Miracle-dz/UniFAR-FTUs huggingface dataset:
Viscosity-model-independent generalized Reynolds number for laminar flow of shear-thinning and viscoplastic fluids

Understanding the flow dynamics of non-Newtonian fluids is crucial in various engineering, industrial, and biomedical applications. However, the existing generalized Reynolds number formulations for non-Newtonian fluids have limited applicability due to their dependencies on their specific viscosity models. In this study, we propose a new viscosity-model-independent formulation for a generalized Reynolds number. The proposed method is based on the direct adaptation of the measurement principles of rotational viscometers for wall shear rate estimation. We assess the accuracy of this proposed formulation for power-law and Carreau-Yasuda viscosity models through robust friction factor experiments. The experimental results demonstrate the applicability and effectiveness of the proposed viscosity-model-independent Reynolds number, as the measured friction factor data align closely with our Reynolds number predictions. Furthermore, we compare the accuracy of our Reynolds number formulation against established generalized Reynolds formulations for pure shear-thinning (Carreau-Yasuda) and viscoplastic (Herschel-Bulkley-extended) models. The results of the comparative analysis confirm the reliability and robustness of this generalized Reynolds number in characterizing and interpreting flow behavior in systems with visco-inelastic non-Newtonian fluids. This unified generalized Reynolds number formulation presents new and significant opportunities for precise flow characterization and interpretation as it is applicable to any visco-inelastic (time-independent) viscosity model without requiring additional derivations.
Beyond Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a variety of applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation.We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization.
A study of dissipative models based on Dirac matrices

We generalize the recent work of Shibata and Katsura, who considered a S=1/2 chain with alternating XX and YY couplings in the presence of dephasing, the dynamics of which are described by the GKLS master equation. Their model is equivalent to a non-Hermitian system described by the Kitaev formulation in terms of a single Majorana species hopping on a two-leg ladder in the presence of a nondynamical Z_2 gauge field. Our generalization involves Dirac gamma matrix `spin' operators on the square lattice, and maps onto a non-Hermitian square lattice bilayer which is also Kitaev-solvable. We describe the exponentially many non-equilibrium steady states in this model. We identify how the spin degrees of freedom can be accounted for in the 2d model in terms of the gauge-invariant quantities and then proceed to study the Liouvillian spectrum. We use a genetic algorithm to estimate the Liouvillian gap and the first decay modes for large system sizes. We observe a transition in the first decay modes, similar to that found by Shibata and Katsura. The results we obtain are consistent with a perturbative analysis for small and large values of the dissipation strength.
SegMatch: A semi-supervised learning method for surgical instrument segmentation

Surgical instrument segmentation is recognised as a key enabler to provide advanced surgical assistance and improve computer assisted interventions. In this work, we propose SegMatch, a semi supervised learning method to reduce the need for expensive annotation for laparoscopic and robotic surgical images. SegMatch builds on FixMatch, a widespread semi supervised classification pipeline combining consistency regularization and pseudo labelling, and adapts it for the purpose of segmentation. In our proposed SegMatch, the unlabelled images are weakly augmented and fed into the segmentation model to generate a pseudo-label to enforce the unsupervised loss against the output of the model for the adversarial augmented image on the pixels with a high confidence score. Our adaptation for segmentation tasks includes carefully considering the equivariance and invariance properties of the augmentation functions we rely on. To increase the relevance of our augmentations, we depart from using only handcrafted augmentations and introduce a trainable adversarial augmentation strategy. Our algorithm was evaluated on the MICCAI Instrument Segmentation Challenge datasets Robust-MIS 2019 and EndoVis 2017. Our results demonstrate that adding unlabelled data for training purposes allows us to surpass the performance of fully supervised approaches which are limited by the availability of training data in these challenges. SegMatch also outperforms a range of state-of-the-art semi-supervised learning semantic segmentation models in different labelled to unlabelled data ratios.
Money Growth and Inflation: A Quantile Sensitivity Approach

