pwc-archive/papers-with-abstracts huggingface dataset:
Predator-Prey Linear Coupling with Hybrid Species
Authors: Jean-Luc Boulnois
Date: 2022-12-29 00:00:00
Tasks: valid

The classical two-species non-linear Predator-Prey system, often used in population dynamics modeling, is expressed in terms of a single positive coupling parameter $\lambda$. Based on standard logarithmic transformations, we derive a novel $\lambda$-\textit{invariant} Hamiltonian resulting in two coupled first-order ODEs for ``hybrid-species'', \textit{albeit} with one being \textit{linear}; we thus derive a new exact, closed-form, single quadrature solution valid for any value of $\lambda$ and the system's energy. In the particular case $\lambda = 1$ the ODE system completely uncouples and a new, exact, energy-only dependent simple quadrature solution is derived. In the case $\lambda \neq 1$ an accurate practical approximation uncoupling the non-linear system is proposed and solutions are provided in terms of explicit quadratures together with high energy asymptotic solutions. A novel, exact, closed-form expression of the system's oscillation period valid for any value of $\lambda$ and orbital energy is also derived; two fundamental properties of the period are established; for $\lambda = 1$ the period is expressed in terms of a universal energy function and shown to be the shortest.
Deterministic and Nondeterministic Particle Motion with Interaction Mechanisms
Authors: Cameron McNamee, Renee Reijo Pera
Date: 2022-12-31 00:00:00
Tasks: Math

Studying systems where many individual bodies in motion interact with one another is a complex and interesting area. Simple mechanisms that may be determined for biological, chemical, or physical reasons can lead to astonishingly complex results that require a further understanding of the moving bodies. With the increasing interaction between computation and various scientific areas, it has become more useful, feasible, and important to create models for these systems. Here, we present two families of models, deterministic and nondeterministic, along with three distinct and realistic interaction mechanisms. These are combined in a unique way to provide the groundwork for particle system models across multiple disciplines. This work has applications that range from biology to chemistry and physics. In addition to describing the motivations and math behind all the models, software is provided that allows researchers to quickly adjust and implement what is described here.
Synthesis-driven design of 3D molecules for structure-based drug discovery using geometric transformers
Authors: Yibo Li, Jianfeng Pei, Luhua Lai
Date: 2022-12-31 00:00:00
Tasks: Drug Design, Drug Discovery

Finding drug-like compounds with high bioactivity is essential for drug discovery, but the task is complicated by the high cost of chemical synthesis and validation. With their outstanding performance in de novo drug design, deep generative models represent promising tools for tackling this challenge. In recently years, 3D molecule generative models have gained increasing attention due to their ability to directly utilize the 3D interaction information between the target and ligand. However, it remains challenging to synthesize the molecules generated by these models, limiting the speed of bioactivity validation and further structure optimization. In this work, we propose DeepLigBuilder+, a deep generative model for 3D molecules that combines structure-based de novo drug design with a reaction-based generation framework. Besides producing 3D molecular structures, the model also proposes synthetic pathways for generated molecules, which greatly assists the retro-synthetic analysis. To achieve this, we developed a new way to enforce the synthesizability constraint using a tree-based organization of purchasable building blocks. This method enjoys high scalability and is compatible with existing atom-based generative models. Additionally, for structure-based design tasks, we developed an SE(3)-equivariant transformer conditioned on the shape and pharmacophore-based inputs, and combine it with the Monte Carlo tree search. Using the ATP-binding pocket of BTK and the NAD+ binding pocket of PHGDH for case studies, we demonstrate that DeepLigBuilder+ is capable of enriching drug-like molecules with high predicted binding affinity and desirable interaction modes while maintaining the synthesizability constraint. We believe that DeepLigBuilder+ is a powerful tool for accelerating the process of drug discovery.
Fast Barrier Option Pricing by the COS BEM Method in Heston Model
Authors: A. Aimi, C. Guardasoni, L. Ortiz-Gracia, S. Sanfelici
Date: 2023-01-02 00:00:00
Tasks: Computational Efficiency, valid

In this work, the Fourier-cosine series (COS) method has been combined with the Boundary Element Method (BEM) for a fast evaluation of barrier option prices. After a description of its use in the Black and Scholes (BS) model, the focus of the paper is on the application of the proposed methodology to the barrier option evaluation in the Heston model, where its contribution is fundamental to improve computational efficiency and to make BEM appealing among Finance practitioners as a valid alternative to Monte Carlo (MC) or other more traditional approaches. An error analysis is provided on the number of terms used in the Fourier-cosine series expansion, where the error bound estimation is based on the characteristic function of the log-asset price process.
Nowcasting Stock Implied Volatility with Twitter
Authors: Thomas Dierckx, Jesse Davis, Wim Schoutens
Date: 2022-12-31 00:00:00

