Miracle-dz/UniFAR-FTUs huggingface dataset:
Mach number and wall thermal boundary condition effects on near-wall compressible turbulence

We investigate the effects of thermal boundary conditions and Mach number on turbulence close to walls. In particular, we study the near-wall asymptotic behavior for adiabatic and pseudo-adiabatic walls, and compare to the asymptotic behavior recently found near isothermal cold walls (Baranwal et al. (2022)). This is done by analyzing a new large database of highly-resolved direct numerical simulations of turbulent channels with different wall thermal conditions and centerline Mach numbers. We observe that the asymptotic power-law behavior of Reynolds stresses as well as heat fluxes does change with both centerline Mach number and thermal-condition at the wall. Power-law exponents transition from their analytical expansion for solenoidal fields to those for non-solenoidal field as the Mach number is increased, though this transition is found to be dependent on the thermal boundary conditions. The correlation coefficients between velocity and temperature are also found to be affected by these factors. Consistent with recent proposals on universal behavior of compressible turbulence, we find that dilatation at the wall is the key scaling parameter for this power-law exponents providing a universal functional law which can provide a basis for general models of near-wall behavior.
An Improved Compound Gaussian Model for Bivariate Surface EMG Signals Related to Strength Training

Recent literature suggests that the surface electromyography (sEMG) signals have non-stationary statistical characteristics specifically due to random nature of the covariance. Thus suitability of a statistical model for sEMG signals is determined by the choice of an appropriate model for describing the covariance. The purpose of this study is to propose a Compound-Gaussian (CG) model for multivariate sEMG signals in which latent variable of covariance is modeled as a random variable that follows an exponential model. The parameters of the model are estimated using the iterative Expectation Maximization (EM) algorithm. Further, a new dataset, electromyography analysis of human activities database 2 (EMAHA-DB2) is developed. Based on the model fitting analysis on the sEMG signals from EMAHA-DB2, it is found that the proposed CG model fits more closely to the empirical pdf of sEMG signals than the existing models. The proposed model is validated by visual inspection, further validated by matching central moments and better quantitative metrics in comparison with other models. The proposed compound model provides an improved fit to the statistical behavior of sEMG signals. Further, the estimate of rate parameter of the exponential model shows clear relation to the training weights. Finally, the average signal power estimates of the channels shows distinctive dependency on the training weights, the subject's training experience and the type of activity.
Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features

Roman Numeral analysis is the important task of identifying chords and their functional context in pieces of tonal music. This paper presents a new approach to automatic Roman Numeral analysis in symbolic music. While existing techniques rely on an intermediate lossy representation of the score, we propose a new method based on Graph Neural Networks (GNNs) that enable the direct description and processing of each individual note in the score. The proposed architecture can leverage notewise features and interdependencies between notes but yield onset-wise representation by virtue of our novel edge contraction algorithm. Our results demonstrate that ChordGNN outperforms existing state-of-the-art models, achieving higher accuracy in Roman Numeral analysis on the reference datasets. In addition, we investigate variants of our model using proposed techniques such as NADE, and post-processing of the chord predictions. The full source code for this work is available at https://github.com/manoskary/chordgnn
Joint Computing Offloading and Resource Allocation for Classification Intelligent Tasks in MEC Systems

Mobile edge computing (MEC) enables low-latency and high-bandwidth applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for intelligent tasks in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum system performance. We focus on classification intelligence tasks and formulate an optimization problem that considers both the accuracy requirements of tasks and the parallel computing capabilities of MEC systems. To solve the optimization problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation, and compression offloading. We use convex optimization and successive convex approximation to derive closed-form expressions for the subcarrier allocation, offloading decisions, computing capacity, and compressed ratio. Based on our solutions, we design an efficient computing offloading and resource allocation algorithm for intelligent tasks in MEC systems. Our simulation results demonstrate that our proposed algorithm significantly improves the performance of intelligent tasks in MEC systems and achieves a flexible trade-off between system revenue and cost considering intelligent tasks compared with the benchmarks.
Hill estimator and extreme quantile estimator for functionals of approximated stochastic processes