An innovative method is proposed to construct a quantile dependence system for inflation and money growth. By considering all quantiles and leveraging a novel notion of quantile sensitivity, the method allows the assessment of changes in the entire distribution of a variable of interest in response to a perturbation in another variable's quantile. The construction of this relationship is demonstrated through a system of linear quantile regressions. Then, the proposed framework is exploited to examine the distributional effects of money growth on the distributions of inflation and its disaggregate measures in the United States and the Euro area. The empirical analysis uncovers significant impacts of the upper quantile of the money growth distribution on the distribution of inflation and its disaggregate measures. Conversely, the lower and median quantiles of the money growth distribution are found to have a negligible influence. Finally, this distributional impact exhibits variation over time in both the United States and the Euro area.
Optimization in Non-parametric Survival Analysis and Climate Change Modeling

Many of the open problems of current interest in probability and statistics involve complicated data sets that do not satisfy the strong assumptions of being independent and identically distributed. Often, the samples are known only empirically, and making assumptions about underlying parametric distributions is not warranted by the insufficient information available. Under such circumstances, the usual Fisher or parametric Bayes approaches cannot be used to model the data or make predictions. However, this situation is quite often encountered in some of the main challenges facing statistical, data-driven studies of climate change, clinical studies, or financial markets, to name a few. We propose a novel approach, based on large deviations theory, convex optimization, and recent results on surrogate loss functions for classifier-type problems, that can be used in order to estimate the probability of large deviations for complicated data. This may include, for instance, highdimensional data, highly-correlated data, or very sparse data. The thesis introduces the new approach, reviews the current known theoretical results, and then presents a number of numerical explorations meant to quantify how far the approximation of survival functions via large deviations principle can be taken, once we leave the limitations imposed by the existing theoretical results. The explorations are encouraging, indicating that indeed the new approximation scheme may be very efficient and can be used under much more general conditions than those warranted by the current theoretical thresholds. After applying the new methodology to two important contemporary problems (atmospheric CO2 data and El Niño/La Niña phenomena), we conclude with a summary outline of possible further research.
On the Optimal Expressive Power of ReLU DNNs and Its Application in Approximation with Kolmogorov Superposition Theorem

This paper is devoted to studying the optimal expressive power of ReLU deep neural networks (DNNs) and its application in approximation via the Kolmogorov Superposition Theorem. We first constructively prove that any continuous piecewise linear functions on $[0,1]$, comprising $O(N^2L)$ segments, can be represented by ReLU DNNs with $L$ hidden layers and $N$ neurons per layer. Subsequently, we demonstrate that this construction is optimal regarding the parameter count of the DNNs, achieved through investigating the shattering capacity of ReLU DNNs. Moreover, by invoking the Kolmogorov Superposition Theorem, we achieve an enhanced approximation rate for ReLU DNNs of arbitrary width and depth when dealing with continuous functions in high-dimensional spaces.
Banzhaf Values for Facts in Query Answering

Quantifying the contribution of database facts to query answers has been studied as means of explanation. The Banzhaf value, originally developed in Game Theory, is a natural measure of fact contribution, yet its efficient computation for select-project-join-union queries is challenging. In this paper, we introduce three algorithms to compute the Banzhaf value of database facts: an exact algorithm, an anytime deterministic approximation algorithm with relative error guarantees, and an algorithm for ranking and top-$k$. They have three key building blocks: compilation of query lineage into an equivalent function that allows efficient Banzhaf value computation; dynamic programming computation of the Banzhaf values of variables in a Boolean function using the Banzhaf values for constituent functions; and a mechanism to compute efficiently lower and upper bounds on Banzhaf values for any positive DNF function. We complement the algorithms with a dichotomy for the Banzhaf-based ranking problem: given two facts, deciding whether the Banzhaf value of one is greater than of the other is tractable for hierarchical queries and intractable for non-hierarchical queries. We show experimentally that our algorithms significantly outperform exact and approximate algorithms from prior work, most times up to two orders of magnitude. Our algorithms can also cover challenging problem instances that are beyond reach for prior work.
A Simple Direct Empirical Observation of Systematic Bias of the Redshift as a Distance Indicator