In this study, we predict next-day movements of stock end-of-day implied volatility using random forests. Through an ablation study, we examine the usefulness of different sources of predictors and expose the value of attention and sentiment features extracted from Twitter. We study the approach on a stock universe comprised of the 165 most liquid US stocks diversified across the 11 traditional market sectors using a sizeable out-of-sample period spanning over six years. In doing so, we uncover that stocks in certain sectors, such as Consumer Discretionary, Technology, Real Estate, and Utilities are easier to predict than others. Further analysis shows that possible reasons for these discrepancies might be caused by either excess social media attention or low option liquidity. Lastly, we explore how our proposed approach fares throughout time by identifying four underlying market regimes in implied volatility using hidden Markov models. We find that most added value is achieved in regimes associated with lower implied volatility, but optimal regimes vary per market sector.
Analytic RFR Option Pricing with Smile and Skew
Authors: Colin Turfus, Aurelio Romero-Bermúdez
Date: 2023-01-03 00:00:00

We extend the short rate model of Turfus and Romero-Berm\'udez [2021] to facilitate accurate arbitrage-free analytic pricing of SOFR, SONIA or ESTR caplets, i.e. options on backward-looking compounded rates payments, in a manner consistent with the smile and skew levels observed in the market. These caplet pricing formulae and corresponding LIBOR or term-rate caplet results are translated into effective variance (implied volatility) formulae, which are seen to be of a particularly simple form. They show that the model is essentially equivalent to imposing on a Hull-White model an effective variance which is a quadratic function of the moneyness parameter (rather than a constant) for any given maturity. Results are also illustrated graphically.
Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
Authors: Francisco Mena, Diego Arenas, Marlon Nuske, Andreas Dengel
Date: 2022-12-20 00:00:00
Tasks: Earth Observation, Multimodal Deep Learning, MULTI-VIEW LEARNING, Sensor Fusion

The advances in remote sensing technologies have boosted applications for Earth observation. These technologies provide multiple observations or views with different levels of information. They might contain static or temporary views with different levels of resolution, in addition to having different types and amounts of noise due to sensor calibration or deterioration. A great variety of deep learning models have been applied to fuse the information from these multiple views, known as deep multi-view or multi-modal fusion learning. However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques. This article gathers works on multi-view fusion for Earth observation by focusing on the common practices and approaches used in the literature. We summarize and structure insights from several different publications concentrating on unifying points and ideas. In this manuscript, we provide a harmonized terminology while at the same time mentioning the various alternative terms that are used in literature. The topics covered by the works reviewed focus on supervised learning with the use of neural network models. We hope this review, with a long list of recent references, can support future research and lead to a unified advance in the area.
DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation
Authors: Fang Chen, Heiko Balzter, Feixiang Zhou, Peng Ren, Huiyu Zhou
Date: 2022-12-19 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Successful implementation of oil spill segmentation in Synthetic Aperture Radar (SAR) images is vital for marine environmental protection. In this paper, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images, and the other is the generative module to produce oil spill segmentation maps by drawing the latent feature variables as inputs. Thus, to yield accurate segmentation, we take into account the intrinsic distribution of backscatter values in SAR images and embed it in our segmentation model. The intrinsic distribution originates from SAR imagery, describing the physical characteristics of oil spills. In the training process, the formulated intrinsic distribution guides efficient learning of optimal latent feature variable inference for oil spill segmentation. The efficient learning enables the training of our proposed DGNet with a small amount of image data. This is economically beneficial to oil spill segmentation where the availability of oil spill SAR image data is limited in practice. Additionally, benefiting from optimal latent feature variable inference, our proposed DGNet performs accurate oil spill segmentation. We evaluate the segmentation performance of our proposed DGNet with different metrics, and experimental evaluations demonstrate its effective segmentations.
Speed up the inference of diffusion models via shortcut MCMC sampling
Authors: Gang Chen
Date: 2022-12-18 00:00:00
Tasks: Image Generation

