pwc-archive/papers-with-abstracts huggingface dataset:
The role of ion channels in the transmission of signals along axons
Authors: Lízia Maria Gonçalves Branco, Rui Manuel Agostinho Dilão
Date: 2023-12-11 00:00:00
Tasks: Informativeness

This work proposes a two-dimensional electrophysiological model for describing neuronal responses to external electric stimuli under patch-clamped conditions. Our proposed model successfully captures the key features of the Hodgkin-Huxley model, while offering improved informativeness and numerical efficiency. By analysing bifurcation diagrams from various reduced models, we describe the contributions of different ionic channels to the stability or instability of the neurones' response. We also explore the influence of various cell parameters in the neurone's spiking frequency, including the membrane capacitance and the ionic channels' conductivities and Nerst potentials. Additionally, we suggest a spatial model for the axon that effectively simulates unidirectional signal transmission along neurons unaffected by membrane potential perturbations. After analysing the characteristics of this compartmental model, including the impact of intracellular resistance and membrane capacitance on the signal's velocity, we observed considerable discrepancies between our findings and those predicted by the cable equation model for signal propagation. Moreover, our investigation reveals that when the transmission of neuronal signals occurs in multiple peaks, there is a transient variation in their wavelength and frequency, resulting in different peak velocities. Furthermore, our findings consistently demonstrate that the first peak - in some cases, the only peak observed - maintains a relatively constant velocity along the axon regardless of the stimulus intensity and is always faster than the subsequent peaks.
High-speed Opto-electronic Pre-processing of Polar Mellin Transform for Shift, Scale and Rotation Invariant Image Recognition at Record-Breaking Speeds
Authors: Julian Gamboa, Xi Shen, Tabassom Hamidfar, Selim M. Shahriar
Date: 2023-12-11 00:00:00

Space situational awareness demands efficient monitoring of terrestrial sites and celestial bodies, necessitating advanced target recognition systems. Current target recognition systems exhibit limited operational speed due to challenges in handling substantial image data. While machine learning has improved this scenario, highresolution images remain a concern. Optical correlators, relying on analog processes, provide a potential alternative but are hindered by material limitations. Recent advancements in hybrid opto-electronic correlators (HOC) have addressed such limitations, additionally achieving shift, scale, and rotation invariant (SSRI) target recognition through use of the polar Mellin transform (PMT). However, there are currently no techniques for obtaining the PMT at speeds fast enough to take advantage of the inherent speed of the HOC. To that end, we demonstrate an optoelectronic PMT pre-processor that can operate at record-breaking millisecond frame rates using commercially available components for use in an automated SSRI HOC image recognition system for space situational awareness.
High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery
Authors: Shreshth A. Malik, James Walsh, Giacomo Acciarini, Thomas E. Berger, Atılım Güneş Baydin
Date: 2023-11-12 00:00:00
Tasks: Collision Avoidance, Density Estimation, State Estimation

Accurate estimation of thermospheric density is critical for precise modeling of satellite drag forces in low Earth orbit (LEO). Improving this estimation is crucial to tasks such as state estimation, collision avoidance, and re-entry calculations. The largest source of uncertainty in determining thermospheric density is modeling the effects of space weather driven by solar and geomagnetic activity. Current operational models rely on ground-based proxy indices which imperfectly correlate with the complexity of solar outputs and geomagnetic responses. In this work, we directly incorporate NASA's Solar Dynamics Observatory (SDO) extreme ultraviolet (EUV) spectral images into a neural thermospheric density model to determine whether the predictive performance of the model is increased by using space-based EUV imagery data instead of, or in addition to, the ground-based proxy indices. We demonstrate that EUV imagery can enable predictions with much higher temporal resolution and replace ground-based proxies while significantly increasing performance relative to current operational models. Our method paves the way for assimilating EUV image data into operational thermospheric density forecasting models for use in LEO satellite navigation processes.
Estimating Counterfactual Matrix Means with Short Panel Data
Authors: Lihua Lei, Brad Ross
Date: 2023-12-12 00:00:00
Tasks: counterfactual

