Miracle-dz/UniFAR-FTUs huggingface dataset:
Forced plume in a stratified fluid

A turbulent buoyant jet operating in a linearly stratified fluid is investigated. By requiring similarity forms for the velocity, density difference, and shear stress and by adopting an integral representation of the governing equations, the rate of entrainment of ambient fluid into the plume is found to be an explicit function of the dependent variables. The entrainment is a function of the Reynolds stress, the form of the similarity profiles, and the local mean densimetric Froude number. The Reynolds stress term is shown to be related to the constant obtained if the entrainment rate is assumed to be proportional to the mean centerline velocity. A critical parameter of the analysis is the ratio of the form of the velocity profile to the form of the density difference profile. Values of this parameter near unity seem most appropriate. Numerical solutions are compared with experimental results to suggest values for the Reynolds stress term.
Equilibrium-Independent Passivity of Power Systems: A Link Between Classical and Two-Axis Synchronous Generator Models

We study the equilibrium-independent (EI) passivity of a nonlinear power system composed of two-axis generator models. The model of our interest consists of a feedback inter-connection of linear and nonlinear subsystems, called mechanical and electromagnetic subsystems. We mathematically prove the following three facts by analyzing the nonlinear electromagnetic subsystem. First, a lossless transmission network is necessary for the EI passivity of the electromagnetic subsystem. Second, the convexity of a strain energy function characterizes the largest set of equilibria over which the electromagnetic subsystem is EI passive. Finally, we prove that the strain energy function for the network of the two-axis generator models is convex if and only if its flux linkage dynamics is stable, and the strain energy function for the network of the classical generator models derived by singular perturbation approximation of the flux linkage dynamics is convex. Numerical simulation of the IEEE 9-bus power system model demonstrates the practical implications of the various mathematical results. In particular, we validate that the convex domain of the strain energy function over which the electromagnetic subsystem is EI passive is almost identical to the set of all stable equilibria. This result is also generalized to lossy power systems based on our finding that the convexity of the strain energy function is equivalent to the positive semidefiniteness of a synchronizing torque coefficient matrix.
Some qualitative properties of solutions to a reaction-diffusion equation with weighted strong reaction

We study the existence and qualitative properties of solutions to the Cauchy problem associated to the quasilinear reaction-diffusion equation $$ \partial_tu=\Delta u^m+(1+|x|)^{\sigma}u^p, $$ posed for $(x,t)\in\real^N\times(0,\infty)$, where $m>1$, $p\in(0,1)$ and $\sigma>0$. Initial data are taken to be bounded, non-negative and compactly supported. In the range when $m+p\geq2$, we prove \emph{local existence of solutions} together with a \emph{finite speed of propagation} of their supports for compactly supported initial conditions. We also show in this case that, for a given compactly supported initial condition, there exist \emph{infinitely many solutions} to the Cauchy problem, by prescribing the evolution of their interface. In the complementary range $m+p<2$, we establish new \emph{Aronson-B\'enilan estimates} satisfied by solutions to the Cauchy problem, which are of independent interest as a priori bounds for the solutions. We apply these estimates to establish \emph{infinite speed of propagation} of the supports of solutions if $m+p<2$, that is, $u(x,t)>0$ for any $x\in\real^N$, $t>0$, even in the case when the initial condition $u_0$ was compactly supported.
Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: A Preliminary Empirical Study

Evaluating the quality of generated text is a challenging task in natural language processing. This difﬁculty arises from the inherent complexity and diversity of text. Recently, OpenAI’s ChatGPT, a powerful large language model (LLM), has garnered signiﬁcant attention due to its impressive performance in various tasks. Therefore, we present this report to investigate the effectiveness of LLMs, especially ChatGPT, and explore ways to optimize their use in assessing text quality. We compared three kinds of reference-free evaluation methods based on ChatGPT or similar LLMs. The experimental results prove that ChatGPT is capable to evaluate text quality effectively from various perspectives without reference and demonstrates superior performance than most existing automatic metrics. In particular, the Explicit Score, which utilizes ChatGPT to generate a numeric score measuring text quality, is the most effective and reliable method among the three exploited approaches. However, directly comparing the quality of two texts using ChatGPT may lead to suboptimal results. We hope this report will provide valuable insights into selecting appropriate meth-ods for evaluating text quality with LLMs such as ChatGPT. We have released the used data 1 .
Data-Driven Safe Controller Synthesis for Deterministic Systems: A Posteriori Method With Validation Tests

