Common Workaround Research Articles

Evaluation and implementation of Lie group integration methods for rigid multibody systems

As commonly known, standard time integration of the kinematic equations of rigid bodies modeled with three rotation parameters is infeasible due to singular points. Common workarounds are reparameterization strategies or Euler parameters. Both approaches typically vary in accuracy depending on the choice of rotation parameters. To efficiently compute different kinds of multibody systems, one aims at simulation results and performance that are independent of the type of rotation parameters. As a clear advantage, Lie group integration methods are rotation parameter independent. However, few studies have addressed whether Lie group integration methods are more accurate and efficient compared to conventional formulations based on Euler parameters or Euler angles. In this paper, we close this gap using the R3×SO(3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\mathbb{R}^{3}\ imes SO(3)$\\end{document} Lie group formulation and several typical rigid multibody systems. It is shown that explicit Lie group integration methods outperform the conventional formulations in terms of accuracy. However, it turns out that the conventional Euler parameter-based formulation is the most accurate one in the case of implicit integration, while the Lie group integration method is computationally the more efficient one. It also turns out that Lie group integration methods can be implemented at almost no extra cost in an existing multibody simulation code if the Lie group method used to describe the configuration of a body is chosen accordingly.

Cascaded Multi-Modal Mixing Transformers for Alzheimer’s Disease Classification with Incomplete Data

Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using multi-modal data, outperforming single-modality models when classifying diseases such as Alzheimer’s Disease (AD). However, those models are usually not flexible enough to handle missing modalities. Currently, the most common workaround is discarding samples with missing modalities which leads to considerable data under-utilisation. Adding to the fact that labelled medical images are already scarce, the performance of data-driven methods like deep learning can be severely hampered. Therefore, a multi-modal method that can handle missing data in various clinical settings is highly desirable. In this paper, we present Multi-Modal Mixing Transformer (3MT), a disease classification transformer that not only leverages multi-modal data but also handles missing data scenarios. In this work, we test 3MT for AD and Cognitively normal (CN) classification and mild cognitive impairment (MCI) conversion prediction to progressive MCI (pMCI) or stable MCI (sMCI) using clinical and neuroimaging data. The model uses a novel Cascaded Modality Transformers architecture with cross-attention to incorporate multi-modal information for more informed predictions. We propose a novel modality dropout mechanism to ensure an unprecedented level of modality independence and robustness to handle missing data scenarios. The result is a versatile network that enables the mixing of arbitrary numbers of modalities with different feature types and also ensures full data utilization in missing data scenarios. The model is trained and evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with the state-of-the-art performance and further evaluated with The Australian Imaging Biomarker & Lifestyle Flagship Study of Ageing (AIBL) dataset with missing data.

On nature-inspired design optimization of antenna structures using variable-resolution EM models

Numerical optimization has been ubiquitous in antenna design for over a decade or so. It is indispensable in handling of multiple geometry/material parameters, performance goals, and constraints. It is also challenging as it incurs significant CPU expenses, especially when the underlying computational model involves full-wave electromagnetic (EM) analysis. In most practical cases, the latter is imperative to ensure evaluation reliability. The numerical challenges are even more pronounced when global search is required, which is most often carried out using nature-inspired algorithms. Population-based procedures are known for their ability to escape from local optima, yet their computational efficiency is poor, which makes them impractical when applied directly to EM models. A common workaround is the utilization of surrogate modeling techniques, typically in the form of iterative prediction-correction schemes, where the accumulated EM simulation data is used to identify the promising regions of the parameter space and to refine the surrogate model predictive power at the same time. Notwithstanding, implementation of surrogate-assisted procedures is often intricate, whereas their efficacy may be hampered by the dimensionality issues and considerable nonlinearity of antenna characteristics. This work investigates the benefits of incorporating variable-resolution EM simulation models into nature-inspired algorithms for optimization of antenna structures, where the model resolution pertains to the level of discretization density of an antenna structure in the full-wave simulation model. The considered framework utilizes EM simulation models which share the same physical background and are selected from a continuous spectrum of allowable resolutions. The early stages of the search process are carried out with the use of the lowest fidelity model, which is subsequently automatically increased to finally reach the high-fidelity antenna representation (i.e., considered as sufficiently accurate for design purposes). Numerical validation is executed using several antenna structures of distinct types of characteristics, and a particle swarm optimizer as the optimization engine. The results demonstrate that appropriate resolution adjustment profiles permit considerable computational savings (reaching up to eighty percent in comparison to high-fidelity-based optimization) without noticeable degradation of the search process reliability. The most appealing features of the presented approach—apart from its computational efficiency—are straightforward implementation and versatility.

