pwc-archive/papers-with-abstracts huggingface dataset:
A Hybrid Millimeter-wave Channel Simulator for Joint Communication and Localization
Authors: Junquan Deng
Date: 2022-10-04 00:00:00

Joint communication and localization~(JCL) is envisioned to be a key feature in future millimeter-wave~(mmWave) wireless networks for context-aware applications. A map-based channel model considering both site-specific radio environment and statistical channel characteristics is essential to facilitate JCL research and to evaluate the performance of various JCL systems. To this end, this paper presents an open-source hybrid mmWave channel simulator called OmniSIM for site-specific JCL research, which uses digital map, network layout and user trajectories as inputs to predict the channel responses between users and base stations. A fast shooting-bouncing rays~(FSBR) algorithm combined with Computational Electromagnetic, has been developed to generate channel parameters relevant to JCL, considering mmWave reflection, diffusing, diffraction and scattering.
Multibody molecular docking on a quantum annealer
Authors: Mohit Pandey, Tristan Zaborniak, Hans Melo, Alexey Galda, Vikram K. Mulligan
Date: 2022-10-20 00:00:00
Tasks: Drug Discovery, Molecular Docking

Molecular docking, which aims to find the most stable interacting configuration of a set of molecules, is of critical importance to drug discovery. Although a considerable number of classical algorithms have been developed to carry out molecular docking, most focus on the limiting case of docking two molecules. Since the number of possible configurations of N molecules is exponential in N, those exceptions which permit docking of more than two molecules scale poorly, requiring exponential resources to find high-quality solutions. Here, we introduce a one-hot encoded quadratic unconstrained binary optimization formulation (QUBO) of the multibody molecular docking problem, which is suitable for solution by quantum annealer. Our approach involves a classical pre-computation of pairwise interactions, which scales only quadratically in the number of bodies while permitting well-vetted scoring functions like the Rosetta REF2015 energy function to be used. In a second step, we use the quantum annealer to sample low-energy docked configurations efficiently, considering all possible docked configurations simultaneously through quantum superposition. We show that we are able to minimize the time needed to find diverse low-energy docked configurations by tuning the strength of the penalty used to enforce the one-hot encoding, demonstrating a 3-4 fold improvement in solution quality and diversity over performance achieved with conventional penalty strengths. By mapping the configurational search to a form compatible with current- and future-generation quantum annealers, this work provides an alternative means of solving multibody docking problems that may prove to have performance advantages for large problems, potentially circumventing the exponential scaling of classical approaches and permitting a much more efficient solution to a problem central to drug discovery and validation pipelines.
Conformational variability in proteins bound to single-stranded DNA: a new benchmark for new docking perspectives
Authors: Dominique Mias-Lucquin, Isaure Chauvot de Beauchene
Date: 2022-10-20 00:00:00

We explored the Protein DataBank (PDB) to collect protein-ssDNA structures and create a multiconformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark. For the 91 sequence-identity groups identified as bound-unbound structures of the same protein, we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions, and compared it to the supposedly binding-induced modifications. To illustrate a use case of this benchmark, we performed docking experiments using ATTRACT docking software. This benchmark is, to our knowledge, the first one made to peruse available structures of ssDNA-protein interactions to such an extent, aiming to improve computational docking tools dedicated to this kind of molecular interactions.
Deep Learning-Derived Optimal Aviation Strategies to Control Pandemics
Authors: Syed Rizvi, Akash Awasthi, Maria J. Peláez, Zhihui Wang, Vittorio Cristini, Hien Van Nguyen, Prashant Dogra
Date: 2022-10-12 00:00:00
Tasks: Deep Learning, Graph Neural Network

The COVID-19 pandemic has affected countries across the world, demanding drastic public health policies to mitigate the spread of infection, leading to economic crisis as a collateral damage. In this work, we investigated the impact of human mobility (described via international commercial flights) on COVID-19 infection dynamics at the global scale. For this, we developed a graph neural network-based framework referred to as Dynamic Connectivity GraphSAGE (DCSAGE), which operates over spatiotemporal graphs and is well-suited for dynamically changing adjacency information. To obtain insights on the relative impact of different geographical locations, due to their associated air traffic, on the evolution of the pandemic, we conducted local sensitivity analysis on our model through node perturbation experiments. From our analyses, we identified Western Europe, North America, and Middle East as the leading geographical locations fueling the pandemic, attributed to the enormity of air traffic originating or transiting through these regions. We used these observations to identify tangible air traffic reduction strategies that can have a high impact on controlling the pandemic, with minimal interference to human mobility. Our work provides a robust deep learning-based tool to study global pandemics and is of key relevance to policy makers to take informed decisions regarding air traffic restrictions during future outbreaks.
Bottom-up data integration in polymer models of chromatin organisation
Authors: Alex Chen Yi Zhang, Angelo Rosa, Guido Sanguinetti
Date: 2022-10-20 00:00:00
Tasks: Bayesian Inference, Data Integration

Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus. Despite the availability of large data sets probing this process from multiple angles, we still lack a bottom-up framework which can incorporate the sequence-specific nature of biochemistry in a unified model of 3D chromatin dynamics. Here we propose SEMPER (Sequence Enhanced Magnetic PolymER), a novel stochastic polymer model which naturally incorporates observational data about sequence-driven biochemical processes, such as binding of transcription factor proteins, in a 3D model of chromatin structure. By introducing a new algorithm for approximate Bayesian inference, we discuss how to estimate in a robust manner the relative importance of biochemical vs. polymer signals in the determination of the chromatin epigenetic states which is leading to a significant revision of the interpretation of previous models. Furthermore we show that, without additional input from the genome 3D structure, our model can predict with reasonable accuracy some notable and non trivial conformational features of chromatin folding within the nucleus. Our work highlights the importance of introducing physically realistic statistical models for predicting chromatin states from epigenetic data, and opens the way to a new class of more systematic approaches to interpret epigenomic data.
Enrichment Score: a better quantitative metric for evaluating the enrichment capacity of molecular docking models
Authors: Ian Scott Knight, Slava Naprienko, John J. Irwin
Date: 2022-10-19 00:00:00
Tasks: Molecular Docking

The standard quantitative metric for evaluating enrichment capacity known as $\textit{LogAUC}$ depends on a cutoff parameter that controls what the minimum value of the log-scaled x-axis is. Unless this parameter is chosen carefully for a given ROC curve, one of the two following problems occurs: either (1) some fraction of the first inter-decoy intervals of the ROC curve are simply thrown away and do not contribute to the metric at all, or (2) the very first inter-decoy interval contributes too much to the metric at the expense of all following inter-decoy intervals. We fix this problem with LogAUC by showing a simple way to choose the cutoff parameter based on the number of decoys which forces the first inter-decoy interval to always have a stable, sensible contribution to the total value. Moreover, we introduce a normalized version of LogAUC known as $\textit{enrichment score}$, which (1) enforces stability by selecting the cutoff parameter in the manner described, (2) yields scores which are more intuitively meaningful, and (3) allows reliably accurate comparison of the enrichment capacities exhibited by different ROC curves, even those produced using different numbers of decoys. Finally, we demonstrate the advantage of enrichment score over unbalanced metrics using data from a real retrospective docking study performed using the program $\textit{DOCK 3.7}$ on the target receptor TRYB1 included in the $\textit{DUDE-Z}$ benchmark.
Modeling Reactive Hyperemia to better understand and assess Microvascular Function: a review of techniques
Authors: Albertoo Coccarelli, Michael D. Nelson
Date: 2022-10-19 00:00:00

Reactive hyperemia is a well-established technique for the non-invasive evaluation of the peripheral microcirculatory function, measured as the magnitude of limb re-perfusion after a brief period of ischemia. Despite widespread adoption by researchers and clinicians alike, many uncertainties remain surrounding interpretation, compounded by patient-specific confounding factors (such as blood pressure or the metabolic rate of the ischemic limb). Mathematical modeling can accelerate our understanding of the physiology underlying the reactive hyperemia response and guide in the estimation of quantities which are difficult to measure experimentally. In this work, we aim to provide a comprehensive guide for mathematical modeling techniques that can be used for describing the key phenomena involved in the reactive hyperemia response, alongside their limitations and advantages. The reported methodologies can be used for investigating specific reactive hyperemia aspects alone, or can be combined into a computational framework to be used in (pre-)clinical settings.
New financial ratios based on the compositional data methodology
Authors: Salvador Linares-Mustarós, Maria Àngels Farreras-Noguer, Núria Arimany-Serrat, Germà Coenders
Date: 2022-10-20 00:00:00

