Alternative Representation Research Articles

Examining mechanics problem-solving strategies among engineering students at a selected public university in Rwanda

Abstract This paper presents the findings of research conducted at a selected university in Rwanda aiming to evaluate the mechanics of problem-solving strategies among engineering students. Problem-solving is an important component of science, technology, engineering, and mathematics education as well as in everyday life. To enhance the development of problem-solving skills, researchers and curriculum creators must develop problem-solving strategies for solving mechanics problems and for improving engineering students’ performance in physics classroom tasks. The study investigated engineering students’ strategies for their mechanics problem-solving using a mixed-methods research approach that includes questionnaires, interviews, and tests. A purposive sample of 99 students was chosen, with 20 students and three instructors randomly selected for interviews. Findings revealed that students heavily rely on algebraic equations, neglecting visual representations in mechanics problem-solving. Challenges identified include limited familiarity with alternative representations, difficulties transitioning between them, and a constrained understanding of their interrelationships. Recognizing these practices and strategies informs the design of more effective pedagogical interventions. Incorporating the best problem-solving strategies in physics education is a potential avenue to bolster problem-solving skills and deepen comprehension of mechanics concepts among engineering students.

More than just consumers? Alternative representations of students when higher education becomes ‘free’

ABSTRACT The introduction of market mechanisms into higher education (HE) systems has profoundly reshaped the sector. One prominent aspect of such reforms has been the conceptualisation of the student as a consumer, ostensibly empowered by choice and seeking value for money from their tuition fees. In this article, we report the findings of an empirical study of student representations when HE becomes ‘free’, after four decades of marketisation. The analysis was conducted in Chile, where the HE sector has undergone a controversial reform that removed tuition fees for low and middle income undergraduate students known as Gratuidad. Considering the importance of the student-consumer subjectivity invoked by proponents of marketisation, how are HE students discursively constructed in a context of fee removal? Based on a discourse analysis of news media, we present three representations of undergraduate students during public discussion of Gratuidad that were common in their negative portrayal: students as (1) egocentric, (2) victims of discrimination, and (3) marionettes. Our work thus indicates that in a culture of marketisation, policy efforts designed to democratise can be overshadowed by strong media discourses opposing government efforts to publicly fund HE.

Mixed orthogonality graphs for continuous‐time state space models and orthogonal projections

In this article, we derive (local) orthogonality graphs for the popular continuous‐time state space models, including in particular multivariate continuous‐time ARMA (MCARMA) processes. In these (local) orthogonality graphs, vertices represent the components of the process, directed edges between the vertices indicate causal influences and undirected edges indicate contemporaneous correlations between the component processes. We present sufficient criteria for state space models to satisfy the assumptions of Fasen‐Hartmann and Schenk (2024a) so that the (local) orthogonality graphs are well‐defined and various Markov properties hold. Both directed and undirected edges in these graphs are characterised by orthogonal projections on well‐defined linear spaces. To compute these orthogonal projections, we use the unique controller canonical form of a state space model, which exists under mild assumptions, to recover the input process from the output process. We are then able to derive some alternative representations of the output process and its highest derivative. Finally, we apply these representations to calculate the necessary orthogonal projections, which culminate in the characterisations of the edges in the (local) orthogonality graph. These characterisations are given by the parameters of the controller canonical form and the covariance matrix of the driving Lévy process.

Environmental sound classification using raw-audio based ensemble framework

Environmental sound classification (ESC) aims to automatically recognize audio recordings from the underlying environment, such as "urban park" or "city centre". Most of the existing methods for ESC use hand-crafted time-frequency features such as log-mel spectrogram to represent audio recordings. However, the hand-crafted features rely on transformations that are defined beforehand and do not consider the variability in the environment due to differences in recording conditions or recording devices. To overcome this, we present an alternative representation framework by leveraging a pre-trained convolutional neural network, SoundNet, trained on a large-scale audio dataset to represent raw audio recordings. We observe that the representations obtained from the intermediate layers of SoundNet lie in low-dimensional subspace. However, the dimensionality of the low-dimensional subspace is not known. To address this, an automatic compact dictionary learning framework is utilized that gives the dimensionality of the underlying subspace. The low-dimensional embeddings are then aggregated in a late-fusion manner in the ensemble framework to incorporate hierarchical information learned at various intermediate layers of SoundNet. We perform experimental evaluation on publicly available DCASE 2017 and 2018 ASC datasets. The proposed ensemble framework improves performance between 1 and 4 percentage points compared to that of existing time-frequency representations.