In this work, we investigate the data-driven safe control synthesis problem for unknown dynamic systems. We first formulate the safety synthesis problem as a robust convex program (RCP) based on notion of control barrier function. To resolve the issue of unknown system dynamic, we follow the existing approach by converting the RCP to a scenario convex program (SCP) by randomly collecting finite samples of system trajectory. However, to improve the sample efficiency to achieve a desired confidence bound, we provide a new posteriori method with validation tests. Specifically, after collecting a set of data for the SCP, we further collect another set of independent validate data as posterior information to test the obtained solution. We derive a new overall confidence bound for the safety of the controller that connects the original sample data, the support constraints, and the validation data. The efficiency of the proposed approach is illustrated by a case study of room temperature control. We show that, compared with existing methods, the proposed approach can significantly reduce the required number of sample data to achieve a desired confidence bound.
Data-Driven Output Regulation Using Single-Gain Tuning Regulators

Current approaches to data-driven control are geared towards optimal performance, and often integrate aspects of machine learning and large-scale convex optimization, leading to complex implementations. In many applications, it may be preferable to sacrifice performance to obtain significantly simpler controller designs. We focus here on the problem of output regulation for linear systems, and revisit the so-called tuning regulator of E. J. Davison as a minimal-order data-driven design for tracking and disturbance rejection. Our proposed modification of the tuning regulator relies only on samples of the open-loop plant frequency response for design, is tuned online by adjusting a single scalar gain, and comes with a guaranteed margin of stability; this provides a faithful extension of tuning procedures for SISO integral controllers to MIMO systems with mixed constant and harmonic disturbances. The results are illustrated via application to a fourtank water control process.
Nonlocal energy functionals and determinantal point processes on non-smooth domains

Given a nonnegative integrable function $J$ on $\mathbb{R}^n$, we relate the asymptotic properties of the nonlocal energy functional \begin{equation*} \int_{\Omega} \int_{\Omega^c} J \bigg(\frac{x-y}{t}\bigg) \ dx dy \end{equation*} as $t \to 0^+$ with the boundary properties of a given domain $\Omega \subset \mathbb{R}^n$. Then, we use these asymptotic properties to study the fluctuations of many determinantal point processes, and show that their variances measure the Minkowski dimension of $\partial \Omega$.
Searching for dark matter subhalos in the Fermi-LAT catalog with Bayesian neural networks

About a third of the γ-ray sources detected by the Fermi Large Area Telescope (Fermi-LAT) remain unidentified, and some of these could be exotic objects such as dark matter subhalos. We present a search for these sources using Bayesian neural network classification methods applied to the latest 4FGL-DR3 Fermi-LAT catalog. We first simulate the γ-ray properties of dark matter subhalos using models from N-body simulations and semi-analytical approaches to the subhalo distribution. We then assess the detectability of this sample in the 4FGL-DR3 catalog using the Fermi-LAT analysis tools. We train our Bayesian neural network to identify candidate dark matter subhalos among the unidentified sources in the 4FGL-DR3 catalog. Our results allow us to derive conservative bounds on the dark matter annihilation cross section by excluding unidentified sources classified as astrophysical-like by our networks. We estimate the number of candidate dark matter subhalos for different dark matter masses and provide a publicly available list for further investigation. Our bounds on the dark matter annihilation cross section are comparable to previous results and become particularly competitive at high dark matter masses.
Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities - A Framework and Dataset Creation Approach

This contribution presents the TOMIE framework (Tracking Of Multiple Industrial Entities), a framework for the continuous tracking of industrial entities (e.g., pallets, crates, barrels) over a network of, in this example, six RGB cameras. This framework makes use of multiple sensors, data pipelines, and data annotation procedures, and is described in detail in this contribution. With the vision of a fully automated tracking system for industrial entities in mind, it enables researchers to efficiently capture high-quality data in an industrial setting. Using this framework, an image dataset, the TOMIE dataset, is created, which at the same time is used to gauge the framework’s validity. This dataset contains annotation files for 112,860 frames and 640,936 entity instances that are captured from a set of six cameras that perceive a large indoor space. This dataset out-scales comparable datasets by a factor of four and is made up of scenarios, drawn from industrial applications from the sector of warehousing. Three tracking algorithms, namely ByteTrack, Bot-Sort, and SiamMOT, are applied to this dataset, serving as a proof-of-concept and providing tracking results that are comparable to the state of the art.
Neuro-Symbolic Execution of Generic Source Code

Can a Python program be executed statement-by-statement by neural networks composed according to the source code? We formulate the Neuro-Symbolic Execution Problem and introduce Neural Interpretation (NI), the first neural model for the execution of generic source code that allows missing definitions. NI preserves source code structure, where every variable has a vector encoding, and every function executes a neural network. NI is a novel neural model of computers with a compiler architecture that can assemble neural layers"programmed"by source code. NI is the first neural model capable of executing Py150 dataset programs, including library functions without concrete inputs, and it can be trained with flexible code understanding objectives. We demonstrate white-box execution without concrete inputs for variable misuse localization and repair.
VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts

In the field of historical manuscript research, scholars frequently encounter novel symbols in ancient texts, investing considerable effort in their identification and documentation. Although existing object detection methods achieve impressive performance on known categories, they struggle to recognize novel symbols without retraining. To address this limitation, we propose a Visually Guided Text Spotting (VGTS) approach that accurately spots novel characters using just one annotated support sample. The core of VGTS is a spatial alignment module consisting of a Dual Spatial Attention (DSA) block and a Geometric Matching (GM) block. The DSA block aims to identify, focus on, and learn discriminative spatial regions in the support and query images, mimicking the human visual spotting process. It first refines the support image by analyzing inter-channel relationships to identify critical areas, and then refines the query image by focusing on informative key points. The GM block, on the other hand, establishes the spatial correspondence between the two images, enabling accurate localization of the target character in the query image. To tackle the example imbalance problem in low-resource spotting tasks, we develop a novel torus loss function that enhances the discriminative power of the embedding space for distance metric learning. To further validate our approach, we introduce a new dataset featuring ancient Dongba hieroglyphics (DBH) associated with the Naxi minority of China. Extensive experiments on the DBH dataset and other public datasets, including EGY, VML-HD, TKH, and NC, show that VGTS consistently surpasses state-of-the-art methods. The proposed framework exhibits great potential for application in historical manuscript text spotting, enabling scholars to efficiently identify and document novel symbols with minimal annotation effort.
An Extension of Duality-Stability Relations to Nonconvex Optimization Problems

By an effective extension of the conjugate function concept a general framework for duality-stability relations in nonconvex optimization problems can be studied. The results obtained show strong correspondences with the duality theory for convex minimization problems. In specializations to mathematical programming problems the canonical Lagrangian of the model appears as the extended Lagrangian considered in exterior penalty function methods.
Factors Analysis for E-Services Adoption in Jordan: A Technology Acceptance Study

This work investigates the factors affecting the adoption and acceptance of e-services in Jordan. In which, requirements for successful e-services implementation is addressed, and significant barriers being faced by e-services users and developers are described. Results have indicated that ease of use, usefulness, credibility, self-efficacy and trust had positive effects on the e-services adoption in Jordan. However, risk and cost at the other spectrum had a significant negative effect. Outcomes of this research have drawn a number of practical implications achieving positive attitude towards e-services adoption and developing the employment of e-services applications. Such implications include; promoting its trust, decreasing the perceived risk and cost, overcoming services development obstacles, considering user’s capability concerns and increasing the awareness of e-services usefulness and ease of use.
Predictive Heterogeneity: Measures and Applications

As an intrinsic and fundamental property of big data, data heterogeneity exists in a variety of real-world applications, such as precision medicine, autonomous driving, financial applications, etc. For machine learning algorithms, the ignorance of data heterogeneity will greatly hurt the generalization performance and the algorithmic fairness, since the prediction mechanisms among different sub-populations are likely to differ from each other. In this work, we focus on the data heterogeneity that affects the prediction of machine learning models, and firstly propose the \emph{usable predictive heterogeneity}, which takes into account the model capacity and computational constraints. We prove that it can be reliably estimated from finite data with probably approximately correct (PAC) bounds. Additionally, we design a bi-level optimization algorithm to explore the usable predictive heterogeneity from data. Empirically, the explored heterogeneity provides insights for sub-population divisions in income prediction, crop yield prediction and image classification tasks, and leveraging such heterogeneity benefits the out-of-distribution generalization performance.
First ALMA Maps of Cosmic-Ray Ionization Rate in High-mass Star-forming Regions

Low-energy cosmic rays (<1 TeV) are a pivotal source of ionization of the interstellar medium, where they play a central role in determining the gas chemical composition and drastically influence the formation of stars and planets. Over the past few decades, H3 + absorption line observations in diffuse clouds have provided reliable estimates of the cosmic-ray ionization rate relative to H2 ( ζH2ion ). However, in denser clouds, where stars and planets form, this method is often inefficient due to the lack of H3 + rotational transitions. The ζH2ion estimates are, therefore, still provisional in this context and represent one of the least understood components when it comes to defining general models of star and planet formation. In this Letter, we present the first high-resolution maps of the ζH2ion in two high-mass clumps obtained with a new analytical approach recently proposed to estimate the ζH2ion in the densest regions of molecular clouds. We obtain 〈ζH2ion〉 that span from 3 × 10−17 to 10−16 s−1, depending on the different distribution of the main ion carriers, in excellent agreement with the most recent cosmic-ray propagation models. The cores belonging to the same parental clump show comparable ζH2ion , suggesting that the ionization properties of prestellar regions are determined by global rather than local effects. These results provide important information for the chemical and physical modeling of star-forming regions.
Stochastic gradient descent with random label noises: doubly stochastic models and inference stabilizer