We study the effect of approximation errors in assessing the extreme behaviour of univariate functionals of random objects. We build our framework into a general setting where estimation of the extreme value index and extreme quantiles of the functional is based on some approximated value instead of the true one. As an example, we consider the effect of discretisation errors in computation of the norms of paths of stochastic processes. In particular, we quantify connections between the sample size $n$ (the number of observed paths), the number of the discretisation points $m$, and the modulus of continuity function $\phi$ describing the path continuity of the underlying stochastic process. As an interesting example fitting into our framework, we consider processes of form $Y(t) = \mathcal{R}Z(t)$, where $\mathcal{R}$ is a heavy-tailed random variable and the increments of the process $Z$ have lighter tails compared to $\mathcal{R}$.
Form, function, foam: evolutionary ecology of anuran nests and nesting behaviour

Amphibians exhibit an incredible diversity of reproductive and life-history strategies, including various forms of nest construction and nesting behaviour. Although anuran amphibians (frogs and toads) are not known for their nests, nesting behaviour in this clade—broadly defined as a location chosen or constructed for eggs and young—is tightly linked to the amphibious lifestyle of this group. Transitions to increasingly terrestrial living have driven reproductive diversity in anurans, including the repeated, independent evolution of nests and nesting. Indeed, a core feature of many notable anuran adaptations—including nesting behaviour—is the maintenance of an aquatic environment for developing offspring. The tight link between increasingly terrestrial reproduction and morphological, physiological and behavioural diversity in anurans provides inroads for studying the evolutionary ecology of nests, their architects and their contents. This review provides an overview of nests and nesting behaviour in anurans, highlighting areas where additional work may be particularly fruitful. I take an intentionally broad view of what constitutes nesting to highlight what we can learn from thinking and researching comparatively across anurans and vertebrates more broadly. This article is part of the theme issue ‘The evolutionary ecology of nests: a cross-taxon approach’.
New developments on AIDS‐related cancers: The role of the delay and treatment options

We propose a simple delay mathematical model for the dynamics of AIDS‐related cancers with treatment of HIV and chemotherapy. The main goals are to study the effects of the delay and of treatment (HAART and chemotherapy) in cancer cells growth. The model was simulated for several biologically reasonable values of the delay, of HAART efficacies and of chemotherapeutic drugs decay rates. The results of the simulations reveal an epidemiologically well‐defined model. Important inferences are drawn for designing future treatment protocols.
Large Linear Multi-output Gaussian Process Learning for Time Series

Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach. LMCs estimate and exploit correlations across the multiple outputs. While model estimation can be performed efficiently for single-output GPs, these assume stationarity, but in the multi-output case the cross-covariance interaction is not stationary. We propose Large Linear GP (LLGP), which circumvents the need for stationarity by inducing structure in the LMC kernel through a common grid of inputs shared between outputs, enabling optimization of GP hyperparameters for multi-dimensional outputs and low-dimensional inputs. When applied to synthetic two-dimensional and real time series data, we find our theoretical improvement relative to the current solutions for multi-output GPs is realized with LLGP reducing training time while improving or maintaining predictive mean accuracy. Moreover, by using a direct likelihood approximation rather than a variational one, model confidence estimates are significantly improved.
Optical multi-beam forming method based on a liquid crystal optical phased array

Three multi-beam forming methods based on optical phased array technology are presented in this paper. These methods can achieve simultaneous multiple-beam pointing in the optical band. The principles of these methods are introduced, the far-field pattern calculation formulas of the subaperture method and the array division multiplexing method are derived, and the algorithm flow of the iterative Fourier transform method is described in detail. Simulations and experiments have been conducted to verify the validity of these methods.
Creating Design Resources to Scaffold the Ideation of AI Concepts

Advances in artificial intelligence have enabled unprecedented technical capabilities, yet making these advances useful in the real world remains challenging. We engaged in a Research through Design process to improve the ideation of AI products and services. We developed a design resource capturing AI capabilities based on 40 AI features commonly used across various domains. To probe its usefulness, we created a set of slides illustrating AI capabilities and asked designers to ideate AI-enabled user experiences. We also incorporated capabilities into our own design process to brainstorm concepts with domain experts and data scientists. Our research revealed that designers should focus on innovations where moderate AI performance creates value. We reflect on our process and discuss research implications for creating and assessing resources to systematically explore AI’s problem-solution space.
Quantifying Meaningful Interaction: Developing the Eudaimonic Technology Experience Scale

