pwc-archive/papers-with-abstracts huggingface dataset:
LncRNA-disease association prediction method based on heterogeneous information completion and convolutional neural network
Authors: Wen-Yu Xi, Juan Wang, Yu-lin Zhang, Jin-Xing Liu, Yin-Lian Gao
Date: 2024-06-02 00:00:00

The emerging research shows that lncRNA has crucial research value in a series of complex human diseases. Therefore, the accurate identification of lncRNA-disease associations (LDAs) is very important for the warning and treatment of diseases. However, most of the existing methods have limitations in identifying nonlinear LDAs, and it remains a huge challenge to predict new LDAs. In this paper, a deep learning model based on a heterogeneous network and convolutional neural network (CNN) is proposed for lncRNA-disease association prediction, named HCNNLDA. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair is constructed according to various biological premises about lncRNAs, diseases, and miRNAs. Then, the low-dimensional feature representation is fully learned by the convolutional neural network. In the end, the XGBoot classifier model is trained to predict the potential LDAs. HCNNLDA obtains a high AUC value of 0.9752 and AUPR of 0.9740 under the 5-fold cross-validation. The experimental results show that the proposed model has better performance than that of several latest prediction models. Meanwhile, the effectiveness of HCNNLDA in identifying novel LDAs is further demonstrated by case studies of three diseases. To sum up, HCNNLDA is a feasible calculation model to predict LDAs.
Equivariant amortized inference of poses for cryo-EM
Authors: Larissa de Ruijter, Gabriele Cesa
Date: 2024-06-01 00:00:00
Tasks: Pose Estimation

Cryo-EM is a vital technique for determining 3D structure of biological molecules such as proteins and viruses. The cryo-EM reconstruction problem is challenging due to the high noise levels, the missing poses of particles, and the computational demands of processing large datasets. A promising solution to these challenges lies in the use of amortized inference methods, which have shown particular efficacy in pose estimation for large datasets. However, these methods also encounter convergence issues, often necessitating sophisticated initialization strategies or engineered solutions for effective convergence. Building upon the existing cryoAI pipeline, which employs a symmetric loss function to address convergence problems, this work explores the emergence and persistence of these issues within the pipeline. Additionally, we explore the impact of equivariant amortized inference on enhancing convergence. Our investigations reveal that, when applied to simulated data, a pipeline incorporating an equivariant encoder not only converges faster and more frequently than the standard approach but also demonstrates superior performance in terms of pose estimation accuracy and the resolution of the reconstructed volume. Notably, $D_4$-equivariant encoders make the symmetric loss superfluous and, therefore, allow for a more efficient reconstruction pipeline.
Focal Loss Analysis of Peripapillary Nerve Fiber Layer Reflectance for Glaucoma Diagnosis
Authors: Ou Tan, Dongseok Choi, Aiyin Chen, David S. Greenfield, Brian A. Francis, Rohit Varma, Joel S. Schuman, David Huang, Advanced Imaging for Glaucoma Study Group
Date: 2024-05-31 00:00:00
Tasks: Diagnostic, Specificity, Superpixels

Purpose: To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis using a large dataset. Methods: Participants were imaged with 4.9mm ONH scans using spectral-domain optical coherence tomography (OCT). The NFL reflectance map was reconstructed from 13 concentric rings of optic nerve head(ONH) scan, then processed by an azimuthal filter to reduce directional reflectance bias due to variation of beam incidence angle. The peripapillary thickness and reflectance maps were both divided into 96 superpixels. Low-reflectance and low-thickness superpixels were defined as values below the 5th percentile normative reference for that location. Focal reflectance loss was measured by summing loss, relative to the normal reference average, in low-reflectance superpixels. Focal thickness loss was calculated in a similar fashion. The area under receiving characteristic curve (AROC) was used to assess diagnostic accuracy. Results: Fifty-three normal, 196 pre-perimetric, 132 early perimetric, and 59 moderate and advanced perimetric glaucoma participants were included from the Advanced Imaging for Glaucoma Study. Sixty-seven percent of glaucomatous reflectance maps showed characteristic contiguous wedge or diffuse defects. Focal NFL reflectance loss had significantly higher diagnostic accuracy than the best NFL thickness parameters (both map-based and profile-based): AROC 0.80 v. 0.75 (p<0.004) for distinguishing glaucoma eyes from healthy control eyes. The diagnostic sensitivity was also significantly higher at both 99% and 95% specificity operating points. Conclusions: Focal NFL reflectance loss improved glaucoma diagnostic accuracy compared to the standard NFL thickness parameters.
Reliability for Nerve Fiber Layer Reflectance Using Spectral Domain Optical Coherence Tomography
Authors: Kabir Hossain, Ou Tan, Po-Han Yeh, Jie Wang, Elizabeth White, Dongseok Choi, David Huang
Date: 2024-05-31 00:00:00
Tasks: Diagnostic