Random label noises (or observational noises) widely exist in practical machine learning settings. While previous studies primarily focus on the affects of label noises to the performance of learning, our work intends to investigate the implicit regularization effects of the label noises, under mini-batch sampling settings of stochastic gradient descent (SGD), with assumptions that label noises are unbiased. Specifically, we analyze the learning dynamics of SGD over the quadratic loss with unbiased label noises, where we model the dynamics of SGD as a stochastic differentiable equation (SDE) with two diffusion terms (namely a Doubly Stochastic Model ). While the first diffusion term is caused by mini-batch sampling over the (labelnoiseless) loss gradients, as in many other works on SGD [1, 2], our model investigates the second noise term of SGD dynamics, which is caused by mini-batch sampling over the label noises, as an implicit regularizer. Our theoretical analysis finds such implicit regularizer would favor some convergence points that could stabilize model outputs against perturbation of parameters (namely inference stability). Though similar phenomenon have been investigated by Blanc et al. [3], our work doesn’t assume SGD as an Ornstein-Uhlenbeck like process and achieve a more generalizable result with convergence of approximation proved. To validate our analysis, we design two sets of empirical studies to analyze the implicit regularizer of SGD with unbiased random label noises for deep neural networks training and linear regression. Our first experiment studies the noisy self-distillation tricks for deep learning, where student networks are trained using the outputs from well-trained teachers with additive unbiased random label noises. Our experiment shows that the implicit regularizer caused by the label noises tend to select models with improved inference stability.
In pituitary adenoma surgery, no vs. perioperative hydrocortisone was noninferior for new-onset adrenal insufficiency

SOURCE CITATION Guo X, Zhang D, Pang H, et al. Safety of withholding perioperative hydrocortisone for patients with pituitary adenomas with an intact hypothalamus-pituitary-adrenal axis: a randomized clinical trial. JAMA Netw Open. 2022;5:e2242221. 36383383.
Micro-column testing of the dynamic adsorption of Pb by Fe-Mg clay–quartz beds

Abstract Clays have been used widely as sorbents of potentially toxic elements, especially Pb. However, their adsorption efficiency has been studied mostly under static experimental conditions, which are labour-intensive. In this study, dynamic adsorption experiments were established using micro-columns to assess the adsorption behaviour of natural Fe-Mg-rich clays for Pb ions by varying the inlet solution pH, Pb concentration and flow rate. The clays, consisting of varying amounts of palygorskite and Fe-smectite, were diluted with quartz (Qz) sand to enhance their permeability. Greater adsorption capacity was observed for the mixed palygorskite/Fe-smectite-rich clay beds (45.2 ± 0.01 mg g–1) followed by the Fe-smectite-rich (35.7 ± 0.07 mg g–1) and palygorskite-rich (20.0 ± 0.03 mg g–1) clay beds. The results indicated that the greater adsorption efficiency of palygorskite/Fe-smectite-rich clays could be attributed to synergistic effects due to the coexistence of palygorskite and Fe-smectite clay phases, which have distinct physicochemical characteristics. Moreover, the results obtained showed good agreement with the static experiments, implying that the use of micro-columns could describe adsorption adequately over shorter experimental times.
Food knowledge level among Tanzanian women of childbearing age: developing a score for the food knowledge questionnaire

Abstract Food knowledge (FK) is one of the factors that contribute to malnutrition conditions in developing countries, together with food safety, food security and food access. FK is defined as ‘the competence to understand healthy nutrition concepts’; it impacts individuals’ life due to its relationship with food behaviour and eating habits. Therefore, acting on FK can represent a starting point for improving the health status of vulnerable populations. The authors present a total score of an FK questionnaire (FKQ) and its relation to the socio-demographic characteristics of a specific target population: Tanzanian women of childbearing age. The results of the manuscript complement evidence of construct validity of the FKQ by providing an algorithm to compute a total score as a measure of FK. The strength of this tool, and its score, lies in the fact that the questionnaire has been validated and is easy to administer.
Muttalip green clay and its ultraviolet and infrared absorption and reflection

Abstract The search for sufficiently high-quality clays for use in the tile industry represents a significant challenge. This study aimed to prepare Muttalip green clay tiles with flexural strengths adhering to the TS EN 1304:2016 standard. Muttalip green clay was subjected to sieving, vacuum pressed, dried and then baked to obtain galette tiles. The tiles were analysed subsequently using optical techniques, including X-ray diffraction, X-ray fluorescence spectrometry and field-emission scanning electron microscopy. The influence of various firing temperatures (900, 950 and 1000°C) on the flexural strength of the tiles was also investigated. The tiles obtained at 950°C exhibited a maximum flexural strength of 153.91 kg cm–2. Fourier-transform infrared spectroscopy revealed that the tiles reflect ultraviolet A radiation and absorb infrared radiation. Muttalip green clay is a suitable material for preparing tiles for roofing applications.