Recent puzzling observations, such as the H0 tension, large-scale anisotropies, and massive disk galaxies at high redshifts, have been challenging the standard cosmological model. While one possible explanation is that the standard model is incomplete, other theories are based on the contention that the redshift model as a distance indicator might be biased. These theories can explain the recent observations, but they are challenged by the absence of a direct empirical reproducible observation that the redshift model can indeed be inconsistent. Here, I describe a simple experiment that shows that the spectra of galaxies depend on their rotational velocity relative to the rotational velocity of the Milky Way. Moreover, it shows that the redshift of galaxies that rotate in the opposite direction relative to the Milky Way is significantly smaller compared with the redshift of galaxies that rotate in the same direction relative to the Milky Way (p < 0.006). Three different datasets were used independently, each one was prepared in a different manner, and all of them showed similar redshift bias. A fourth dataset of galaxies from the Southern Galactic pole was also analyzed and shows similar results. All four datasets are publicly available. While a maximum average z difference of ∼0.012 observed with galaxies of relatively low redshift (z < 0.25) is not extreme, the bias is consistent and canpotentially lead to explanations to puzzling observations such as the H0 tension.
MNL-Prophet: Sequential Assortment Selection under Uncertainty

Due to numerous applications in retail and (online) advertising the problem of assortment selection has been widely studied under many combinations of discrete choice models and feasibility constraints. In many situations, however, an assortment of products has to be constructed gradually and without accurate knowledge of all possible alternatives; in such cases, existing offline approaches become inapplicable. We consider a stochastic variant of the assortment selection problem, where the parameters that determine the revenue and (relative) demand of each item are jointly drawn from some known item-specific distribution. The items are observed sequentially in an arbitrary and unknown order; upon observing the realized parameters of each item, the decision-maker decides irrevocably whether to include it in the constructed assortment, or forfeit it forever. The objective is to maximize the expected total revenue of the constructed assortment, relative to that of an offline algorithm which foresees all the parameter realizations and computes the optimal assortment. We provide simple threshold-based online policies for the unconstrained and cardinality-constrained versions of the problem under a natural class of substitutable choice models; as we show, our policies are (worst-case) optimal under the celebrated Multinomial Logit choice model. We extend our results to the case of knapsack constraints and discuss interesting connections to the Prophet Inequality problem, which is already subsumed by our setting.
Evaluating Pedestrian Trajectory Prediction Methods for the Application in Autonomous Driving

In this paper, we assess the state of the art in pedestrian trajectory prediction within the context of generating single trajectories, a critical aspect aligning with the requirements in autonomous systems. The evaluation is conducted on the widely-used ETH/UCY dataset where the Average Displacement Error (ADE) and the Final Displacement Error (FDE) are reported. Alongside this, we perform an ablation study to investigate the impact of the observed motion history on prediction performance. To evaluate the scalability of each approach when confronted with varying amounts of agents, the inference time of each model is measured. Following a quantitative analysis, the resulting predictions are compared in a qualitative manner, giving insight into the strengths and weaknesses of current approaches. The results demonstrate that although a constant velocity model (CVM) provides a good approximation of the overall dynamics in the majority of cases, additional features need to be incorporated to reflect common pedestrian behavior observed. Therefore, this study presents a data-driven analysis with the intent to guide the future development of pedestrian trajectory prediction algorithms.
Understanding Developers Well-Being and Productivity: a 2-year Longitudinal Analysis during the COVID-19 Pandemic

The COVID-19 pandemic has brought significant and enduring shifts in various aspects of life, including increased flexibility in work arrangements. In a longitudinal study, spanning 24 months with six measurement points from April 2020 to April 2022, we explore changes in well-being, productivity, social contacts, and needs of software engineers during this time. Our findings indicate systematic changes in various variables. For example, well-being and quality of social contacts increased while emotional loneliness decreased as lockdown measures were relaxed. Conversely, people’s boredom and productivity, remained stable. Furthermore, a preliminary investigation into the future of work at the end of the pandemic revealed a consensus among developers for a preference of hybrid work arrangements. We also discovered that prior job changes and low job satisfaction were consistently linked to intentions to change jobs if current work conditions do not meet developers’ needs. This highlights the need for software organizations to adapt to various work arrangements to remain competitive employers. Building upon our findings and the existing literature, we introduce the Integrated Job Demands-Resources and Self-Determination (IJARS) Model as a comprehensive framework to explain the well-being and productivity of software engineers during the COVID-19 pandemic.
Efficacy of the Localized Aviation MOS Program in Ceiling Flight Category Forecasts