Purpose: Reliability for Nerve Fiber Layer Reflectance Using Spectral Domain Optical Coherence Tomography (OCT) Methods: The study utilized OCT to scan participants with a cubic 6x6 mm disc scan. NFL reflectance were normalized by the average of bands below NFL and summarized. We selected several reference bands, including the pigment epithelium complex (PPEC), the band between NFL and Bruch's membrane (Post-NFL), and the top 50% of pixels with higher values were selected from the Post-NFL band by Post-NFL-Bright. Especially, we also included NFL attenuation coefficient (AC), which was equivalent to NFL reflectance normalized by all pixels below NFL. An experiment was designed to test the NFL reflectance against different levels of attenuation using neutral density filter (NDF). We also evaluated the within-visit and between-visit repeatability using a clinical dataset with normal and glaucoma eyes. Results: The experiment enrolled 20 healthy participants. The clinical dataset selected 22 normal and 55 glaucoma eyes with at least two visits form functional and structural OCT (FSOCT) study. The experiment showed that NFL reflectance normalized PPEC Max and Post-NFL-Bright had lowest dependence, slope=-0.77 and -1.34 dB/optical density on NDF levels, respectively. The clinical data showed that the NFL reflectance metrics normalized by Post-NFL-Bright or Post-NFL-Mean metrics had a trend of better repeatability and reproducibility than others, but the trend was not significant. All metrics demonstrated similar diagnostic accuracy (0.82-0.87), but Post-NFL-Bright provide the best result. Conclusions: The NFL reflectance normalized by the maximum in PPEC had less dependence of the global attenuation followed by Post-NFL-Bright, PPEC/Mean, Post-NFL-Mean and NFL/AC. But NFL reflectance normalized by Post-NFL-Bright had better result in two datasets.
ADEP: A Novel Approach Based on Discriminator-Enhanced Encoder-Decoder Architecture for Accurate Prediction of Adverse Effects in Polypharmacy
Authors: Katayoun Kobraei, Mehrdad Baradaran, Seyed Mohsen Sadeghi, Raziyeh Masumshah, Changiz Eslahchi
Date: 2024-05-31 00:00:00
Tasks: Decoder

Motivation: Unanticipated drug-drug interactions (DDIs) pose significant risks in polypharmacy, emphasizing the need for predictive methods. Recent advancements in computational techniques aim to address this challenge. Methods: We introduce ADEP, a novel approach integrating a discriminator and an encoder-decoder model to address data sparsity and enhance feature extraction. ADEP employs a three-part model, including multiple classification methods, to predict adverse effects in polypharmacy. Results: Evaluation on benchmark datasets shows ADEP outperforms well-known methods such as GGI-DDI, SSF-DDI, LSFC, DPSP, GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, Random Forest, K-Nearest-Neighbor, Logistic Regression, and Decision Tree. Key metrics include Accuracy, AUROC, AUPRC, F-score, Recall, Precision, False Negatives, and False Positives. ADEP achieves more accurate predictions of adverse effects in polypharmacy. A case study with real-world data illustrates ADEP's practical application in identifying potential DDIs and preventing adverse effects. Conclusions: ADEP significantly advances the prediction of polypharmacy adverse effects, offering improved accuracy and reliability. Its innovative architecture enhances feature extraction from sparse medical data, improving medication safety and patient outcomes. Availability: Source code and datasets are available at https://github.com/m0hssn/ADEP.
Complex hemodynamic responses to trans-vascular electrical stimulation of the renal nerve in anesthetized pigs
Authors: Filippo Agnesi, Lucia Carlucci, Gia Burjanadze, Fabio Bernini, Khatia Gabisonia, John W Osborn, Silvestro Micera, Fabio A. Recchia
Date: 2024-05-29 00:00:00

