pwc-archive/papers-with-abstracts huggingface dataset:
Balancing property optimization and constraint satisfaction for constrained multi-property molecular optimization
Authors: Xin Xia, YaJie Zhang, Xiangxiang Zeng, Xingyi Zhang, ChunHou Zheng, Yansen Su
Date: 2024-11-19 00:00:00

Molecular optimization, which aims to discover improved molecules from a vast chemical search space, is a critical step in chemical development. Various artificial intelligence technologies have demonstrated high effectiveness and efficiency on molecular optimization tasks. However, few of these technologies focus on balancing property optimization with constraint satisfaction, making it difficult to obtain high-quality molecules that not only possess desirable properties but also meet various constraints. To address this issue, we propose a constrained multi-property molecular optimization framework (CMOMO), which is a flexible and efficient method to simultaneously optimize multiple molecular properties while satisfying several drug-like constraints. CMOMO improves multiple properties of molecules with constraints based on dynamic cooperative optimization, which dynamically handles the constraints across various scenarios. Besides, CMOMO evaluates multiple properties within discrete chemical spaces cooperatively with the evolution of molecules within an implicit molecular space to guide the evolutionary search. Experimental results show the superior performance of the proposed CMOMO over five state-of-the-art molecular optimization methods on two benchmark tasks of simultaneously optimizing multiple non-biological activity properties while satisfying two structural constraints. Furthermore, the practical applicability of CMOMO is verified on two practical tasks, where it identified a collection of candidate ligands of $\beta$2-adrenoceptor GPCR and candidate inhibitors of glycogen synthase kinase-3$\beta$ with high properties and under drug-like constraints.
Daniel Chukuma
Authors: Daniel Chukuma
Date: 2024-11-26 00:00:00
Proceeding: 26/11 2024 11
Tasks: Marketing

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Automated Registration of 3D Neurovascular Territory Atlas to 2D DSA for Targeted Quantitative Angiography Analysis
Authors: George Dimopoulos, Sabrina De Los Angeles Reverol Parra, Parmita Mondal, Michael Udin, Kyle Williams, Parisa Naghdi, Ahmad Rahmatpour, Swetadri Vasan Setlur Nagesh, Mohammad Mahdi Shiraz Bhurwani, Jason Davies, Ciprian N. Ionita
Date: 2024-11-25 00:00:00
Tasks: Diagnostic, SSIM

Subarachnoid hemorrhage (SAH), typically due to intracranial aneurysms, demands precise imaging for effective treatment. Digital Subtraction Angiography (DSA), despite being the gold standard, broadly visualizes cerebral blood flow, potentially masking key details in areas. This study introduces an approach integrating a 3D vascular atlas with 2D DSA images to allow targeted quantitative analysis in these crucial regions, thus enhancing diagnostic accuracy during interventions. Initially, DSA data was examined to ascertain the injection site. Following this, the appropriate viewing angle was determined to align accurately with the 3D vascular atlas. Utilizing this atlas, regions corresponding to the areas indicated as perfused were selected. Concurrently, a mask representing the perfused areas was created from the DSA sequence. This mask facilitated the initial coarse alignment of the projected 3D atlas to the DSA perfused territory deformable registration techniques, ensuring a precise overlay with the DSAs perfused territories. The performance of each overlay was measured using the Structural Similarity Index Measure (SSIM). The coregistration process revealed that deformable registrations was essential to achieve precise overlays of the 3D atlas projections with the 2D DSA perfused areas. This approach enabled the extraction of targeted quantitative angiography parameters, essential for detailed vascular assessment in subarachnoid hemorrhage cases. The integration of 3D atlas registration with 2D DSA projections facilitates a more precise and targeted diagnostic process for SAH during critical interventions. This image processing strategy enhances the visualization of affected arterial territories, potentially improving the accuracy of diagnostics and supporting better informed clinical decisions at the time of intervention
Coherence Based Sound Speed Aberration Correction -- with clinical validation in fetal ultrasound
Authors: Anders Emil Vrålstad, Peter Fosodeder, Karin Ulrike Deibele, Siri Ann Nyrnes, Ole Marius Hoel Rindal, Vibeke Skoura-Torvik, Martin Mienkina, Svein-Erik Måsøy
Date: 2024-11-25 00:00:00