A mini review on metal-organic framework-based electrode materials for capacitive deionization.

Capacitive deionization (CDI) is an electrochemical method of extracting ions from solution at potentials below electrolysis. It has various applications ranging from water remediation and desalination to heavy metal removal and selective resource recovery. A CDI device applies an electrical charge across two porous electrodes to attract and remove ions without producing waste products. It is generally considered environmentally friendly and promising for sustainability, yet ion removal efficiency still falls short of more established filtration methods. Commercially available activated carbon is typically used for CDI, and its ion adsorption capacity is low at approximately 20-30 mg g-1. Recently, much interest has been in the highly porous and well-structured family of materials known as metal-organic frameworks (MOFs). Most MOFs are poor conductors of electricity and cannot be directly used to make electrodes. A common workaround is to pyrolyze the MOF to convert its organic components to carbon while maintaining its underlying microstructure. However, most MOF-derived materials only retain partial microstructure after pyrolysis and cannot inherit the robust porosity of the parent MOFs. This review provides a systematic breakdown of structure-performance relationships between a MOF-derived material and its CDI performance based on recent works. This review also serves as a starting point for researchers interested in developing MOF-derived materials for CDI applications.

Electrospun Conducting Polymers: Approaches and Applications.

Inherently conductive polymers (CPs) can generally be switched between two or more stable oxidation states, giving rise to changes in properties including conductivity, color, and volume. The ability to prepare CP nanofibers could lead to applications including water purification, sensors, separations, nerve regeneration, wound healing, wearable electronic devices, and flexible energy storage. Electrospinning is a relatively inexpensive, simple process that is used to produce polymer nanofibers from solution. The nanofibers have many desirable qualities including high surface area per unit mass, high porosity, and low weight. Unfortunately, the low molecular weight and rigid rod nature of most CPs cannot yield enough chain entanglement for electrospinning, instead yielding polymer nanoparticles via an electrospraying process. Common workarounds include co-extruding with an insulating carrier polymer, coaxial electrospinning, and coating insulating electrospun polymer nanofibers with CPs. This review explores the benefits and drawbacks of these methods, as well as the use of these materials in sensing, biomedical, electronic, separation, purification, and energy conversion and storage applications.

Further Dimensions for Sensing in Biofluids: Distinguishing Bioorganic Analytes by the Salt-Induced Adaptation of a Cucurbit[7]uril-Based Chemosensor

Insufficient binding selectivity of chemosensors often renders biorelevant metabolites indistinguishable by the widely used indicator displacement assay. Array-based chemosensing methods are a common workaround but require additional effort for synthesizing a chemosensor library and setting up a sensing array. Moreover, it can be very challenging to tune the inherent binding preference of macrocyclic systems such as cucurbit[n]urils (CBn) by synthetic means. Using a novel cucurbit[7]uril-dye conjugate that undergoes salt-induced adaptation, we now succeeded in distinguishing 14 bioorganic analytes from each other through the facile stepwise addition of salts. The salt-specific concentration-resolved emission provides additional information about the system at a low synthetic effort. We present a data-driven approach to translate the human-visible curve differences into intuitive pairwise difference measures. Ion mobility experiments combined with density functional theory calculations gave further insights into the binding mechanism and uncovered an unprecedented ternary complex geometry for CB7. TThis work introduces the non-selectively binding, salt-adaptive cucurbit[n]uril system for sensing applications in biofluids such as urine, saliva, and blood serum.

Understanding the effects of negative (and positive) pointwise mutual information on word vectors

ABSTRACT Despite the recent popularity of contextual word embeddings, static word embeddings still dominate lexical semantic tasks, making their study of continued relevance. A widely adopted family of such static word embeddings is derived by explicitly factorising the Pointwise Mutual Information (PMI) weighting of the co-occurrence matrix. As unobserved co-occurrences lead PMI to negative infinity, a common workaround is to clip negative PMI at 0. However, it is unclear what information is lost by collapsing negative PMI values to 0. To answer this question, we isolate and study the effects of negative (and positive) PMI on the semantics and geometry of models adopting factorisation of different PMI matrices. Word and sentence-level evaluations show that only accounting for positive PMI in the factorisation strongly captures both semantics and syntax, whereas using only negative PMI captures little of semantics but a surprising amount of syntactic information. Results also reveal that incorporating negative PMI induces stronger rank invariance of vector norms and directions, as well as improved rare word representations.