The objective of this study was to characterize hemodynamic changes during trans-vascular stimulation of the renal nerve and their dependence on stimulation parameters. We employed a stimulation catheter inserted in the right renal artery under fluoroscopic guidance, in pigs. Systolic, diastolic and pulse blood pressure and heart rate were recorded during stimulations delivered at different intravascular sites along the renal artery or while varying stimulation parameters (amplitude, frequency, and pulse width). Blood pressure changes during stimulation displayed a pattern more complex than previously described in literature, with a series of negative and positive peaks over the first two minutes, followed by a steady state elevation during the remainder of the stimulation. Pulse pressure and heart rate only showed transient responses, then they returned to baseline values despite constant stimulation. The amplitude of the evoked hemodynamic response was roughly linearly correlated with stimulation amplitude, frequency, and pulse width.
Localized FDG loss in lung cancer lesions
Authors: Davide Parodi, Edoardo Dighero, Giorgia Biddau, Francesca D'Amico, Matteo Bauckneht, Cecilia Marini, Sara Garbarino, Cristina Campi, Michele Piana, Gianmario Sambuceti
Date: 2024-06-01 00:00:00

Purpose: Analysis of [18F]-Fluorodeoxyglucose (FDG) kinetics in cancer has been most often limited to the evaluation of the average uptake over relatively large volumes. Nevertheless, tumor lesion almost contains inflammatory infiltrates whose cells are characterized by a significant radioactivity washout due to the hydrolysis of FDG-6P catalyzed by glucose-6P phosphatase. The present study aimed to verify whether voxel-wise compartmental analysis of dynamic imaging can identify tumor regions characterized by tracer washout. Materials & Methods: The study included 11 patients with lung cancer submitted to PET/CT imaging for staging purposes. Tumor was defined by drawing a volume of interest loosely surrounding the lesion and considering all inside voxels with standardized uptake value (SUV) >40% of the maximum. After 20 minutes dynamic imaging centered on the heart, eight whole body scans were repeated. Six parametric maps were progressively generated by computing six regression lines that considered all eight frames, the last seven ones, and so on, up to the last three. Results: Progressively delaying the starting point of regression line computation identified a progressive increase in the prevalence of voxels with a negative slope. Conclusions: The voxel-wise parametric maps provided by compartmental analysis permits to identify a measurable volume characterized by radioactivity washout. The spatial localization of this pattern is compatible with the recognized preferential site of inflammatory infiltrates populating the tumor stroma and might improve the power of FDG imaging in monitoring the effectiveness of treatments aimed to empower the host immune response against the cancer.
Pricing and calibration in the 4-factor path-dependent volatility model
Authors: Guido Gazzani, Julien Guyon
Date: 2024-06-04 00:00:00

We consider the path-dependent volatility (PDV) model of Guyon and Lekeufack (2023), where the instantaneous volatility is a linear combination of a weighted sum of past returns and the square root of a weighted sum of past squared returns. We discuss the influence of an additional parameter that unlocks enough volatility on the upside to reproduce the implied volatility smiles of S\&P 500 and VIX options. This PDV model, motivated by empirical studies, comes with computational challenges, especially in relation to VIX options pricing and calibration. We propose an accurate \emph{pathwise} neural network approximation of the VIX which leverages on the Markovianity of the 4-factor version of the model. The VIX is learned pathwise as a function of the Markovian factors and the model parameters. We use this approximation to tackle the joint calibration of S\&P 500 and VIX options, quickly sample VIX paths, and price derivatives that jointly depend on S\&P 500 and VIX. As an interesting aside, we also show that this \emph{time-homogeneous}, low-parametric, Markovian PDV model is able to fit the whole surface of S\&P 500 implied volatilities remarkably well.
Machine Learning Methods for Pricing Financial Derivatives
Authors: Lei Fan, Justin Sirignano
Date: 2024-06-01 00:00:00