Recent research has shown that users increasingly seek meaning in technologies and that eudaimonic user experience (UX) is part of everyday encounters with technology. Yet, to date, there is no validated means to assess eudaimonic properties in interactive artefacts. We conceptualised, developed and validated a six-item questionnaire for measuring eudaimonic properties of technologies—the Eudaimonic Technology Experience Scale (ETES). Our scale includes two factors, which describe what aspects of a eudaimonic experience can be supported by technology: eudaimonic goals and self-knowlege. We consulted work in Human-Computer Interaction (HCI), psychology and philosophy to gather an initial set of concepts that could contribute to eudaimonic UX. We then built the scale based on expert interviews and exploratory factor analysis and verified its quality in a number of tests (confirmatory factor analysis, reliability and validity checks). ETES provides a standardised tool for identifying eudaimonic qualities in interactive systems and allows for rapidly comparing prototypes.
Ludification as a Lens for Algorithmic Management: A Case Study of Gig-Workers’ Experiences of Ambiguity in Instacart Work

On-demand work platforms are attractive alternatives to traditional employment arrangements. However, several questions around employment classification, compensation, data privacy, and equitable outcomes remain open. The abilities of algorithmic management to structure different forms of platform-worker relationships compounds fraught regulatory debates. Understanding the conditions of algorithmic management that result in these variations could point us towards better worker futures. In this work, we studied the platform-worker relationships in Instacart work through the accounts of its workers. From a qualitative analysis of 400 Reddit posts by Instacart’s workers, we identified sources and types of ambiguity that gave rise to open-ended experiences for workers. Ambiguities supplemented gamification mechanisms to regulate worker behaviors. Yet, they also generated affective experiences for workers that enabled their playful participation in the Reddit community. We propose the frame of ludification to explain these seemingly contradicting findings and conclude with implications for accountability in on-demand work platforms.
Co-designing Magic Machines for Everyday Mindfulness with Practitioners

Many digital technologies have been invented to support mindfulness, the practice of bringing attention to the present moment without judgment. While most technologies focus on mindfulness meditation training for novices, in this paper, we explore designing technology to support everyday mindfulness activities for people with varying levels of experience. Through 9 magic machine workshops, 30 mindfulness practitioners explored and reflected on their personal experiences of everyday mindfulness, and generated designs that support their daily practice. Our findings identified six categories of designs conceptualized by our participants: everyday objects, physical spaces, wearables, metaphorical art, companions, and toys. We further analyze the practitioners’ thought processes and considerations for designs that support everyday mindfulness, such as eliciting and regulating emotion and associating mindfulness with routine daily activities. Finally, we discuss the implications of designing individualized mindfulness products and the potential of using co-design magic machine workshops to explore a practical design space.
GANCollage: A GAN-Driven Digital Mood Board to Facilitate Ideation in Creativity Support

During past decades, Artificial Intelligence (AI) has been consistently used in Creativity Support Tools (CSTs). Recently, with the development of generative AI models, particularly Generative Adversarial Nets (GAN) in Computer Vision, it became possible that AI directly generates visual ideas. However, there were rarely any work in creativity research that harnessed the design ideas generated by such models directly for design space exploration. In this paper, we propose a StyleGAN-driven digital mood board, GANCollage, that integrates AI generated visual ideas into the ideation phase for creativity support. GANCollage supports semantic explorations of StyleGAN generations in an iterative human-in-the-loop manner, using an AI-driven interactive tagging system. Our evaluation involving 10 participants manifests that GANCollage provides more creativity support without compromising the final results. It also offers a more enjoyable, explicit and effective way of exploring AI generated visual ideas for ideation.
Portrayal: Leveraging NLP and Visualization for Analyzing Fictional Characters

Many creative writing tasks (e.g., fiction writing) require authors to write complex narrative components (e.g., characterization, events, dialogue) over the course of a long story. Similarly, literary scholars need to manually annotate and interpret texts to understand such abstract components. In this paper, we explore how Natural Language Processing (NLP) and interactive visualization can help writers and scholars in such scenarios. To this end, we present Portrayal, an interactive visualization system for analyzing characters in a story. Portrayal extracts natural language indicators from a text to capture the characterization process and then visualizes the indicators in an interactive interface. We evaluated the system with 12 creative writers and scholars in a one-week-long qualitative study. Our findings suggest Portrayal helped writers revise their drafts and create dynamic characters and scenes. It helped scholars analyze characters without the need for any manual annotation, and design literary arguments with concrete evidence.
Mr-wallace at SemEval-2023 Task 5: Novel Clickbait Spoiling Algorithm Using Natural Language Processing

