Miracle-dz/UniFAR-FTUs huggingface dataset:
Building-block-flow wall model for large-eddy simulation

A wall model for large-eddy simulation (LES) is proposed by devising the ﬂow as a collection of building blocks. The core assumption of the model is that a ﬁnite set of simple canonical ﬂows contains the essential physics to predict the wall stress in more complex scenarios. The model is constructed to predict zero/favorable/adverse mean-pressure-gradient wall turbulence, separation, statistically unsteady turbulence with mean-ﬂow three-dimensionality, and laminar ﬂow. The approach is implemented using two types of artiﬁcial neural networks: a classiﬁer, which identiﬁes the contribution of each building block in the ﬂow; and a predictor, which estimates the wall stress via combination of the building-block ﬂows. The training data are directly obtained from wall-modeled LES optimized to obtain the correct mean quantities. This approach guarantees the consistency of the training data with the numerical discretization and the gridding strategy. The output of the model is accompanied by a conﬁdence score in the prediction. The latter aids the detection of areas where the model underperforms, such as ﬂow regions that are not representative of the building blocks used to train the model. The model is validated in canonical ﬂows (laminar/turbulent boundary layers, turbulent channels, turbulent Poiseuille-Couette, turbulent pipe) and two realistic aircraft conﬁgurations: the NASA Common Research Model High-lift and NASA Juncture Flow Experiment. It is shown that the building-block-ﬂow wall model outperforms (or matches) the predictions by an equilibrium wall model. It is also concluded that further improvements in WMLES should involve advances in SGS modeling.
The First 3$\pi$ 3D Map of ISM Dust Temperature

We present the first large-scale 3D map of interstellar dust temperature. We build upon existing 3D reddening maps derived from starlight absorption (Bayestar19), covering 3/4 of the sky. Starting with the column density for each of 500 million 3D voxels, we propose a temperature and emissivity power-law slope ($\beta$) for each of them, and integrate along the line of sight to synthesize an emission map in five frequency bands observed by \emph{Planck} and \emph{IRAS}. The reconstructed emission map is constrained to match observations on a $10'$ scale, and does so with good fidelity. We produce 3D temperature maps at resolutions of $110', 55', $and $27'$. We assess performance on Cepheus, a dust cloud with two distinct components along the line of sight, and find distinct temperatures for the two components. We thus show that this methodology has enough precision to constrain clouds with different temperature along the line of sight up to $1-\sigma$ error. This would be an important result for dust frequency decorrelation foreground analysis for cosmic microwave background experiments, which would be impacted by a line-of-sight with varying temperature and magnetic field components. In addition to $T$ and $\beta$, we constrain the conversion factor between emission optical depth and reddening. This conversion factor is assumed to be constant in commonly used emission-based reddening maps. However, this work shows a factor of two variation that may prove significant for some applications.
Logical Tasks for Measuring Extrapolation and Rule Comprehension

Logical reasoning is essential in a variety of human activities. A representative example of a logical task is mathematics. Recent large-scale models trained on large datasets have been successful in various fields, but their reasoning ability in arithmetic tasks is limited, which we reproduce experimentally. Here, we recast this limitation as not unique to mathematics but common to tasks that require logical operations. We then propose a new set of tasks, termed logical tasks, which will be the next challenge to address. This higher point of view helps the development of inductive biases that have broad impact beyond the solution of individual tasks. We define and characterize logical tasks and discuss system requirements for their solution. Furthermore, we discuss the relevance of logical tasks to concepts such as extrapolation, explainability, and inductive bias. Finally, we provide directions for solving logical tasks.
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.
Extending the Neural Additive Model for Survival Analysis with EHR Data

With increasing interest in applying machine learning to develop healthcare solutions, there is a desire to create interpretable deep learning models for survival analysis. In this paper, we extend the Neural Additive Model (NAM) by incorporating pairwise feature interaction networks and equip these models with loss functions that fit both proportional and non-proportional extensions of the Cox model. We show that within this extended framework, we can construct non-proportional hazard models, which we call TimeNAM, that significantly improve performance over the standard NAM model architecture on benchmark survival datasets. We apply these model architectures to data from the Electronic Health Record (EHR) database of Seoul National University Hospital Gangnam Center (SNUHGC) to build an interpretable neural network survival model for gastric cancer prediction. We demonstrate that on both benchmark survival analysis datasets, as well as on our gastric cancer dataset, our model architectures yield performance that matches, or surpasses, the current state-of-the-art black-box methods.
Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics

Stressors are related to depression, but this relationship is complex. We investigate the relationship between open-ended text responses about stressors and depressive symptoms across gender and racial/ethnic groups. First, we use topic models and other NLP tools to find thematic and vocabulary differences when reporting stressors across demographic groups. We train language models using self-reported stressors to predict depressive symptoms, finding a relationship between stressors and depression. Finally, we find that differences in stressors translate to downstream performance differences across demographic groups.
YORO - Lightweight End to End Visual Grounding

We present YORO - a multi-modal transformer encoder-only architecture for the Visual Grounding (VG) task. This task involves localizing, in an image, an object referred via natural language. Unlike the recent trend in the literature of using multi-stage approaches that sacrifice speed for accuracy, YORO seeks a better trade-off between speed an accuracy by embracing a single-stage design, without CNN backbone. YORO consumes natural language queries, image patches, and learnable detection tokens and predicts coordinates of the referred object, using a single transformer encoder. To assist the alignment between text and visual objects, a novel patch-text alignment loss is proposed. Extensive experiments are conducted on 5 different datasets with ablations on architecture design choices. YORO is shown to support real-time inference and outperform all approaches in this class (single-stage methods) by large margins. It is also the fastest VG model and achieves the best speed/accuracy trade-off in the literature.
Physical properties of dense cores in Orion B9

Aims. We aim to determine the physical and chemical properties of the starless and protostellar cores in Orion B9, which represents a relatively quiescent star-forming region in Orion B. Methods. We observed the NH3 (J, K) = (1, 1) and (2, 2) inversion lines and the N2H + (3−2) rotational lines, with the Effelsberg 100-m and APEX telescopes, respectively, towards the submillimetre peak positions in Orion B9. These data are used in conjunction with our APEX/LABOCA 870 μm dust continuum data of the region. Results. The gas kinetic temperature in the cores derived from the NH3 data is between ∼9.4−13.9 K. The non-thermal velocity dispersion is subsonic in most of the cores. The non-thermal linewidth in protostellar cores appears to increase with increasing bolometric luminosity. The core masses, ∼2−8 M� , are very likely drawn from the same parent distribution as the core masses in Orion B North. Based on the virial parameter analysis, starless cores in the region are likely to be gravitationally bound, and thus prestellar. Some of the cores have a lower radial velocity than the systemic velocity of the region, suggesting that they are members of the “low-velocity part” of Orion B. The observed core-separation distances deviate from the corresponding random-like model distributions. The distances between the nearest neighbours are comparable to the thermal Jeans length. The fractional abundances of NH3 and N2H + in the cores are ∼1.5−9.8 × 10 −8 and ∼0.2−5.9 × 10 −10 , respectively. The NH3 abundance appears to decrease with increasing H2 column and number densities. The NH3/N2H + column density ratio is larger in starless cores than in cores with embedded protostars. Conclusions. The core population in Orion B9 is comparable in physical properties to those in nearby low-mass star-forming regions. The Orion B9 cores also seem to resemble cores found in isolation rather than those associated with clusters. Moreover, because the cores may not be randomly distributed within the region (contrary to what was suggested in our Paper I), it is unclear whether the origin of cores could be explained by turbulent fragmentation. On the other hand, many of the core properties conform to the picture of dynamic core evolution. The Orion B9 region has probably been influenced by the feedback from the nearby Ori OB 1b group, and the fragmentation of the parental cloud into cores could be caused by gravitational instability.
Clustering phenomena in low dimensions for a boundary Yamabe problem

We consider the classical geometric problem of prescribing the scalar and boundary mean curvatures via conformal deformation of the metric on a n−dimensional compact Riemannian manifold. We deal with the case of negative scalar curvature and positive boundary mean curvature. It is known that if n = 3 all the blow-up points are isolated and simple. In this work we prove that, for a linear perturbation, this is not true anymore in low dimensions 4 ≤ n ≤ 7. In particular, we construct a solution with a clustering blow-up boundary point (i.e. non-isolated), which is non-umbilic and is a local minimizer of the norm of the trace-free second fundamental form of the boundary.
The LP-Newton Method and Conic Optimization