The purpose of this work is to demonstrate a robust and clinically validated method for correcting sound speed aberrations in medical ultrasound. We propose a correction method that calculates focusing delays directly from the observed two-way distributed average sound speed. The method beamforms multiple coherence images and selects the sound speed that maximizes the coherence for each image pixel. The main contribution of this work is the direct estimation of aberration, without the ill-posed inversion of a local sound speed map, and the proposed processing of coherence images which adapts to in vivo situations where low coherent regions and off-axis scattering represents a challenge. The method is validated in vitro and in silico showing high correlation with ground truth speed of sound maps. Further, the method is clinically validated by being applied to channel data recorded from 172 obstetric Bmode images, and 12 case examples are presented and discussed in detail. The data is recorded with a GE HealthCare Voluson Expert 22 system with an eM6c matrix array probe. The images are evaluated by three expert clinicians, and the results show that the corrected images are preferred or gave equivalent quality to no correction (1540m/s) for 72.5% of the 172 images. In addition, a sharpness metric from digital photography is used to quantify image quality improvement. The increase in sharpness and the change in average sound speed are shown to be linearly correlated with a Pearson Correlation Coefficient of 0.67.
ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction
Authors: Yuyang Hu, Albert Peng, Weijie Gan, Ulugbek S. Kamilov
Date: 2024-11-25 00:00:00
Tasks: MRI Reconstruction

Diffusion bridges (DB) have emerged as a promising alternative to diffusion models for imaging inverse problems, achieving faster sampling by directly bridging low- and high-quality image distributions. While incorporating measurement consistency has been shown to improve performance, existing DB methods fail to maintain this consistency in blind inverse problems, where the forward model is unknown. To address this limitation, we introduce ADOBI (Adaptive Diffusion Bridge for Inverse Problems), a novel framework that adaptively calibrates the unknown forward model to enforce measurement consistency throughout sampling iterations. Our adaptation strategy allows ADOBI to achieve high-quality parallel magnetic resonance imaging (PMRI) reconstruction in only 5-10 steps. Our numerical results show that ADOBI consistently delivers state-of-the-art performance, and further advances the Pareto frontier for the perception-distortion trade-off.
A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports
Authors: Gabriel Okasa, Alberto de León, Michaela Strinzel, Anne Jorstad, Katrin Milzow, Matthias Egger, Stefan Müller
Date: 2024-11-25 00:00:00
Tasks: text-classification, Text Classification

Peer review in grant evaluation informs funding decisions, but the contents of peer review reports are rarely analyzed. In this work, we develop a thoroughly tested pipeline to analyze the texts of grant peer review reports using methods from applied Natural Language Processing (NLP) and machine learning. We start by developing twelve categories reflecting content of grant peer review reports that are of interest to research funders. This is followed by multiple human annotators' iterative annotation of these categories in a novel text corpus of grant peer review reports submitted to the Swiss National Science Foundation. After validating the human annotation, we use the annotated texts to fine-tune pre-trained transformer models to classify these categories at scale, while conducting several robustness and validation checks. Our results show that many categories can be reliably identified by human annotators and machine learning approaches. However, the choice of text classification approach considerably influences the classification performance. We also find a high correspondence between out-of-sample classification performance and human annotators' perceived difficulty in identifying categories. Our results and publicly available fine-tuned transformer models will allow researchers and research funders and anybody interested in peer review to examine and report on the contents of these reports in a structured manner. Ultimately, we hope our approach can contribute to ensuring the quality and trustworthiness of grant peer review.
What events matter for exchange rate volatility ?
Authors: Igor Martins, Hedibert Freitas Lopes
Date: 2024-11-25 00:00:00