Stochastic differential equation (SDE) models are the foundation for pricing and hedging financial derivatives. The drift and volatility functions in SDE models are typically chosen to be algebraic functions with a small number (less than 5) parameters which can be calibrated to market data. A more flexible approach is to use neural networks to model the drift and volatility functions, which provides more degrees-of-freedom to match observed market data. Training of models requires optimizing over an SDE, which is computationally challenging. For European options, we develop a fast stochastic gradient descent (SGD) algorithm for training the neural network-SDE model. Our SGD algorithm uses two independent SDE paths to obtain an unbiased estimate of the direction of steepest descent. For American options, we optimize over the corresponding Kolmogorov partial differential equation (PDE). The neural network appears as coefficient functions in the PDE. Models are trained on large datasets (many contracts), requiring either large simulations (many Monte Carlo samples for the stock price paths) or large numbers of PDEs (a PDE must be solved for each contract). Numerical results are presented for real market data including S&P 500 index options, S&P 100 index options, and single-stock American options. The neural-network-based SDE models are compared against the Black-Scholes model, the Dupire's local volatility model, and the Heston model. Models are evaluated in terms of how accurate they are at pricing out-of-sample financial derivatives, which is a core task in derivative pricing at financial institutions.
A Geometric Approach To Asset Allocation With Investor Views
Authors: Alexandre V. Antonov, Koushik Balasubramanian, Alexander Lipton, Marcos Lopez de Prado
Date: 2024-06-03 00:00:00

In this article, a geometric approach to incorporating investor views in portfolio construction is presented. In particular, the proposed approach utilizes the notion of generalized Wasserstein barycenter (GWB) to combine the statistical information about asset returns with investor views to obtain an updated estimate of the asset drifts and covariance, which are then fed into a mean-variance optimizer as inputs. Quantitative comparisons of the proposed geometric approach with the conventional Black-Litterman model (and a closely related variant) are presented. The proposed geometric approach provides investors with more flexibility in specifying their confidence in their views than conventional Black-Litterman model-based approaches. The geometric approach also rewards the investors more for making correct decisions than conventional BL based approaches. We provide empirical and theoretical justifications for our claim.
Mean field equilibrium asset pricing model with habit formation
Authors: Masaaki Fujii, Masashi Sekine
Date: 2024-06-04 00:00:00

This paper presents an asset pricing model in an incomplete market involving a large number of heterogeneous agents based on the mean field game theory. In the model, we incorporate habit formation in consumption preferences, which has been widely used to explain various phenomena in financial economics. In order to characterize the market-clearing equilibrium, we derive a quadratic-growth mean field backward stochastic differential equation (BSDE) and study its well-posedness and asymptotic behavior in the large population limit. Additionally, we introduce an exponential quadratic Gaussian reformulation of the asset pricing model, in which the solution is obtained in a semi-analytic form.
Simulation-based approach for Multiproject Scheduling based on composite priority rules
Authors: Pablo Alvarez-Campana, Felix Villafanez, Fernando Acebes, David Poza
Date: 2024-06-04 00:00:00
Tasks: Management, Scheduling

This paper presents a simulation approach to enhance the performance of heuristics for multi-project scheduling. Unlike other heuristics available in the literature that use only one priority criterion for resource allocation, this paper proposes a structured way to sequentially apply more than one priority criterion for this purpose. By means of simulation, different feasible schedules are obtained to, therefore, increase the probability of finding the schedule with the shortest duration. The performance of this simulation approach was validated with the MPSPLib library, one of the most prominent libraries for resource-constrained multi-project scheduling. These results highlight the proposed method as a useful option for addressing limited time and resources in portfolio management.
Stochastic Earned Value Analysis using Monte Carlo Simulation and Statistical Learning Techniques
Authors: Fernando Acebes, M Pereda, David Poza, Javier Pajares, Jose M Galan
Date: 2024-05-31 00:00:00
Tasks: Anomaly Detection