Investigating Alternative Representations of Building Structures in Traffic Noise Modeling

This paper presents the results of an investigation of various traffic noise modeling techniques with the Traffic Noise Model (TNM) 2.5 software package, with specific focus on the representation of traffic noise shielding from residences parallel to the freeway. A comprehensive traffic noise field study was undertaken in a neighborhood adjacent to Interstate 270 in Columbus, Ohio. Data collection activities occurred in three “waves” accounting for the pre-clearing, no barrier, and with barrier conditions. Measured noise levels at the study site indicated that there was some noise shielding effect of the residential structures; this effect was most readily perceptible behind the first row of residences. Separate TNM layouts were developed to represent the neighborhood’s residential structures as either a TNM building row object or as separate TNM barrier objects for each structure. The analysis found that modeling each individual residential structure as a separate TNM noise barrier object (single building façade that is closest to the freeway) produces the most accurate modeled noise levels, as compared to the building row representation or simply omitting the shielding from structures altogether. Based on these findings, it is recommended that traffic noise practitioners consider modeling each residential structure in noise-sensitive areas as TNM noise barrier objects.

Spatial Computing Opportunities in Biomedical Decision Support: The Atlas-EHR Vision

We consider the problem of reducing the time that healthcare professionals need to understand the patient’s medical history through the next generation of biomedical decision support. This problem is societally important because it has the potential to improve healthcare quality and patient outcomes. However, navigating electronic health records (EHR) is challenging due to high patient-doctor ratios, potentially long medical histories, urgency of treatment for some medical conditions, and patient variability. The current EHR systems provide only a longitudinal view of patient medical history, which is time-consuming to browse, and doctors often need to engage nurses, residents, and others for initial analysis. To overcome this limitation, we envision an alternative spatial representation of patient histories (e.g., electronic health records) and other biomedical data in the form of Atlas-EHR. Just like Google Maps, which allows a global, national, regional, and local view, Atlas-EHR can start with an overview of the patient’s anatomy and history before drilling down to spatially anatomical subsystems, their individual components, or subcomponents. Atlas-EHR presents a compelling opportunity for spatial computing since healthcare is almost a fifth of the US economy. However, traditional spatial computing designed for geographic use cases (e.g., navigation, land survey, and mapping) faces many hurdles in the biomedical domain. This article presents several open research questions under this theme in five broad areas of spatial computing.

Towards a practice of translanguaging subtitling for the mediatised articulation of fangyan

Abstract This article addresses an underexplored intersection between subtitling and translanguaging with reference to the representation of diverse Chinese fangyan in hip-hop music videos. Drawing on recent progress in the intersection between translanguaging and Translation Studies, the article sketches the multilingualism of the Han ethnic majority and its precarious existence under the governmental policy and language planning of China. Second, it contextualises the rising audibility and visibility of local speeches and non-standard writing in cinematic, metrolingual, and digital cultures. This highlights the social and technological conditions that enable culture creators and language users to practice novel representations of linguistic diversity and variations in the subtitling medium. Furthermore, the case-study analyses illustrate how Chinese hip-hop musicians assemble linguistic, multimodal, and multisemiotic resources to effect translanguaging performance across different communicational repertoires. The conclusion outlines the practical and theoretical implications of translanguaging subtitles for alternative media representations of linguacultural diversities in China and beyond.

The expression dependence graph

The system dependence graph (SDG) is a widely used program representation for the static analysis of programs. In particular, it is the standard way to represent control and flow dependences between the statements of a program. It is at the core of program slicing, a technique that extracts the part of a program that can influence a given program point (the so-called slicing criterion). In this paper, we identify several program slicing situations (e.g., list comprehensions, try-catch and for) where the representation provided by the SDG is imprecise, and we present an alternative program representation, which is indeed an extension of the SDG, that solves these situations. We call this extension the expression dependence graph (EDG). The EDG redefines the SDG by increasing the granularity of its nodes to abstract syntax tree (AST) nodes. The implications of this change include an improvement in precision, the appearance of new kinds of dependences (besides control and flow dependences), and the possibility of selecting any (sub)expression as the slicing criterion.

Data transformations cause altered edaphic‐climatic controls and reduced predictability on soil carbon decomposition rates

AbstractData transformation of the reference soil organic matter (SOM) decomposition rates (kref), often derived as turnover times or in alternative formats, is commonly used to develop ecological models for projecting the persistence of SOM. However, the effects of reciprocal or logarithmic transformation of kref on model performance and edaphic‐climatic patterns remain uncertain. Here, we convert published kref values into reciprocal or logarithmic formats and establish machine learning models between the transformed kref and edaphic‐climatic predictors. We show that models trained with the transformed kref exhibit a 11.6%−68.4% reduction in performance upon re‐conversion to kref compared to those trained with the original kref. The variable importance analysis identifies distinct key predictors governing the original kref and its transformed counterparts. This suggests that data transformation alters the relative significance of predictors without necessarily improving kref prediction performance. Consequently, our study underscores the importance of directly focusing on the original values rather than alternative representations when dissecting a given variable's patterns and mechanisms in ecological modeling.