Due to their type of mathematical construction, the use of standard financial ratios in studies analysing the financial health of a group of firms leads to a series of statistical problems that can invalidate the results obtained. These problems are originated by the asymmetry of financial ratios. The present article justifies the use of a new methodology using compositional data (CoDa) to analyse the financial statements of a sector, improving analyses using conventional ratios since the new methodology enables statistical techniques to be applied without encountering any serious drawbacks such as skewness and outliers, and without the results depending on the arbitrary choice as to which of the accounting figures is the numerator of the ratio and which is the denominator. An example with data of the wine sector is provided. The results show that when using CoDa, outliers and skewness are much reduced and results are invariant to numerator and denominator permutation.
Beyond capacity: contractual form in electricity reliability obligations
Authors: Han Shu, Jacob Mays
Date: 2022-10-19 00:00:00
Tasks: Form

Liberalized electricity markets often include resource adequacy mechanisms that require consumers to contract with generation resources well in advance of real-time operations. While administratively defined mechanisms have most commonly taken the form of a capacity obligation, efficient markets would feature a broad array of arrangements adapted to the risk profiles and appetites of market participants. This article considers how the financial hedge embedded in alternative resource adequacy contract designs can induce different responses from risk-averse investors, with consequences for the resource mix and market structure. We construct a stochastic equilibrium model describing a competitive market with incomplete risk trading and compute investment equilibria under different contracting regimes. Two policy recommendations result. First, to avoid creating inefficiency by crowding out other forms of risk sharing, system operators should allow resources contracted through other means to opt out of mandatory capacity mechanisms, with their contribution to those requirements subtracted from administratively defined demand curves. Second, if they wish to promote a single contractual form, regulators should consider replacing existing option-like capacity mechanisms with a shaped forward contract for energy. Beyond these recommendations, we discuss the tension that liberalized systems face in seeking to promote both reliability and competitive outcomes.
Application of artificial neural network to determine the thickness profile of thin film
Authors: Archana Bora
Date: 2022-09-24 00:00:00

In this paper, we introduce a novel artificial neural network (ANN) based scheme to estimate the thickness of thin films deposited on a given substrate. Here we consider the visible interference pattern between a plane wave and a diverging wave reflected from the thin film surface that records the thickness information of the thin film. We assume a uniform thickness profile of the film. However, the thickness increases as the deposition takes place. We extract the intensity data along a line through the center of the interference pattern. We train our network by using a number of such line information of known thickness profiles. The performance of the trained network is then tested by estimating the thickness of unknown surfaces. The numerical simulation results show that the proposed technique can be very much useful for automated measurement of thickness, quickly and in real time, during deposition
Some distance bounds of branching processes and their diffusion limits
Authors: Niels B. Kammerer, Wolfgang Stummer
Date: 2010-05-20 00:00:00
Tasks: Decision Making

We compute exact values respectively bounds of "distances" - in the sense of (transforms of) power divergences and relative entropy - between two discrete-time Galton-Watson branching processes with immigration GWI for which the offspring as well as the immigration is arbitrarily Poisson-distributed (leading to arbitrary type of criticality). Implications for asymptotic distinguishability behaviour in terms of contiguity and entire separation of the involved GWI are given, too. Furthermore, we determine the corresponding limit quantities for the context in which the two GWI converge to Feller-type branching diffusion processes, as the time-lags between observations tend to zero. Some applications to (static random environment like) Bayesian decision making and Neyman-Pearson testing are presented as well.
Coupled Rigid-Block Analysis: Stability-Aware Design of Complex Discrete-Element Assemblies
Authors: Gene Ting-Chun Kao; Antonino Iannuzzo; Bernhard Thomaszewski; Stelian Coros; Tom Van Mele; Philippe Block
Date: 2022-05-01 00:00:00
Proceeding: CAD Computer-Aided Design Journal 2022 5
Tasks: 3D Assembly, Computational fabrication, Concave shapes, Contact mechanics, Friction, Stability-aware design

