regulation that incentivizes the entry of the challenger, e.g., by increasing production costs for the exclusive data.
Knowledge Phenomenology Research of Future Industrial Iconic Product Innovation
Authors: Jiang Xu, Haoxiang Qu
Date: 2025-01-13 00:00:00
Tasks: Philosophy

Iconic products, as innovative carriers supporting the development of future industries, are key breakthrough points for driving the transformation of new quality productive forces. This article is grounded in the philosophy of technology and examines the evolution of human civilization to accurately identify the patterns of product innovation. By integrating theories from systems science, it analyzes the intrinsic logical differences between traditional products and iconic products. The study finds that iconic products are based on a comprehensive knowledge system that integrates explicit and tacit knowledge, enabling them to adapt to complex dynamic environments. Therefore, based on the method of phenomenological essence reduction and the process of specialized knowledge acquisition, this study establishes the first principle of knowledge phenomenology: "knowledge generation-moving from the tacit to the explicit-moving from the explicit to the tacit-fusion of the explicit and tacit." Grounded in knowledge phenomenology, it reconstructs the product design evolution process and establishes a forward innovative design framework for iconic products, consisting of "design problem space-explicit knowledge space-tacit knowledge space-innovative solution space." Furthermore, based on FBS design theory, it develops a disruptive technology innovation forecasting framework of "technology problem space-knowledge base prediction-application scenario prediction-coupled technology prediction," which collectively advances the innovation systems engineering of iconic products. In light of the analysis of the global future industrial competitive landscape, it proposes a strategy for enhancing embodied intelligence in iconic products.
Blackwell Equilibrium in Repeated Games
Authors: Costas Cavounidis, Sambuddha Ghosh, Johannes Hörner, Eilon Solan, Satoru Takahashi
Date: 2025-01-07 00:00:00

We apply Blackwell optimality to repeated games. An equilibrium whose strategy profile is sequentially rational for all high enough discount factors simultaneously is a Blackwell (subgame-perfect, perfect public, etc.) equilibrium. The bite of this requirement depends on the monitoring structure. Under perfect monitoring, a ``folk'' theorem holds relative to an appropriate notion of minmax. Under imperfect public monitoring, absent a public randomization device, any perfect public equilibrium generically involves pure action profiles or stage-game Nash equilibria only. Under private conditionally independent monitoring, in a class of games that includes the prisoner's dilemma, the stage-game Nash equilibrium is played in every round.
Estimation and Restoration of Unknown Nonlinear Distortion using Diffusion
Authors: Michal Švento, Eloi Moliner, Lauri Juvela, Alec Wright, Vesa Välimäki
Date: 2025-01-10 00:00:00
Tasks: Audio Effects Modeling, Quantization, Speech Enhancement

The restoration of nonlinearly distorted audio signals, alongside the identification of the applied memoryless nonlinear operation, is studied. The paper focuses on the difficult but practically important case in which both the nonlinearity and the original input signal are unknown. The proposed method uses a generative diffusion model trained unconditionally on guitar or speech signals to jointly model and invert the nonlinear system at inference time. Both the memoryless nonlinear function model and the restored audio signal are obtained as output. Successful example case studies are presented including inversion of hard and soft clipping, digital quantization, half-wave rectification, and wavefolding nonlinearities. Our results suggest that, out of the nonlinear functions tested here, the cubic Catmull-Rom spline is best suited to approximating these nonlinearities. In the case of guitar recordings, comparisons with informed and supervised methods show that the proposed blind method is at least as good as they are in terms of objective metrics. Experiments on distorted speech show that the proposed blind method outperforms general-purpose speech enhancement techniques and restores the original voice quality. The proposed method can be applied to audio effects modeling, restoration of music and speech recordings, and characterization of analog recording media.
Large Model Empowered Streaming Speech Semantic Communications
Authors: Zhenzi Weng, Zhijin Qin, Geoffrey Ye Li
Date: 2025-01-10 00:00:00
Tasks: model, Semantic Communication, Translation

In this paper, we introduce a large model-empowered streaming semantic communication system for speech transmission across various languages, named LSSC-ST. Specifically, we devise an edge-device collaborative semantic communication architecture by offloading the intricate semantic extraction and channel coding modules to edge servers, thereby reducing the computational burden on local devices. To support multilingual speech transmission, pre-trained large speech models are utilized to learn unified semantic features from speech in different languages, breaking the constraint of a single input language and enhancing the practicality of the LSSC-ST. Moreover, the input speech is sequentially streamed into the developed system as short speech segments, which enables low transmission latency without degrading the quality of the produced speech. A novel dynamic speech segmentation algorithm is proposed to further reduce the transmission latency by adaptively adjusting the duration of speech segments. According to simulation results, the LSSC-ST provides more accurate speech transmission and achieves a streaming manner with lower latency compared to the existing non-streaming semantic communication systems.
Sub-band Domain Multi-Hypothesis Acoustic Echo Canceler Based Acoustic Scene Analysis
Authors: Benjamin J Southwell, Yin-Lee Ho, David Gunawan
Date: 2025-01-10 00:00:00