Diffusion probabilistic models have generated high quality image synthesis recently. However, one pain point is the notorious inference to gradually obtain clear images with thousands of steps, which is time consuming compared to other generative models. In this paper, we present a shortcut MCMC sampling algorithm, which balances training and inference, while keeping the generated data's quality. In particular, we add the global fidelity constraint with shortcut MCMC sampling to combat the local fitting from diffusion models. We do some initial experiments and show very promising results. Our implementation is available at https://github.com//vividitytech/diffusion-mcmc.git.
Mask Matching Transformer for Few-Shot Segmentation
Authors: Siyu Jiao, Gengwei Zhang, Shant Navasardyan, Ling Chen, Yao Zhao, Yunchao Wei, Humphrey Shi
Date: 2022-12-05 00:00:00
Tasks: Few-Shot Semantic Segmentation, Segmentation

In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results. However, to obtain satisfactory segments, such a paradigm needs to couple the learning of the matching operations with heavy segmentation modules, limiting the flexibility of design and increasing the learning complexity. To alleviate this issue, we propose Mask Matching Transformer (MM-Former), a new paradigm for the few-shot segmentation task. Specifically, MM-Former first uses a class-agnostic segmenter to decompose the query image into multiple segment proposals. Then, a simple matching mechanism is applied to merge the related segment proposals into the final mask guided by the support images. The advantages of our MM-Former are two-fold. First, the MM-Former follows the paradigm of decompose first and then blend, allowing our method to benefit from the advanced potential objects segmenter to produce high-quality mask proposals for query images. Second, the mission of prototypical features is relaxed to learn coefficients to fuse correct ones within a proposal pool, making the MM-Former be well generalized to complex scenarios or cases. We conduct extensive experiments on the popular COCO-$20^i$ and Pascal-$5^i$ benchmarks. Competitive results well demonstrate the effectiveness and the generalization ability of our MM-Former.
RegTraffic: A Regression Based Traffic Simulator for Spatiotemporal Traffic Modeling, Simulation and Visualization
Authors: Sifatul Mostafi, Taghreed Alghamdi, Khalid Elgazzar
Date: 2022-11-23 00:00:00
Tasks: Management, regression

Traffic simulation is a great tool to demonstrate complex traffic structures which can be extremely useful for the planning, development, and management of road traffic networks. Current traffic simulators offer limited features when it comes to interactive and adaptive traffic modeling. This paper presents RegTraffic, a novel interactive traffic simulator that integrates dynamic regression-based spatiotemporal traffic analysis to predict congestion of intercorrelated road segments. The simulator models traffic congestion of road segments depending on neighboring road links and temporal features of the dynamic traffic flow. The simulator provides a user-friendly web interface to select road segments of interest, receive user-defined traffic parameters, and visualize the traffic for the flow of correlated road links based on the user inputs and the underlying correlation of these road links. Performance evaluation shows that RegTraffic can effectively predict traffic congestion with a Mean Squared Error of 1.3 Km/h and a Root Mean Squared Error of 1.71 Km/h. RegTraffic can effectively simulate the results and provide visualization on interactive geographical maps.
Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI
Authors: Davood Karimi, Lana Vasung, Camilo Jaimes, Fedel Machado-Rivas, Simon K. Warfield, Ali Gholipour
Date: 2021-10-01 00:00:00
Proceeding: NeuroImage 2021 10

Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the diffusion signal or mathematical simplifications, which can impact the estimation accuracy. In this paper, we propose a data-driven method that avoids some of these pitfalls. Our proposed method is based on a multilayer perceptron that learns to map the diffusion-weighted measurements, interpolated onto a fixed spherical grid in the q space, to the target fODF. Importantly, we also propose methods for synthesizing reliable simulated training data. We show that the model can be effectively trained with simulated or real training data. Our phantom experiments show that the proposed method results in more accurate fODF estimation and tractography than several competing methods including the multi-tensor model, Bayesian estimation, spherical deconvolution, and two other machine learning techniques. On real data, we compare our method with other techniques in terms of accuracy of estimating the ground-truth fODF. The results show that our method is more accurate than other methods, and that it performs better than the competing methods when applied to under-sampled diffusion measurements. We also compare our method with the Sparse Fascicle Model in terms of expert ratings of the accuracy of reconstruction of several commissural, projection, association, and cerebellar tracts. The results show that the tracts reconstructed with the proposed method are rated significantly higher by three independent experts. Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.
Multimodal Forgery Detection Using Ensemble Learning
Authors: Ammarah Hashmi, Sahibzada Adil Shahzad, Wasim Ahmad, Chia Wen Lin, Yu Tsao, Hsin-Min Wang
Date: 2022-11-07 00:00:00
Proceeding: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 2022 11
Tasks: Ensemble Learning, Face Swapping, Multimodal Forgery Detection