The rigid-block equilibrium (RBE) method uses a penalty formulation to measure structural infeasibility or to guide the design of stable discrete-element assemblies from unstable geometry. However, RBE is a purely force-based formulation, and it incorrectly describes stability when complex interface geometries are involved. To overcome this issue, this paper introduces the coupled rigid-block analysis (CRA) method, a more robust approach building upon RBE’s strengths. The CRA method combines equilibrium and kinematics in a penalty formulation in a nonlinear programming problem. An extensive benchmark campaign is used to show how CRA enables accurate modelling of complex three-dimensional discrete-element assemblies formed by rigid blocks. In addition, an interactive stability-aware design process to guide user design towards structurally-sound assemblies is proposed. Finally, the potential of our method for real-world problems are demonstrated by designing complex and scaffolding-free physical models.
Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis
Authors: Michael Rebsamen, Richard McKinley, Piotr Radojewski, Maximilian Pistor, Christoph Friedli, Robert Hoepner, Anke Salmen, Andrew Chan, Mauricio Reyes, Franca Wagner, Roland Wiest, Christian Rummel
Date: 2022-10-17 00:00:00
Proceeding: Human Brain Mapping 2022 10
Tasks: 3D Medical Imaging Segmentation, Anatomy, Brain Morphometry, Brain Segmentation, Diagnostic, Diffeomorphic Medical Image Registration, MRI segmentation

Brain morphometry is usually based on non-enhanced (pre-contrast) T1-weighted MRI. However, such dedicated protocols are sometimes missing in clinical examinations. Instead, an image with a contrast agent is often available. Existing tools such as FreeSurfer yield unreliable results when applied to contrast-enhanced (CE) images. Consequently, these acquisitions are excluded from retrospective morphometry studies, which reduces the sample size. We hypothesize that deep learning (DL)-based morphometry methods can extract morphometric measures also from contrast-enhanced MRI. We have extended DL+DiReCT to cope with contrast-enhanced MRI. Training data for our DL-based model were enriched with non-enhanced and CE image pairs from the same session. The segmentations were derived with FreeSurfer from the non-enhanced image and used as ground truth for the coregistered CE image. A longitudinal dataset of patients with multiple sclerosis (MS), comprising relapsing remitting (RRMS) and primary progressive (PPMS) subgroups, was used for the evaluation. Global and regional cortical thickness derived from non-enhanced and CE images were contrasted to results from FreeSurfer. Correlation coefficients of global mean cortical thickness between non-enhanced and CE images were significantly larger with DL+DiReCT (r = 0.92) than with FreeSurfer (r = 0.75). When comparing the longitudinal atrophy rates between the two MS subgroups, the effect sizes between PPMS and RRMS were higher with DL+DiReCT both for non-enhanced (d = −0.304) and CE images (d = −0.169) than for FreeSurfer (non-enhanced d = −0.111, CE d = 0.085). In conclusion, brain morphometry can be derived reliably from contrast-enhanced MRI using DL-based morphometry tools, making additional cases available for analysis and potential future diagnostic morphometry tools.
A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine
Date: 2022-10-12 00:00:00
Tasks: compressed sensing, Image Reconstruction, Quantitative MRI

Quantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R2*-mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R2* from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R2*-maps. In contrast, when using the U-Net as network architecture, a negative bias in R2* in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R2*. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R2* in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.
ConvUNeXt: An efficient convolution neural network for medical image segmentation
Authors: Zhimeng Han, Muwei Jian, Gai-Ge Wang
Date: 2022-07-25 00:00:00
Proceeding: Knowledge-Based Systems 2022 7
Tasks: Decoder, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Recently, ConvNeXts constructing from standard ConvNet modules has produced competitive performance in various image applications. In this paper, an efficient model based on the classical UNet, which can achieve promising results with a low number of parameters, is proposed for medical image segmentation. Inspired by ConvNeXt, the designed model is called ConvUNeXt and towards reduction in the amount of parameters while retaining outstanding segmentation superiority. Specifically, we firstly improved the convolution block of UNet by using large convolution kernels and depth-wise separable convolution to considerably decrease the number of parameters; then residual connections in both encoder and decoder are added and pooling is abandoned via adopting convolution for down-sampling; during skip connection, a lightweight attention mechanism is designed to filter out noise in low-level semantic information and suppress irrelevant features, so that the network can pay more attention to the target area. Compared to the standard UNet, our model has 20% fewer parameters, meanwhile, experimental results on different datasets show that it exhibits superior segmentation performance when the amount of data is scarce or sufficient. Code will be available at https://github.com/1914669687/ConvUNeXt.