We propose that the LP-Newton method can be used to solve conic LPs over a 'conic interval', whenever linear optimization over an unconstrained conic interval is easy. In particular, if $\leq_\mathcal{K}$ is the partial order induced by a proper convex cone $\mathcal{K}$, then conic LPs over $[l,u]_{\mathcal{K}}=\{ l\leq_\mathcal{K} x\leq_\mathcal{K} u\}$ can be solved whenever optimizing a linear function over $[l,u]_{\mathcal{K}}$ is fast. This generalizes the result for the case of $\mathcal{K}=\mathbb{R}^n_+$ that was originally proposed for using the method. Specifically, we show how to adapt this method for both SOCP and SDP problems and illustrate the method with a few experiments.
Software Reuse in Organizations: A Survey in Moroccan Software Industry Context

A good software reuse process, contributes towards accelerating time to market of software products and reducing costs and efforts. Research raised that the adoption of software reuse is becoming a necessity for organizations to compete against others and stand out amongst them. Thereby many organizations try to invest in software reuse by identifying best reuse techniques, methods, and practices. This paper presents a complete survey method with findings backed up with statistics. The goal is to evaluate in practice the adoption of techniques, methods and practices proposed by academics, and highlight the motivations as well as the difficulties to implement a successful reuse program by different types of organizations in Morocco. A descriptive survey of 84 software participants with different backgrounds (software managers, analysts, engineers and software developers) has been conducted, in order to identify the current state of software reuse in practice inside the Moroccan software industry. A complete survey method is presented, including the process, data collection, and analysis phases, as well as a discussion on the study’s validity.The survey consisted of 28 questions, grouped into 4 sections and backed up our findings with statistics. A discussion was also given based on the comparison of the survey results with related literature and presented the main of our finding. It has been shown through statistics that even if most of organizations are aware of the strengths and benefits of software reuse, they don't consider reuse as part of the company's culture, and most of them didn’t implement efficient software reuse programs and not apply the most effective reuse strategies, methods and practices.
A Survey of Deep Causal Models and Their Industrial Applications

The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continued advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models, and its core contributions are as follows: 1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; 2) we outline some typical applications of causal effect estimation to industry; 3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.
Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels

As a promising solution of reducing annotation cost, training multi-label models with partial positive labels (MLR-PPL), in which merely few positive labels are known while other are missing, attracts increasing attention. Due to the absence of any negative labels, previous works regard unknown labels as negative and adopt traditional MLR algorithms. To reject noisy labels, recent works regard large loss samples as noise but ignore the semantic correlation different multi-label images. In this work, we propose to explore semantic correlation among different images to facilitate the MLR-PPL task. Specifically, we design a unified framework, Category-Adaptive Label Discovery and Noise Rejection, that discovers unknown labels and rejects noisy labels for each category in an adaptive manner. The framework consists of two complementary modules: (1) Category-Adaptive Label Discovery module first measures the semantic similarity between positive samples and then complement unknown labels with high similarities; (2) Category-Adaptive Noise Rejection module first computes the sample weights based on semantic similarities from different samples and then discards noisy labels with low weights. Besides, we propose a novel category-adaptive threshold updating that adaptively adjusts the threshold, to avoid the time-consuming manual tuning process. Extensive experiments demonstrate that our proposed method consistently outperforms current leading algorithms.
Self-training of Machine Learning Models for Liver Histopathology: Generalization under Clinical Shifts

Histopathology images are gigapixel-sized and include features and information at diﬀerent resolutions. Collecting annotations in histopathology requires highly specialized pathologists, mak-ing it expensive and time-consuming. Self-training can alleviate annotation constraints by learning from both labeled and unlabeled data, reducing the amount of annotations required from pathologists. We study the design of teacher-student self-training systems for Non-alcoholic Steatohepatitis (NASH) using clinical histopathology datasets with limited annotations. We evaluate the models on in-distribution and out-of-distribution test data under clinical data shifts. We demonstrate that through self-training, the best student model statistically outperforms the teacher with a 3% absolute diﬀerence on the macro F1 score. The best student model also approaches the performance of a fully supervised model trained with twice as many annotations.
How the form of weighted networks impacts quantum reservoir computation