(1) Background: Flying in instrument meteorological conditions (IMC) carries an elevated risk of fatal outcome for general aviation (GA) pilots. For the typical GA flight, aerodrome-specific forecasts (Terminal Aerodrome Forecast (TAF), Localized Aviation Model Output Statistics Program (LAMP)) assist the airman in pre-determining whether a flight can be safely undertaken. While LAMP forecasts are more prevalent at GA-frequented aerodromes, the Federal Aviation Administration (FAA) recommends that this tool be used as supplementary to the TAF only. Herein, the predictive accuracy of LAMP for ceiling flight categories of visual flight rules (VFR) and instrument flight rules (IFR) was determined. (2) Methods: LAMP accuracy was evaluated for the period of July–December 2018 using aviation-specific probability of detection (PODA), false alarm ratio (FARA) and critical success scores (CSSA). Statistical differences were determined using Chi-Square tests. (3) Results: LAMP forecasts (n = 823) across 39 states were accrued. LAMP PODA for VFR (0.67) and IFR (0.78) exceeded (p < 0.031) the corresponding TAF scores (0.57 and 0.56). For VFR, the LAMP showed a non-significant (p = 0.243) higher FARA (0.25) than the TAF (0.19). For IFR forecasts, the LAMP FARA was lower (p < 0.001) (0.48 and 0.81, respectively). LAMP CSSA scores exceeded the TAF for VFR (p = 0.012) and IFR forecasts (p < 0.001). (4) Conclusion: These findings support the greater integration of LAMP into pre-flight weather briefings.
Better Sheep and Cattle Nutrition by Piping Artesian Water

Capping bores and controlling the flow of artesian water through pipes allows certain nutrient supplements to be given in drinking water, and the regulation of grazing to protect vulnerable pastures and to exclude unwanted animal species. The low rumen ammonia-N levels found in grazing animals during dry seasons strongly suggest that ruminally available N is the first-limiting nutrient in both mulga grassland and Mitchell grass associations. P deficiency occurs widely over the mulga lands and may also be a first-limiting nutrient in these areas. Initial studies of water-based nutrient supplementation have been promising, although there are concerns about the effects of non-uniform drinking water intake on urea consumption. Water medication could be extended to include anthelmintics, insecticides and growth promoters. Control of water reticulation potentially allows control of grazing by restricting availability of drinking water, and control of pest animals.
Multi-direction prediction based on SALSTM model for ship motion

This paper proposes a self-attention LSTM (SALSTM) model for ship motion prediction, which combines the advantages of LSTM and self-attention mechanisms. The model also introduces the concept of attention gate. The paper studies the influence of forecast lead time on the prediction accuracy of three degrees of freedom: roll, surge and heave. The paper compares the SALSTM model with a baseline LSTM model on a ship motion data set under different forecast durations and lead times. The paper evaluates the performance of the SALSTM model using four metrics and verifies its effectiveness under three representative working conditions. The paper also gives the applicable conditions of the SALSTM model for ship motion prediction
Evidence that superstructures comprise self-similar coherent motions in high Reynolds number boundary layers

Abstract We present experimental evidence that the superstructures in turbulent boundary layers comprise smaller, geometrically self-similar coherent motions. The evidence comes from identifying and analysing instantaneous superstructures from large-scale particle image velocimetry datasets acquired at high Reynolds numbers, capable of capturing streamwise elongated motions extending up to 12 times the boundary layer thickness. Given the challenge in identifying the constituent motions of the superstructures based on streamwise velocity signatures, a new approach is adopted that analyses the wall-normal velocity fluctuations within these very long motions, which reveals the constituent motions unambiguously. The conditional streamwise energy spectra of the Reynolds shear stress and the wall-normal fluctuations, corresponding exclusively to the superstructure region, are found to exhibit the well-known distance-from-the-wall scaling in the intermediate-scale range. It suggests that geometrically self-similar motions are the constituent motions of these very-large-scale structures. Investigation of the spatial organization of the wall-normal momentum-carrying eddies, within the superstructures, also lends empirical support to the concatenation hypothesis for the formation of these structures. The association between the superstructures and self-similar motions is reaffirmed on comparing the vertical coherence of the Reynolds-shear-stress-carrying motions, by computing conditionally averaged two-point correlations, which are found to match with the mean correlations. The mean vertical coherence of these motions, investigated for the log region across three decades of Reynolds numbers, exhibits a unique distance-from-the-wall scaling invariant with Reynolds number. The findings support modelling of these dynamically significant motions via data-driven coherent structure-based models.