We develop a new, spectral approach for identifying and estimating average counterfactual outcomes under a low-rank factor model with short panel data and general outcome missingness patterns. Applications include event studies and studies of outcomes of "matches" between agents of two types, e.g. workers and firms, typically conducted under less-flexible Two-Way-Fixed-Effects (TWFE) models of outcomes. Given an infinite population of units and a finite number of outcomes, we show our approach identifies all counterfactual outcome means, including those not estimable by existing methods, if a particular graph constructed based on overlaps in observed outcomes between subpopulations is connected. Our analogous, computationally efficient estimation procedure yields consistent, asymptotically normal estimates of counterfactual outcome means under fixed-$T$ (number of outcomes), large-$N$ (sample size) asymptotics. In a semi-synthetic simulation study based on matched employer-employee data, our estimator has lower bias and only slightly higher variance than a TWFE-model-based estimator when estimating average log-wages.
Export complexity, industrial complexity and regional economic growth in Brazil
Authors: Ben-Hur Francisco Cardoso, Eva Yamila da Silva Catela, Guilherme Viegas, Flávio L. Pinheiro, Dominik Hartmann
Date: 2023-12-12 00:00:00

Research on productive structures has shown that economic complexity conditions economic growth. However, little is known about which type of complexity, e.g., export or industrial complexity, matters more for regional economic growth in a large emerging country like Brazil. Brazil exports natural resources and agricultural goods, but a large share of the employment derives from services, non-tradables, and within-country manufacturing trade. Here, we use a large dataset on Brazil's formal labor market, including approximately 100 million workers and 581 industries, to reveal the patterns of export complexity, industrial complexity, and economic growth of 558 micro-regions between 2003 and 2019. Our results show that export complexity is more evenly spread than industrial complexity. Only a few -- mainly developed urban places -- have comparative advantages in sophisticated services. Regressions show that a region's industrial complexity is a significant predictor for 3-year growth prospects, but export complexity is not. Moreover, economic complexity in neighboring regions is significantly associated with economic growth. The results show export complexity does not appropriately depict Brazil's knowledge base and growth opportunities. Instead, promoting the sophistication of the heterogeneous regional industrial structures and development spillovers is a key to growth.
Knowledge Management in Socio-Economic Development of Municipal Units: Basic Concepts
Authors: Maria Shishanina, Anatoly Sidorov
Date: 2023-12-12 00:00:00
Tasks: Decision Making, Management

The article discusses the basic concepts of strategic planning in the Russian Federation, highlights the legal, financial and resource features that act as restrictions in decision making in the field of socio-economic development of municipalities. The analysis concluded that to design an adequate model of socio-economic development of municipalities is a very difficult task, particularly when the traditional approaches are applied. To solve the task, we proposed to use the semantic modeling as well as cognitive maps which are able to point out the set of dependencies that arise between factors having a direct impact on socio-economic development.
Would Russian solar energy projects be feasible independent of state subsidies?
Authors: Gordon Rausser, Galina Chebotareva, Wadim Strielkowski, Lubos Smutka
Date: 2023-12-12 00:00:00