Quantum extreme reservoir computation (QERC) is a versatile quantum neural network model that combines the concepts of extreme machine learning with quantum reservoir computation. Key to QERC is the generation of a complex quantum reservoir (feature space) that does not need to be optimized for different problem instances. Originally, a periodically-driven system Hamiltonian dynamics was employed as the quantum feature map. In this work we capture how the quantum feature map is generated as the number of time-steps of the dynamics increases by a method to characterize unitary matrices in the form of weighted networks. Furthermore, to identify the key properties of the feature map that has sufficiently grown, we evaluate it with various weighted network models that could be used for the quantum reservoir in image classification situations. At last, we show how a simple Hamiltonian model based on a disordered discrete time crystal with its simple implementation route provides nearly-optimal performance while removing the necessity of programming of the quantum processor gate by gate.
ON THE CONSERVATION OF THE VERTICAL ACTION IN GALACTIC DISKS

We employ high-resolution N-body simulations of isolated spiral galaxy models, from low-amplitude, multi-armed galaxies to Milky Way-like disks, to estimate the vertical action of ensembles of stars in an axisymmetrical potential. In the multi-armed galaxy the low-amplitude arms represent tiny perturbations of the potential, hence the vertical action for a set of stars is conserved, although after several orbital periods of revolution the conservation degrades significantly. For a Milky Way-like galaxy with vigorous spiral activity and the formation of a bar, our results show that the potential is far from steady, implying that the action is not a constant of motion. Furthermore, because of the presence of high-amplitude arms and the bar, considerable in-plane and vertical heating occurs that forces stars to deviate from near-circular orbits, reducing the degree at which the actions are conserved for individual stars, in agreement with previous results, but also for ensembles of stars. If confirmed, this result has several implications, including the assertion that the thick disk of our Galaxy forms by radial migration of stars, under the assumption of the conservation of the action describing the vertical motion of stars.
(Optimal) Online Bipartite Matching with Degree Information

We propose a model for online graph problems where algorithms are given access to an oracle that predicts (e.g., based on modeling assumptions or on past data) the degrees of nodes in the graph. Within this model, we study the classic problem of online bipartite matching, and a natural greedy matching algorithm called MinPredictedDegree, which uses predictions of the degrees of offline nodes. For the bipartite version of a stochastic graph model due to Chung, Lu, and Vu where the expected values of the offline degrees are known and used as predictions, we show that MinPredictedDegree stochastically dominates any other online algorithm, i.e., it is optimal for graphs drawn from this model. Since the"symmetric"version of the model, where all online nodes are identical, is a special case of the well-studied"known i.i. d. model", it follows that the competitive ratio of MinPredictedDegree on such inputs is at least 0.7299. For the special case of graphs with power law degree distributions, we show that MinPredictedDegree frequently produces matchings almost as large as the true maximum matching on such graphs. We complement these results with an extensive empirical evaluation showing that MinPredictedDegree compares favorably to state-of-the-art online algorithms for online matching.
Cosmic Evolution of Gas and Star Formation

Atacama Large Millimeter/submillimeter Array (ALMA) observations of the long-wavelength dust continuum are used to estimate the gas masses in a sample of 708 star-forming galaxies at z = 0.3−4.5. We determine the dependence of gas masses and star formation efficiencies (SFEs; SFR per unit gas mass) on redshift (z), M *, and star formation rate (SFR) relative to the main sequence (MS). We find that 70% of the increase in SFRs of the MS is due to the increased gas masses at earlier epochs, while 30% is due to increased efficiency of star formation (SF). For galaxies above the MS this is reversed—with 70% of the increased SFR relative to the MS being due to elevated SFEs. Thus, the major evolution of star formation activity at early epochs is driven by increased gas masses, while the starburst activity taking galaxies above the MS is due to enhanced triggering of star formation (likely due to galactic merging). The interstellar gas peaks at z = 2 and dominates the stellar mass down to z = 1.2. Accretion rates needed to maintain continuity of the MS evolution reach >100 M ⊙ yr−1 at z > 2. The galactic gas contents are likely the driving determinant for both the rise in SF and AGN activity from z = 5 to their peak at z = 2 and subsequent fall at lower z. We suggest that for self-gravitating clouds with supersonic turbulence, cloud collisions and the filamentary structure of the clouds regulate the star formation activity.