This paper expands on stochastic volatility models by proposing a data-driven method to select the macroeconomic events most likely to impact volatility. The paper identifies and quantifies the effects of macroeconomic events across multiple countries on exchange rate volatility using high-frequency currency returns, while accounting for persistent stochastic volatility effects and seasonal components capturing time-of-day patterns. Given the hundreds of macroeconomic announcements and their lags, we rely on sparsity-based methods to select relevant events for the model. We contribute to the exchange rate literature in four ways: First, we identify the macroeconomic events that drive currency volatility, estimate their effects and connect them to macroeconomic fundamentals. Second, we find a link between intraday seasonality, trading volume, and the opening hours of major markets across the globe. We provide a simple labor-based explanation for this observed pattern. Third, we show that including macroeconomic events and seasonal components is crucial for forecasting exchange rate volatility. Fourth, our proposed model yields the lowest volatility and highest Sharpe ratio in portfolio allocations when compared to standard SV and GARCH models.
Utilization and Profitability of Tractor Services for Maize Farming in Ejura-Sekyedumase Municipality, Ghana
Authors: Fred Nimoh, Innocent Yao Yevu, Attah-Nyame Essampong, Asante Emmanuel Addo, Addai Kevin
Date: 2024-11-24 00:00:00
Tasks: Descriptive

Maize farming is a major livelihood activity for many farmers in Ghana. Unfortunately, farmers usually do not obtain the expected returns on their investment due to reliance on rudimentary, labor-intensive, and inefficient methods of production. Using cross-sectional data from 359 maize farmers, this study investigates the profitability and determinants of the use of tractor services for maize production in Ejura-Sekyedumase, Ashanti Region of Ghana. Results from descriptive and profitability analyses reveal that tractor services such as ploughing and shelling are widely used, but their profitability varies significantly among farmers. Key factors influencing profitability include farm size, fertilizer quantity applied, and farmer experience. Results from a multivariate probit analysis also showed that farming experience, fertilizer quantity, and profit per acre have a positive influence on tractor service use for shelling, while household size, farm size, and FBO have a negative influence. Farming experience, fertilizer quantity, and profit per acre positively influence tractor service use for ploughing, while farm size has a negative influence. A t-test result reveals a statistically significant difference in profit between farmers who use tractor services and those who do not. Specifically, farmers who utilize tractor services on their maize farm had a return to cost of 9 percent more than those who do not (p-value < 0.05). The Kendall's result showed a moderate agreement among the maize farmers (first ranked being financial issues) in their ability to access/utilize tractor services on their farm.
From Replications to Revelations: Heteroskedasticity-Robust Inference
Authors: Sebastian Kranz
Date: 2024-11-22 00:00:00

Analysing the Stata regression commands from 4,420 reproduction packages of leading economic journals, we find that, among the 40,571 regressions specifying heteroskedasticity-robust standard errors, 98.1% adhere to Stata's default HC1 specification. We then compare several heteroskedasticity-robust inference methods with a large-scale Monte Carlo study based on regressions from 155 reproduction packages. Our results show that t-tests based on HC1 or HC2 with default degrees of freedom exhibit substantial over-rejection. Inference methods with customized degrees of freedom, as proposed by Bell and McCaffrey (2002), Hansen (2024), and a novel approach based on partial leverages, perform best. Additionally, we provide deeper insights into the role of leverages and partial leverages across different inference methods.
FinML-Chain: A Blockchain-Integrated Dataset for Enhanced Financial Machine Learning
Authors: Jingfeng Chen, Wanlin Deng, Dangxing Chen, Luyao Zhang
Date: 2024-11-25 00:00:00
Tasks: Fairness