The aim of this paper is to describe a new an integrated methodology for project control under uncertainty. This proposal is based on Earned Value Methodology and risk analysis and presents several refinements to previous methodologies. More specifically, the approach uses extensive Monte Carlo simulation to obtain information about the expected behavior of the project. This dataset is exploited in several ways using different statistical learning methodologies in a structured fashion. Initially, simulations are used to detect if project deviations are a consequence of the expected variability using Anomaly Detection algorithms. If the project follows this expected variability, probabilities of success in cost and time and expected cost and total duration of the project can be estimated using classification and regression approaches.
Project Risk Management from the bottom-up: Activity Risk Index
Authors: Fernando Acebes, Javier Pajares, Jose M Gonzalez-Varona, Adolfo Lopez-Paredes
Date: 2024-05-31 00:00:00
Tasks: Management, Sensitivity

Project managers need to manage risks throughout the project lifecycle and, thus, need to know how changes in activity durations influence project duration and risk. We propose a new indicator (the Activity Risk Index, ARI) that measures the contribution of each activity to the total project risk while it is underway. In particular, the indicator informs us about what activities contribute the most to the project's uncertainty so that project managers can pay closer attention to the performance of these activities. The main difference between our indicator and other activity sensitivity metrics in the literature (e.g. cruciality, criticality, significance, or schedule sensitivity indices) is that our indicator is based on the Schedule Risk Baseline concept instead of on cost or schedule baselines. The new metric not only provides information at the beginning of the project, but also while it is underway. Furthermore, the ARI is the only one to offer a normalized result: if we add its value for each activity, the total sum is 100%.
Loss-Versus-Fair: Efficiency of Dutch Auctions on Blockchains
Authors: Ciamac C. Moallemi, Dan Robinson
Date: 2024-05-31 00:00:00

Milionis et al.(2023) studied the rate at which automated market makers leak value to arbitrageurs when block times are discrete and follow a Poisson process, and where the risky asset price follows a geometric Brownian motion. We extend their model to analyze another popular mechanism in decentralized finance for onchain trading: Dutch auctions. We compute the expected losses that a seller incurs to arbitrageurs and expected time-to-fill for Dutch auctions as a function of starting price, volatility, decay rate, and average interblock time. We also extend the analysis to gradual Dutch auctions, a variation on Dutch auctions for selling tokens over time at a continuous rate. We use these models to explore the tradeoff between speed of execution and quality of execution, which could help inform practitioners in setting parameters for starting price and decay rate on Dutch auctions, or help platform designers determine performance parameters like block times.
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation
Authors: Yinjun Wu, Mayank Keoliya, Kan Chen, Neelay Velingker, Ziyang Li, Emily J Getzen, Qi Long, Mayur Naik, Ravi B Parikh, Eric Wong
Date: 2024-06-02 00:00:00

Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as healthcare should ideally be (i) accurate, and (ii) provide faithful explanations. However, current solutions are inadequate: state-of-the-art black-box models do not supply explanations, post-hoc explainers for black-box models lack faithfulness guarantees, and self-interpretable models greatly compromise accuracy. To address these issues, we propose DISCRET, a self-interpretable ITE framework that synthesizes faithful, rule-based explanations for each sample. A key insight behind DISCRET is that explanations can serve dually as database queries to identify similar subgroups of samples. We provide a novel RL algorithm to efficiently synthesize these explanations from a large search space. We evaluate DISCRET on diverse tasks involving tabular, image, and text data. DISCRET outperforms the best self-interpretable models and has accuracy comparable to the best black-box models while providing faithful explanations. DISCRET is available at https://github.com/wuyinjun-1993/DISCRET-ICML2024.