This paper introduces a novel approach for acoustic scene analysis by exploiting an ensemble of statistics extracted from a sub-band domain multi-hypothesis acoustic echo canceler (SDMH-AEC). A well-designed SDMH-AEC employs multiple adaptive filtering strategies with potentially complementary behaviours during convergence, perturbations, and steady-state conditions. By aggregating statistics across the sub-bands, we derive a feature vector that exhibits strong discriminative power for distinguishing different acoustic events and estimating acoustic parameters. The complementary nature of the SDMH-AEC filters provides a rich source of information that can be extracted at insignificant cost for acoustic scene analysis tasks. We demonstrate the effectiveness of the proposed approach experimentally with real data containing double-talk, echo path change and events where the full-duplex device is physically moved. The extracted features enable acoustic scene analysis using existing echo cancellation algorithms and techniques.
UNIR-Net: A Novel Approach for Restoring Underwater Images with Non-Uniform Illumination Using Synthetic Data
Authors: Ezequiel Perez-Zarate, Chunxiao Liu, Oscar Ramos-Soto, Diego Oliva, Marco Perez-Cisneros
Date: 2025-01-15 00:00:00
Tasks: Semantic Segmentation

Enhancing underwater images with non-uniform illumination (NUI) is crucial for improving visibility and visual quality in marine environments, where image degradation is caused by significant absorption and scattering effects. However, traditional model-based methods are often ineffective at capturing the complex illumination variations present in such images, resulting in limited visual improvements. On the other hand, learning-based approaches have shown promising results but face challenges due to the lack of specific datasets designed to effectively address the non-uniform illumination problem. To overcome these challenges, the Underwater Non-uniform Illumination Restoration Network (UNIR-Net) is introduced, a novel method that integrates illumination enhancement and attention blocks, along with visual refinement and contrast correction modules. This approach is specifically designed to mitigate the scattering and absorption effects that cause light attenuation in underwater environments. Additionally, the Paired Underwater Non-uniform Illumination (PUNI) dataset is presented, a paired resource that facilitates the restoration of underwater images under non-uniform illumination conditions. Extensive experiments conducted on the PUNI dataset and the large-scale real-world Non-Uniform Illumination Dataset (NUID), which contains underwater images with non-uniform illumination, demonstrate the robust generalization ability of UNIR-Net. This method outperforms existing approaches in both quantitative metrics and qualitative evaluations. Furthermore, UNIR-Net not only significantly enhances the visual quality of images but also improves performance in advanced computer vision tasks, such as semantic segmentation in underwater environments, highlighting its broad applicability and potential impact. The code of this method is available at https://github.com/xingyumex/UNIR-Net
Bayesian Multifractal Image Segmentation
Authors: Kareth M. León-López, Abderrahim Halimi, Jean-Yves Tourneret, Herwig Wendt
Date: 2025-01-15 00:00:00
Tasks: Image Segmentation, parameter estimation, Segmentation, Semantic Segmentation

Multifractal analysis (MFA) provides a framework for the global characterization of image textures by describing the spatial fluctuations of their local regularity based on the multifractal spectrum. Several works have shown the interest of using MFA for the description of homogeneous textures in images. Nevertheless, natural images can be composed of several textures and, in turn, multifractal properties associated with those textures. This paper introduces a Bayesian multifractal segmentation method to model and segment multifractal textures by jointly estimating the multifractal parameters and labels on images. For this, a computationally and statistically efficient multifractal parameter estimation model for wavelet leaders is firstly developed, defining different multifractality parameters to different regions of an image. Then, a multiscale Potts Markov random field is introduced as a prior to model the inherent spatial and scale correlations between the labels of the wavelet leaders. A Gibbs sampling methodology is employed to draw samples from the posterior distribution of the parameters. Numerical experiments are conducted on synthetic multifractal images to evaluate the performance of the proposed segmentation approach. The proposed method achieves superior performance compared to traditional unsupervised segmentation techniques as well as modern deep learning-based approaches, showing its effectiveness for multifractal image segmentation.
Fast multi-contrast MRI using joint multiscale energy model
Authors: Nima Yaghoobi, Jyothi Rikhab Chand, Yan Chen, Steve R. Kecskemeti, James H. Holmes, Mathews Jacob
Date: 2025-01-11 00:00:00

The acquisition of 3D multicontrast MRI data with good isotropic spatial resolution is challenged by lengthy scan times. In this work, we introduce a CNN-based multiscale energy model to learn the joint probability distribution of the multi-contrast images. The joint recovery of the contrasts from undersampled data is posed as a maximum a posteriori estimation scheme, where the learned energy serves as the prior. We use a majorize-minimize algorithm to solve the optimization scheme. The proposed model leverages the redundancies across different contrasts to improve image fidelity. The proposed scheme is observed to preserve fine details and contrast, offering sharper reconstructions compared to reconstruction methods that independently recover the contrasts. While we focus on 3D MPNRAGE acquisitions in this work, the proposed approach is generalizable to arbitrary multi-contrast settings.