Machine learning is critical for innovation and efficiency in financial markets, offering predictive models and data-driven decision-making. However, challenges such as missing data, lack of transparency, untimely updates, insecurity, and incompatible data sources limit its effectiveness. Blockchain technology, with its transparency, immutability, and real-time updates, addresses these challenges. We present a framework for integrating high-frequency on-chain data with low-frequency off-chain data, providing a benchmark for addressing novel research questions in economic mechanism design. This framework generates modular, extensible datasets for analyzing economic mechanisms such as the Transaction Fee Mechanism, enabling multi-modal insights and fairness-driven evaluations. Using four machine learning techniques, including linear regression, deep neural networks, XGBoost, and LSTM models, we demonstrate the framework's ability to produce datasets that advance financial research and improve understanding of blockchain-driven systems. Our contributions include: (1) proposing a research scenario for the Transaction Fee Mechanism and demonstrating how the framework addresses previously unexplored questions in economic mechanism design; (2) providing a benchmark for financial machine learning by open-sourcing a sample dataset generated by the framework and the code for the pipeline, enabling continuous dataset expansion; and (3) promoting reproducibility, transparency, and collaboration by fully open-sourcing the framework and its outputs. This initiative supports researchers in extending our work and developing innovative financial machine-learning models, fostering advancements at the intersection of machine learning, blockchain, and economics.
Inter-firm Heterogeneity in Production
Authors: Michele Battisti, Valentino Dardanoni, Stefano Demichelis
Date: 2024-11-24 00:00:00

This paper studies inter-firm heterogeneity in production. Unlike much of the existing research, which primarily addresses heterogeneous production through unobserved fixed effects, our approach also focuses on differences in factors' output elasticities. Using manufacturing data from Chile, Colombia, and Japan, we apply an innovative Empirical Bayes methodology to estimate heterogeneous Cobb-Douglas production functions. We uncover substantial heterogeneity in both factor neutral productivity and factor elasticities, with a strong negative correlation between them. These findings are consistently observed across datasets and remain robust when using CES and intensive Cobb-Douglas specifications. We show that accounting for these features has significant implications for issues such as markup estimation, firms' technology adoption, and productivity measurement.
Can an increase in productivity cause a decrease in production? Insights from a model economy with AI automation
Authors: Casey O. Barkan
Date: 2024-11-24 00:00:00

It is widely assumed that increases in economic productivity necessarily lead to economic growth. In this paper, it is shown that this is not always the case. An idealized model of an economy is presented in which a new technology allows capital to be utilized autonomously without labor input. This is motivated by the possibility that advances in artificial intelligence (AI) will give rise to AI agents that act autonomously in the economy. The economic model involves a single profit-maximizing firm which is a monopolist in the product market and a monopsonist in the labor market. The new automation technology causes the firm to replace labor with capital in such a way that its profit increases while total production decreases. The model is not intended to capture the structure of a real economy, but rather to illustrate how basic economic mechanisms can give rise to counterintuitive and undesirable outcomes.
Trade Wars with Trade Deficits
Authors: Pau Pujolas, Jack Rossbach
Date: 2024-11-22 00:00:00

Trade imbalances significantly alter the welfare implications of tariffs. Using an illustrative model, we show that trade deficits enhance a country's ability to alter its terms of trade, and thereby benefit from tariffs. Greater trade deficits imply higher optimal, or welfare maximizing, tariffs. We compute optimal unilateral and Nash equilibrium tariffs between the United States and China $\unicode{x2014}$ the countries with the largest bilateral trade imbalance $\unicode{x2014}$ using a multi-region, multi-sector applied general equilibrium model with service sectors and input-output linkages, a computationally complex task. Free trade benefits both countries compared to a trade war. Relative to existing tariff rates, however, the United States gains from a trade war with China $\unicode{x2014}$ a result that hinges on their bilateral trade imbalance.
Marginal Reputation
Authors: Daniel Luo, Alexander Wolitzky
Date: 2024-11-22 00:00:00