The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AIsynthesized videos) has become a critical task. The existing systems generally do not fully consider the unified processing of audio and video data, so there is still room for further improvement. In this paper, we focus on the multimodal forgery detection task and propose a deep forgery detection method based on audiovisual ensemble learning. The proposed method consists of four parts, namely a Video Network, an Audio Network, an Audiovisual Network, and a Voting Module. Given a video, the proposed multimodal and ensemble learning system can identify whether it is fake or real. Experimental results on a recently released multimodal FakeAVCeleb dataset show that the proposed method achieves 89% accuracy, significantly outperforming existing models.
Medical Image Segmentation via Cascaded Attention Decoding
Authors: Md Mostafijur Rahman, Radu Marculescu
Date: 2023-01-03 00:00:00
Proceeding: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 1
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Polyp Segmentation, Semantic Segmentation

Transformers have shown great promise in medical image segmentation due to their ability to capture long-range dependencies through self-attention. However, they lack the ability to learn the local (contextual) relations among pixels. Previous works try to overcome this problem by embedding convolutional layers either in the encoder or decoder modules of transformers thus ending up sometimes with inconsistent features. To address this issue, we propose a novel attention-based decoder, namely CASCaded Attention DEcoder (CASCADE), which leverages the multiscale features of hierarchical vision transformers. CASCADE consists of i) an attention gate which fuses features with skip connections and ii) a convolutional attention module that enhances the long-range and local context by suppressing background information. We use a multi-stage feature and loss aggregation framework due to their faster convergence and better performance. Our experiments demonstrate that transformers with CASCADE significantly outperform state-of-the-art CNN- and transformer-based approaches, obtaining up to 5.07% and 6.16% improvements in DICE and mIoU scores, respectively. CASCADE opens new ways of designing better attention-based decoders.
Drawing clinical pictures of heart failure with high mortality risk
Authors: Tanya Mohammadi, Babak Mohammadi
Date: 2021-10-04 00:00:00
Proceeding: Informatics in Medicine Unlocked 2021 10
Tasks: Vocal Bursts Intensity Prediction

Background: Risk stratification allows guiding treatment for patients with heart diseases. We aimed to provide
clinical rules for identifying patients at high risk for mortality owing to heart failure.
Methods: The research was a secondary analysis of data from a community cohort study that had been conducted
from October 2007 to December 2011 in the United States. In total, 425 adult patients (180 women) with heart
failure had been included. The mean (SD) age was 73⋅5 (13⋅1) years, and participants had been followed for 2
years. The outcome was mortality and the predictors were a variety of demographic, clinical, and laboratory
variables.
Results: We recognized two clusters of patients in the sample with different survival probabilities (P < 0⋅001) and
defined clinical rules for identifying patients at high risk for mortality. Association rule mining showed that
diabetes was a strong solitary risk for mortality in heart failure patients (support = 38⋅4%, confidence = 100%,
lift = 1⋅9). Also, combinations of categories in age, sex, systolic blood pressure, estimated glomerular filtration
rate, left ventricular ejection fraction, serum sodium, blood urea nitrogen, the cause of heart failure (ischemic vs.
non-ischemic), diabetes mellitus, and increase in natriuretic peptide were able to identify patients at risk for
death.
Conclusion: Different approaches to risk stratification provide distinct sets of clinical pictures. We presented the
results as sets of clinical pictures accessible to all healthcare professionals. The clinical pictures support recognizing
patients with heart failure who are at high risk for mortality.
Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems
Authors: Hyunki Seong, Chanyoung Chung, David Hyunchul Shim
Date: 2023-01-04 00:00:00
Tasks: Autonomous Racing, Hyperparameter Optimization

In this letter, we propose a model parameter identification method via a hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient explore-exploit strategy to identify the parameters of dynamic models in a data-driven optimization manner. We utilize our method for model parameter identification of the AV-21, a full-scaled autonomous race vehicle. We then incorporate the optimized parameters for the design of model-based planning and control systems of our platform. In experiments, MI-HPO exhibits more than 13 times faster convergence than traditional parameter identification methods. Furthermore, the parametric models learned via MI-HPO demonstrate good fitness to the given datasets and show generalization ability in unseen dynamic scenarios. We further conduct extensive field tests to validate our model-based system, demonstrating stable obstacle avoidance and high-speed driving up to 217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The source code for our work and videos of the tests are available at https://github.com/hynkis/MI-HPO.