This paper presents a model for clickbait spoiling,which aims at generating short texts that satisfy thecuriosity induced by a clickbait post. The modelis split into two tasks: identifying the clickbaittype and spoiling the clickbait. The first task isto classify the spoiler type that the clickbait postwarrants, and the second task is to generate thespoiler for the clickbait post. The model utilizesthe Distilbert-base-uncased model for the first taskand the Bert-base-uncased model for the secondtask. The trained model is optimized through trialand error on different model selections, and hyper-parameters and results are presented in a confusionmatrix. The main reason we utilized Distilbert-base-uncased is that it analyzes words in the con-text of what’s around it. The objective of this modelis to save readers time and spoil the clickbait of dif-ferent articles they may see on different platformslike Twitter and Reddit
Sentiment-guided Transformer with Severity-aware Contrastive Learning for Depression Detection on Social Media

Early identification of depression is beneficial to public health surveillance and disease treatment. There are many models that mainly treat the detection as a binary classification task, such as detecting whether a user is depressed. However, identifying users’ depression severity levels from posts on social media is more clinically useful for future prevention and treatment. Existing severity detection methods mainly model the semantic information of posts while ignoring the relevant sentiment information, which can reflect the user’s state of mind and could be helpful for severity detection. In addition, they treat all severity levels equally, making the model difficult to distinguish between closely-labeled categories. We propose a sentiment-guided Transformer model, which efficiently fuses social media posts’ semantic information with sentiment information. Furthermore, we also utilize a supervised severity-aware contrastive learning framework to enable the model to better distinguish between different severity levels. The experimental results show that our model achieves superior performance on two public datasets, while further analysis proves the effectiveness of all proposed modules.
APTSumm at BioLaySumm Task 1: Biomedical Breakdown, Improving Readability by Relevancy Based Selection

In this paper we tackle a lay summarization task which aims to produce lay-summary of biomedical articles. BioLaySumm in the BioNLP Workshop at ACL 2023 (Goldsack et al., 2023), has presented us with this lay summarization task for biomedical articles. Our proposed models provide a three-step abstractive approach for summarizing biomedical articles. Our methodology involves breaking down the original document into distinct sections, generating candidate summaries for each subsection, then finally re-ranking and selecting the top performing paragraph for each section. We run ablation studies to establish that each step in our pipeline is critical for improvement in the quality of lay summary. This model achieved the second-highest rank in terms of readability scores (Luo et al., 2022). Our work distinguishes itself from previous studies by not only considering the content of the paper but also its structure, resulting in more coherent and comprehensible lay summaries. We hope that our model for generating lay summaries of biomedical articles will be a useful resource for individuals across various domains, including academia, industry, and healthcare, who require rapid comprehension of key scientific research.
Towards automatically extracting morphosyntactical error patterns from L1-L2 parallel dependency treebanks

L1-L2 parallel dependency treebanks are UD-annotated corpora of learner sentences paired with correction hypotheses. Automatic morphosyntactical annotation has the potential to remove the need for explicit manual error tagging and improve interoperability, but makes it more challenging to locate grammatical errors in the resulting datasets. We therefore propose a novel method for automatically extracting morphosyntactical error patterns and perform a preliminary bilingual evaluation of its first implementation through a similar example retrieval task. The resulting pipeline is also available as a prototype CALL application.
The Candide model: How narratives emerge where observations meet beliefs

This paper presents the Candide model as a computational architecture for modelling human-like, narrative-based language understanding. The model starts from the idea that narratives emerge through the process of interpreting novel linguistic observations, such as utterances, paragraphs and texts, with respect to previously acquired knowledge and beliefs. Narratives are personal, as they are rooted in past experiences, and constitute perspectives on the world that might motivate different interpretations of the same observations. Concretely, the Candide model operationalises this idea by dynamically modelling the belief systems and background knowledge of individual agents, updating these as new linguistic observations come in, and exposing them to a logic reasoning engine that reveals the possible sources of divergent interpretations. Apart from introducing the foundational ideas, we also present a proof-of-concept implementation that demonstrates the approach through a number of illustrative examples.