RAEF: An Imputation Framework Based on a Gated Regulator Autoencoder for Incomplete IIoT Time-Series Data

The number of intelligent applications available for IIoT environments is growing, but when the time-series data these applications rely on are incomplete, their performance suffers. Unfortunately, incomplete data are all too frequent to a phenomenon in the world of IIoT. A common workaround is to use imputation. However, the current methods are largely designed to reconstruct a single missing pattern, where a robust and flexible imputation framework would be able to handle many different missing patterns. Hence, the framework presented in this study, RAEF, is capable of processing multiple missing patterns. Based on a recurrent autoencoder, RAEF houses a novel neuron structure, called a gated regulator, which reduces the negative impact of different missing patterns. In a comparison of the state-of-the-art time-series imputation frameworks at a range of different missing rates, RAEF yielded fewer errors than all its counterparts.

Pandemic Has Compromised Mental Health of New Moms

Pandemic Has Compromised Mental Health of New Moms

A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields

Abstract. Spatial downscaling of rainfall fields is a challenging mathematical problem for which many different types of methods have been proposed. One popular solution consists of redistributing rainfall amounts over smaller and smaller scales by means of a discrete multiplicative random cascade (DMRCs). This works well for slowly varying homogeneous rainfall fields but often fails in the presence of intermittency (i.e., large amounts of zero rainfall values). The most common workaround in this case is to use two separate cascade models, namely one for the occurrence and another for the intensity. In this paper, a new and simpler approach based on the notion of equal-volume areas (EVAs) is proposed. Unlike classical cascades where rainfall amounts are redistributed over grid cells of equal size, the EVA cascade splits grid cells into areas of different sizes, with each of them containing exactly half of the original amount of water. The relative areas of the subgrid cells are determined by drawing random values from a logit-normal cascade generator model with scale and intensity-dependent standard deviation (SD). The process ends when the amount of water in each subgrid cell is smaller than a fixed-bucket capacity, at which point the output of the cascade can be resampled over a regular Cartesian mesh. The present paper describes the implementation of the EVA cascade model and gives some first results for 100 selected events in the Netherlands. Performance is assessed by comparing the outputs of the EVA model to bilinear interpolation and to a classical DMRC model based on fixed grid cell sizes. Results show that, on average, the EVA cascade outperforms the classical method, producing fields with more realistic distributions, small-scale extremes and spatial structures. Improvements are mostly credited to the higher robustness of the EVA model in the presence of intermittency and to the lower variance of its generator. However, both approaches have their advantages and weaknesses. For example, while the classical cascade tends to overestimate small-scale variability and extremes, the EVA model tends to produce fields that are slightly too smooth and block shaped compared to the observations. The complementary nature of the two approaches, and the fact that they produce errors of opposite signs, opens up new possibilities for quality control and bias corrections of downscaled fields.

Geometric seismic attribute estimation using data-adaptive windows

Geometric seismic attributes such as coherence are routinely used for highlighting geologic features such as faults and channels. Traditionally, we use a single user-defined analysis window of fixed size to calculate attributes for the entire seismic volume. In general, smaller windows produce sharper geologic edges, but they are more sensitive to noise. In contrast, larger windows reduce the effect of random noise, but they might laterally smear faults and channel edges and vertically mix the stratigraphy. The vertical and lateral resolutions of a 3D seismic survey change with depth due to attenuation losses and velocity increase, such that a window size that provides optimal images in the shallower section is often too small for the deeper section. A common workaround to address this problem is to compute the seismic attributes using a suite of fixed windows and then splice the results at the risk of reducing the vertical continuity of the final volume. Our proposed solution is to define laterally and vertical smoothly varying analysis windows. The construction of such tapered windows requires a simple modification of the covariance matrix for eigenstructure-based coherence and a less obvious, but also simple, modification of semblance-based coherence. We determine the values of our algorithm by applying it to a vintage 3D seismic survey acquired offshore Louisiana, USA.