We study reputation formation where a long-run player repeatedly observes private signals and takes actions. Short-run players observe the long-run player's past actions but not her past signals. The long-run player can thus develop a reputation for playing a distribution over actions, but not necessarily for playing a particular mapping from signals to actions. Nonetheless, we show that the long-run player can secure her Stackelberg payoff if distinct commitment types are statistically distinguishable and the Stackelberg strategy is confound-defeating. This property holds if and only if the Stackelberg strategy is the unique solution to an optimal transport problem. If the long-run player's payoff is supermodular in one-dimensional signals and actions, she secures the Stackelberg payoff if and only if the Stackelberg strategy is monotone. Applications include deterrence, delegation, signaling, and persuasion. Our results extend to the case where distinct commitment types may be indistinguishable but the Stackelberg type is salient under the prior.
Learning Volumetric Neural Deformable Models to Recover 3D Regional Heart Wall Motion from Multi-Planar Tagged MRI
Authors: Meng Ye, Bingyu Xin, Bangwei Guo, Leon Axel, Dimitris Metaxas
Date: 2024-11-21 00:00:00

Multi-planar tagged MRI is the gold standard for regional heart wall motion evaluation. However, accurate recovery of the 3D true heart wall motion from a set of 2D apparent motion cues is challenging, due to incomplete sampling of the true motion and difficulty in information fusion from apparent motion cues observed on multiple imaging planes. To solve these challenges, we introduce a novel class of volumetric neural deformable models ($\upsilon$NDMs). Our $\upsilon$NDMs represent heart wall geometry and motion through a set of low-dimensional global deformation parameter functions and a diffeomorphic point flow regularized local deformation field. To learn such global and local deformation for 2D apparent motion mapping to 3D true motion, we design a hybrid point transformer, which incorporates both point cross-attention and self-attention mechanisms. While use of point cross-attention can learn to fuse 2D apparent motion cues into material point true motion hints, point self-attention hierarchically organised as an encoder-decoder structure can further learn to refine these hints and map them into 3D true motion. We have performed experiments on a large cohort of synthetic 3D regional heart wall motion dataset. The results demonstrated the high accuracy of our method for the recovery of dense 3D true motion from sparse 2D apparent motion cues. Project page is at https://github.com/DeepTag/VolumetricNeuralDeformableModels.
Deep Learning-Based Classification of Hyperkinetic Movement Disorders in Children
Authors: Nandika Ramamurthy, Dr Daniel Lumsden, Dr Rachel Sparks
Date: 2024-11-19 00:00:00
Tasks: Deep Learning, Diagnostic, Specificity

Hyperkinetic movement disorders (HMDs) in children, including dystonia (abnormal twisting) and chorea (irregular, random movements), pose significant diagnostic challenges due to overlapping clinical features. The prevalence of dystonia ranges from 2 to 50 per million, and chorea from 5 to 10 per 100,000. These conditions are often diagnosed with delays averaging 4.75 to 7.83 years. Traditional diagnostic methods depend on clinical history and expert physical examinations, but specialized tests are ineffective due to the complex pathophysiology of these disorders. This study develops a neural network model to differentiate between dystonia and chorea from video recordings of paediatric patients performing motor tasks. The model integrates a Graph Convolutional Network (GCN) to capture spatial relationships and Long Short-Term Memory (LSTM) networks to account for temporal dynamics. Attention mechanisms were incorporated to improve model interpretability. The model was trained and validated on a dataset of 50 videos (31 chorea-predominant, 19 dystonia-predominant) collected under regulatory approval from Guy's and St Thomas' NHS Foundation Trust. The model achieved 85% accuracy, 81% sensitivity, and 88% specificity at 15 frames per second. Attention maps highlighted the model's ability to correctly identify involuntary movement patterns, with misclassifications often due to occluded body parts or subtle movement variations. This work demonstrates the potential of deep learning to improve the accuracy and efficiency of HMD diagnosis and could contribute to more reliable, interpretable clinical tools.