Applying A/r/tography as a creative community resilience strategy in response to the climate emergency

ABSTRACT This paper connects arts education creative practice and the rise of climate emergency discourse with community resilience policymaking. We apply a creative-relational approach informed by the principles of a/r/tography to position community stories as central to place based transformation. Through our Guattari-inspired conceptual model, we apply the a/r/tographic method to critically explore its suitability for identifying and mapping hidden community stories — traces of a new ecosophy — as a local resilience response. Our research took place in North Lanarkshire, Scotland, as part of a creative programme commissioned to address inequalities through the arts. We entered into artistic dialogue with community creative practices that occurred at two ecological sites, creating reflective textual and visual artworks to produce alternative representations of place that challenge dominant narratives within local resilience policymaking. These mutual creative engagements informed our findings while remaining within their artistic context rather than reduced to categorised themes. This work offers a new local resilience strategy to strengthen community representation of their places.

A Deep Dive into Statistical Modeling of RNA Splicing QTLs Reveals New Variants that Explain Neurodegenerative Disease.

Genome-wide association studies (GWAS) have identified thousands of putative disease causing variants with unknown regulatory effects. Efforts to connect these variants with splicing quantitative trait loci (sQTLs) have provided functional insights, yet sQTLs reported by existing methods cannot explain many GWAS signals. We show current sQTL modeling approaches can be improved by considering alternative splicing representation, model calibration, and covariate integration. We then introduce MAJIQTL, a new pipeline for sQTL discovery. MAJIQTL includes two new statistical methods: a weighted multiple testing approach for sGene discovery and a model for sQTL effect size inference to improve variant prioritization. By applying MAJIQTL to GTEx, we find significantly more sGenes harboring sQTLs with functional significance. Notably, our analysis implicates the novel variant rs582283 in Alzheimer's disease. Using antisense oligonucleotides, we validate this variant's effect by blocking the implicated YBX3 binding site, leading to exon skipping in the gene MS4A3.

OneTrack - An End2End approach to enhance MOT with Transformers

This paper introduces OneTrack, an innovative end-to-end transformer-based model for Multiple Object Tracking (MOT), focusing on enhancing efficiency without significantly compromising accuracy. Addressing the challenges inherent in MOT, such as occlusions, varied object sizes, and motion prediction, OneTrack leverages the power of vision transformers and attention layers, optimizing them for real-time applications. Utilizing a unique Object Sequence Patch Input and a Vision Transformer Encoder, the model simplifies the standard transformer approach by employing only the encoder component, significantly reducing computational costs. This approach is validated using the MOT17 dataset, a benchmark in the field, ensuring a comprehensive evaluation against established metrics like MOTA, HOTA, and IDF1. The experimental results demonstrate OneTrack's capability to outperform other transformer-based models in inference speed, with a marginal trade-off in accuracy metrics. The model's inherent design limitations, such as a maximum of 100 objects per window, are adjustable to suit specific applications, offering flexibility in various scenarios. The conclusion highlights the model's potential as a lightweight solution for MOT tasks, suggesting future work directions that include exploring alternative data representations and encoders, and developing a dedicated loss function to further enhance detection and tracking capabilities. OneTrack presents a promising step towards efficient and effective MOT solutions, catering to the demands of real-time applications.

Generalized Time‐Series Analysis for In Situ Spacecraft Observations: Anomaly Detection and Data Prioritization Using Principal Components Analysis and Unsupervised Clustering

AbstractIn situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near‐Earth space and beyond. However, on‐orbit telemetry constraints can severely limit the capability of spacecraft to transmit high‐cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission's science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist‐In‐The‐Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high‐cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low‐cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One‐Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data's potential for scientific utility. The technique's performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e‐POP/Swarm‐Echo and MMS missions.

Extreme ATM skew in a local volatility model with discontinuity: joint density approach

This paper concerns a local volatility model in which the volatility takes two possible values, and the specific value depends on whether the underlying price is above or below a given threshold. The model is known, and a number of results have been obtained for it. In particular, option pricing formulas and a power-law behaviour of the implied volatility skew have been established in the case when the threshold is taken at the money. In this paper, we derive an alternative representation of option pricing formulas. In addition, we obtain an approximation of option prices by the corresponding Black–Scholes prices. Using this approximation streamlines obtaining the aforementioned behaviour of the skew. Our approach is based on the natural relationship of the model with skew Brownian motion and consists of the systematic use of the joint distribution of this stochastic process and some of its functionals.

On the Uniform Duality in Copositive Optimization

Abstract In this paper, we establish new necessary and sufficient conditions guaranteeing the uniform LP duality for linear problems of Copositive Programming and formulate these conditions in different equivalent forms. The main results are obtained using the approach developed in previous papers of the authors and based on a concept of immobile indices that permits alternative representations of the set of feasible solutions.