Flowchart Programs, Regular Expressions, and Decidability of Polynomial Growth-Rate

We present a new method for inferring complexity properties for a class of programs in the form of flowcharts annotated with loop information. Specifically, our method can (soundly and completely) decide if computed values are polynomially bounded as a function of the input; and similarly for the running time. Such complexity properties are undecidable for a Turing-complete programming language, and a common work-around in program analysis is to settle for sound but incomplete solutions. In contrast, we consider a class of programs that is Turing-incomplete, but strong enough to include several challenges for this kind of analysis. For a related language that has well-structured syntax, similar to Meyer and Ritchie's LOOP programs, the problem has been previously proved to be decidable. The analysis relied on the compositionality of programs, hence the challenge in obtaining similar results for flowchart programs with arbitrary control-flow graphs. Our answer to the challenge is twofold: first, we propose a class of loop-annotated flowcharts, which is more general than the class of flowcharts that directly represent structured programs; secondly, we present a technique to reuse the ideas from the work on tructured programs and apply them to such flowcharts. The technique is inspired by the classic translation of non-deterministic automata to regular expressions, but we obviate the exponential cost of constructing such an expression, obtaining a polynomial-time analysis. These ideas may well be applicable to other analysis problems.

MToS: A Tree of Shapes for Multivariate Images.

The topographic map of a gray-level image, also called tree of shapes, provides a high-level hierarchical representation of the image contents. This representation, invariant to contrast changes and to contrast inversion, has been proved very useful to achieve many image processing and pattern recognition tasks. Its definition relies on the total ordering of pixel values, so this representation does not exist for color images, or more generally, multivariate images. Common workarounds, such as marginal processing, or imposing a total order on data, are not satisfactory and yield many problems. This paper presents a method to build a tree-based representation of multivariate images, which features marginally the same properties of the gray-level tree of shapes. Briefly put, we do not impose an arbitrary ordering on values, but we only rely on the inclusion relationship between shapes in the image definition domain. The interest of having a contrast invariant and self-dual representation of multivariate image is illustrated through several applications (filtering, segmentation, and object recognition) on different types of data: color natural images, document images, satellite hyperspectral imaging, multimodal medical imaging, and videos.

Work-Arounds Observed by Fourth-Year Nursing Students

Much has been written about the need for health care professionals to consistently promote policies and best practices that create safe, high-quality care environments. At times, nurses deviate from established policies and procedures to create work-arounds or changes in work patterns to accomplish patient care goals. The purpose of this study was to identify common work-arounds and describe what influenced the nurse to engage in the work-around as observed by fourth-year baccalaureate students in clinical settings. A descriptive qualitative approach was used to describe the findings from a Quality and Safety Education for Nurses-based assignment. Ninety-six individual student assignments were included in this analysis; the themes of infection prevention and control and medication management emerged. The theme of workload emerged as the reason why students believed nurses engaged in work-arounds. Further studies are needed to determine how work-arounds influence short- and long-term patient outcomes.

Enforcing safety of cyberphysical systems using flatness and abstraction

The diffusion of cyberphysical systems acting in human-populated environments brings to the fore the problem of implementing provably safe control laws, to avoid potentially dangerous collisions between moving parts of the system, and with nearby obstacles, without compromising the system's functionality. The limiting factor in most implementations is the model's complexity, and a common workaround includes the reduction of the physical model, based on differential equations, to a finite symbolic model. Following this strategy, we are investigating ways to exploit the specific structure of many mechanical systems (the differentially flat systems) to achieve this simplification. Our objective is to construct a supervisor enforcing a given set of safety rules, while imposing as little constraints as possible on the system's functionality. In this paper, we outline our approach, and present an example -- a collision avoidance algorithm for a fleet of vehicles converging to an intersection. Our approach improves on previous results by providing a deterministic symbolic model for a class of system, regardless of their stability properties, and by addressing explicitly the problem of safety enforcing.

Breaking down barriers: Helpful breakthrough statistical methods you need to understand better

T hree barriers to effective, helpful, and accurate analysis of clinical outcomes for which breakthrough technology has just emerged include: (1) general analysis of repeated follow-up measures, (2) treatment comparisons from nonrandomized clinical experiences, and (3) scientific risk factor identification. The three barriers have in common workarounds, that is, unsatisfactory and inadequate temporary, but simple, solutions in the absence of a real solution. Our expectations may be so low that we run the risk of preferring the workarounds for their simplicity rather than rejoicing that breakthrough has occurred! Therefore, the purpose of this article is to acquaint you with the breakthrough methods in a nontechnical fashion so that you may read reports more knowledgeably, interact with your statistical collaborators more closely, or encourage your statistician to consider these methods if they are applicable to your clinical research.