This paper explores the critical question of the sustainability of Russian solar energy initiatives in the absence of governmental financial support. The study aims to determine if Russian energy companies can maintain operations in the solar energy sector without relying on direct state subsidies. Methodologically, the analysis utilizes established investment metrics such as Net Present Value (NPV), Internal Rate of Return (IRR), and Discounted Payback Period (DPP), tailored to reflect the unique technical and economic aspects of Russian solar energy facilities and to evaluate the influence of sector-specific risks on project efficiency, using a rating approach. We examined eleven solar energy projects under ten different scenarios to understand the dynamics of direct state support, exploring variations in support cessation, reductions in financial assistance, and the projects' resilience to external risk factors. Our multi-criteria scenario assessment indicates that, under the prevailing market conditions, the Russian solar energy sector is not yet equipped to operate efficiently without ongoing state financial subsidies. Interestingly, our findings also suggest that the solar energy sector in Russia has a greater potential to reduce its dependence on state support compared to the wind energy sector. Based on these insights, we propose energy policy recommendations aimed at gradually minimizing direct government funding in the Russian renewable energy market. This strategy is designed to foster self-sufficiency and growth in the solar energy sector.
Towards a Framework for a New Research Ecosystem
Authors: Roberto Savona, Cristina Maria Alberini, Lucia Alessi, Iacopo Baussano, Petros Dellaportas, Ranieri Guerra, Sean Khozin, Andrea Modena, Sergio Pecorelli, Guido Rasi, Paolo Daniele Siviero, Roger M. Stein
Date: 2023-12-12 00:00:00

A major gap exists between the conceptual suggestion of how much a nation should invest in science, innovation, and technology, and the practical implementation of what is done. We identify 4 critical challenges that must be address in order to develop an environment conducive to collaboration across organizations and governments, while also preserving commercial rewards for investors and innovators, in order to move towards a new Research Ecosystem.
The behavioral intention to adopt Proptech services in Vietnam real estate market
Authors: Le Tung Bach
Date: 2023-12-12 00:00:00
Tasks: Marketing

One of the main stages for achieving success is the adoption of new technology by its users. Several studies show that Property technology is advantageous for real estate stakeholders. Hence, the purpose of this paper is to investigate the users' engagement behavior to adopt Property technology in the Vietnamese real estate market. To that end, a purposive sample of 142 participants was recruited to complete an online quantitative approach based survey. The survey consisted of a modified and previously validated measure of acceptance based on the extended demographic version of the unified theory of acceptance and use of technology), as well as usage scale items. The empirical findings confirm that participants were generally accepting of Property technology in the Vietnamese real estate market. The highest mean values were associated with the subscales of effort expectancy and performance expectancy, while we can easily identify the lowest mean value in the social influence subscale. The usage of Property technology was slightly more concerned with the gathering of information on properties and markets than transactions or portfolio management. This study provides an in depth understanding of Property technology for firms' managers and marketers. Online social interactions might be either harmful or fruitful for firms depending on the type of interaction and engagement behavior. This is especially true of property portals and social media forums that would help investors to connect, communicate and learn. Findings can help users to improve their strategies for digital marketing. By providing robust findings by addressing issues like omitted variables and endogeneity, the findings of this study are promising for developing new hypotheses and theoretical models in the context of the Vietnamese real estate market.
Serious errors impair an assessment of forest carbon projects: A rebuttal of West et al. (2023)
Authors: Edward T. A. Mitchard, Harry Carstairs, Riccardo Cosenza, Sassan S. Saatchi, Jason Funk, Paula Nieto Quintano, Thom Brade, Iain M. McNicol, Patrick Meir, Murray B. Collins, Eric Nowak
Date: 2023-12-11 00:00:00
Tasks: counterfactual

Independent retrospective analyses of the effectiveness of reducing deforestation and forest degradation (REDD) projects are vital to ensure climate change benefits are being delivered. A recent study in Science by West et al. (1) appeared therefore to be a timely alert that the majority of projects operating in the 2010s failed to reduce deforestation rates. Unfortunately, their analysis suffered from major flaws in the choice of underlying data, resulting in poorly matched and unstable counterfactual scenarios. These were compounded by calculation errors, biasing the study against finding that projects significantly reduced deforestation. This flawed analysis of 24 projects unfairly condemned all 100+ REDD projects, and risks cutting off finance for protecting vulnerable tropical forests from destruction at a time when funding needs to grow rapidly.
An Inquiry Into The Economic Linkages Between The Swedish Air Transport Sector And The Economy As A Whole In The Context Of The Covid-19 Pandemic
Authors: Rafael Andersson Lipcsey
Date: 2023-12-09 00:00:00