Gender and terror tangled in the weeds: Poison Ivy between eco-feminism and eco-terrorism

ABSTRACT Eco-Terrorist. Femme fatale. Botanist. Environmentalist. Scientist. Villain. DC’s Poison Ivy has been challenging ideas about human interactions with the planet since 1966. Exploring alternative portrayals of femininity, power and plant liberation, Ivy sits at a unique intersection between female and plant empowerment – which is complicated by her criminal activities. By examining recently published comic book stories of Poison Ivy in conjunction with academic feminist and terrorism theory, this paper investigates how these two ideas intertwine. Ivy’s approach to the environment and her engagement with nature are inextricably linked to violence in the form of eco-terrorism, and this paper unpacks the extent to which this calls into question her portrayal as a plant-empowered female protagonist. In this way, the article explores ideas of identity and autonomy at the crossroads of feminism and botany through alternative representations of eco-feminism, but also reveals a number of provocative tensions at this very intersection.

A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

Kolmogorov–Arnold Networks (KANs) were recently introduced as an alternative representation model to MLP. Herein, we employ KANs to construct physics-informed machine learning models (PIKANs) and deep operator models (DeepOKANs) for solving differential equations for forward and inverse problems. In particular, we compare them with physics-informed neural networks (PINNs) and deep operator networks (DeepONets), which are based on the standard MLP representation. We find that although the original KANs based on the B-splines parameterization lack accuracy and efficiency, modified versions based on low-order orthogonal polynomials have comparable performance to PINNs and DeepONet, although they still lack robustness as they may diverge for different random seeds or higher order orthogonal polynomials. We visualize their corresponding loss landscapes and analyze their learning dynamics using information bottleneck theory. Our study follows the FAIR principles so that other researchers can use our benchmarks to further advance this emerging topic.

Visualizing Evaluation Theory: Current Reflections and Future Projections

Background: Among other potential benefits, evaluation theories are intended to serve as a guide to evaluation practice. Scholars have offered alternative visual representations of multiple evaluation theories, their contents and/or interrelationships. Purpose: I offer brief commentary on the evaluation theory visualizations in other articles in this special section of the Journal of Multidisciplinary Evaluation (JMDE). I suggest evaluators should be familiar with multiple representations. I also speculate about a future generation of visual representations which might be interactive, allowing evaluators to select the level of detail most useful to them.

Information Design and Sharing in Supply Chains

We study the interplay between inventory replenishment policies and information sharing in the context of a two-tier supply chain with a single supplier and a single retailer serving an independent and identically distributed Gaussian market demand. We investigate how the retailer’s inventory policy impacts the supply chain’s cumulative expected long-term average inventory costs [Formula: see text] in two extreme information-sharing cases: (a) full information sharing and (b) no information sharing. To find the retailer’s inventory policy that minimizes [Formula: see text], we formulate an infinite-dimensional optimization problem whose decision variables are the MA([Formula: see text]) coefficients that characterize a stationary ordering policy. Under full information sharing, the optimization problem admits a simple solution and the optimal policy is given by an MA(1) process. On the other hand, to solve the optimization problem under no information sharing, we reformulate the optimization from its time domain formulation to an equivalent z-transform formulation in which the decision variables correspond to elements of the Hardy space H2. This alternative representation allows us to use a number of results from H2 theory to compute the optimal value of [Formula: see text] and characterize a sequence of ϵ-optimal inventory policies under some mild technical conditions. By comparing the optimal solution under full information sharing and no information sharing, we derive a number of important practical takeaways. For instance, we show that there is value in information sharing if and only if the retailer’s optimal policy under full information sharing is not invertible with respect to the sequence of demand shocks. Furthermore, we derive a fundamental mathematical identity that reveals the value of information sharing by exploiting the canonical Smirnov–Beurling inner–outer factorization of the retailer’s orders when viewed as an element of H2. We also show that the value of information sharing can grow unboundedly when the cumulative supply chain costs are dominated by the supplier’s inventory costs. Funding: R. Caldentey acknowledges the University of Chicago Booth School of Business for financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2023.008 .

Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger–Rivlin–Ericksen

Abstract Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent generalization performance. In these models, the stress prediction follows from a linear combination of invariant-dependent coefficient functions and known tensor basis generators. However, thus far the formulations have been limited to stress representations based on the classical Finger–Rivlin–Ericksen form, while the performance of alternative representations has yet to be investigated. In this work, we survey a variety of tensor basis neural network models for modeling hyperelastic materials in a finite deformation context, including a number of so far unexplored formulations which use theoretically equivalent invariants and generators to Finger–Rivlin–Ericksen. Furthermore, we compare potential-based and coefficient-based approaches, as well as different calibration techniques. Nine variants are tested against both noisy and noiseless datasets for three different materials. Theoretical and practical insights into the performance of each formulation are given.