This thesis aims to assess the importance of the air transport sector for Sweden's economy in the context of the COVID-19 pandemic. Two complementary research goals are formulated. Firstly, investigating economic linkages of the Swedish air transport sector. Secondly, estimating the effects of the pandemic on Swedish air transport, and the spin-off effects on the economy as a whole. Overview of literature in the field reveals that while a fair amount of research exists on the importance of air transport, unsurprisingly, pandemic effects have yet to be investigated. The methodological framework chosen is input-output analysis, thus, the backbone of data materials used is the Swedish input-output table, complemented by additional datasets. For measuring economic linkages, basic input-output analysis is applied. Meanwhile, to capture the pandemic's impacts, a combination of inoperability analysis and the partial hypothetical extraction model is implemented. It is found that while Swedish air transport plays an important role in turning the cogs of the Swedish economy, when compared to all other sectors, it ranks on the lower end. Furthermore, while the COVID-19 pandemic has a detrimental short-term impact on Swedish air transport, the spin-off effects for the economy as a whole are milder. It is concluded, that out of a value-added perspective, the Swedish government, and by extension policy makers globally need not support failing airlines, and other air transport actors. Nonetheless, some aspects could not be captured through the methods used, such as the possible importance of airlines to national security at times of dodgy global cooperation. Lastly, to accurately capture not only short-term, but also long-term effects of the pandemic, future research using alternative, causality-based frameworks and dynamic input-output models will be highly beneficial.
The Transformative Effects of AI on International Economics
Authors: Rafael Andersson Lipcsey
Date: 2023-12-09 00:00:00

As AI adoption accelerates, research on its economic impacts becomes a salient source to consider for stakeholders of AI policy. Such research is however still in its infancy, and one in need of review. This paper aims to accomplish just that and is structured around two main themes. Firstly, the path towards transformative AI, and secondly the wealth created by it. It is found that sectors most embedded into global value chains will drive economic impacts, hence special attention is paid to the international trade perspective. When it comes to the path towards transformative AI, research is heterogenous in its predictions, with some predicting rapid, unhindered adoption, and others taking a more conservative view based on potential bottlenecks and comparisons to past disruptive technologies. As for wealth creation, while some agreement is to be found in AI's growth boosting abilities, predictions on timelines are lacking. Consensus exists however around the dispersion of AI induced wealth, which is heavily biased towards developed countries due to phenomena such as anchoring and reduced bargaining power of developing countries. Finally, a shortcoming of economic growth models in failing to consider AI risk is discovered. Based on the review, a calculated, and slower adoption rate of AI technologies is recommended.
Deep Hierarchical Video Compression
Authors: Ming Lu, Zhihao Duan, Fengqing Zhu, Zhan Ma
Date: 2023-12-12 00:00:00
Tasks: Video Compression

Recently, probabilistic predictive coding that directly models the conditional distribution of latent features across successive frames for temporal redundancy removal has yielded promising results. Existing methods using a single-scale Variational AutoEncoder (VAE) must devise complex networks for conditional probability estimation in latent space, neglecting multiscale characteristics of video frames. Instead, this work proposes hierarchical probabilistic predictive coding, for which hierarchal VAEs are carefully designed to characterize multiscale latent features as a family of flexible priors and posteriors to predict the probabilities of future frames. Under such a hierarchical structure, lightweight networks are sufficient for prediction. The proposed method outperforms representative learned video compression models on common testing videos and demonstrates computational friendliness with much less memory footprint and faster encoding/decoding. Extensive experiments on adaptation to temporal patterns also indicate the better generalization of our hierarchical predictive mechanism. Furthermore, our solution is the first to enable progressive decoding that is favored in networked video applications with packet loss.
Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging
Authors: Yo-Yu Lai, Chia-Hsiang Lin, Zi-Chao Leng
Date: 2023-12-12 00:00:00
Tasks: Denoising, Image Restoration, Super-Resolution

The deep learning model Transformer has achieved remarkable success in the hyperspectral image (HSI) restoration tasks by leveraging Spectral and Spatial Self-Attention (SA) mechanisms. However, applying these designs to remote sensing (RS) HSI restoration tasks, which involve far more spectrums than typical HSI (e.g., ICVL dataset with 31 bands), presents challenges due to the enormous computational complexity of using Spectral and Spatial SA mechanisms. To address this problem, we proposed Hyper-Restormer, a lightweight and effective Transformer-based architecture for RS HSI restoration. First, we introduce a novel Lightweight Spectral-Spatial (LSS) Transformer Block that utilizes both Spectral and Spatial SA to capture long-range dependencies of input features map. Additionally, we employ a novel Lightweight Locally-enhanced Feed-Forward Network (LLFF) to further enhance local context information. Then, LSS Transformer Blocks construct a Single-stage Lightweight Spectral-Spatial Transformer (SLSST) that cleverly utilizes the low-rank property of RS HSI to decompose the feature maps into basis and abundance components, enabling Spectral and Spatial SA with low computational cost. Finally, the proposed Hyper-Restormer cascades several SLSSTs in a stepwise manner to progressively enhance the quality of RS HSI restoration from coarse to fine. Extensive experiments were conducted on various RS HSI restoration tasks, including denoising, inpainting, and super-resolution, demonstrating that the proposed Hyper-Restormer outperforms other state-of-the-art methods.
Robust Target Detection of Intelligent Integrated Optical Camera and mmWave Radar System
Authors: Chen Zhu, Zhouxiang Zhao, Zejing Shan, Lijie Yang, Sijie Ji, Zhaohui Yang, Zhaoyang Zhang
Date: 2023-12-12 00:00:00

Target detection is pivotal for modern urban computing applications. While image-based techniques are widely adopted, they falter under challenging environmental conditions such as adverse weather, poor lighting, and occlusion. To improve the target detection performance under complex real-world scenarios, this paper proposes an intelligent integrated optical camera and millimeter-wave (mmWave) radar system. Utilizing both physical knowledge and data-driven methods, a long-term robust radar-camera fusion algorithm is proposed to solve the heterogeneous data fusion problem for detection improvement. For the occlusion scenarios, the proposed algorithm can effectively detect occluded targets with the help of memory through performing long-term detection. For dark scenarios with low-light conditions, the proposed algorithm can effectively mark the target in the dark picture as well as provide rough stickman imaging. The above two innovative functions of the hybrid optical camera and mmWave radar system are tested in real-world scenarios. The results demonstrate the robustness and significant enhancement in the target detection performance of our integrated system.
Breast Cancer Detection Using Deep Learning Technique Based On Ultrasound Image
Authors: Abdulqader Mohammed, Mohammed Abdel Razek, Mohamed El-dosuky, Ahmed Sobhi
Date: 2023-12-04 00:00:00
Tasks: Breast Cancer Detection, Deep Learning, Image Segmentation, Position, Semantic Segmentation

Breast cancer ranks as the most prevalent form of cancer diagnosed in women, and diagnosis faces several challenges, a change in the size, shape and appearance of breasts, dense breast tissue, lumps or thickening in the breast especially if in only one breast, lumps and nodules in the breast. The major challenge that faces deep learning diagnosis of breast cancer was its shape, size and position non-uniformity especially malignant cancer. This work proposed a deep learning system that increased the accuracy of classification of breast cancer types from ultrasound images. It reaches 99.29% accuracy, exceeding other previous work. First, image processing was applied to in enhance the quality of input images. Second, the image segmentation was performed using U-Net architecture. Third, many features are extracted using Mobilenet. Finally, the accuracy of proposed system was evaluated.