Miracle-dz/UniFAR-FTUs huggingface dataset:
LeMMINGs – II. The e-MERLIN legacy survey of nearby galaxies. The deepest radio view of the Palomar sample on parsec scale

We present the second data release of high-resolution (≤0.2 arcsec) 1.5-GHz radio images of 177 nearby galaxies from the Palomar sample, observed with the e-MERLIN array, as part of the Legacy e-MERLIN Multi-band Imaging of Nearby Galaxies Sample (LeMMINGs) survey. Together with the 103 targets of the first LeMMINGs data release, this represents a complete sample of 280 local active (LINER and Seyfert) and inactive galaxies (H ii galaxies and absorption line galaxies, ALG). This large program is the deepest radio survey of the local Universe, ≳1017.6 W Hz−1, regardless of the host and nuclear type: we detect radio emission ≳0.25 mJy beam−1 for 125/280 galaxies (44.6 per cent) with sizes of typically ≲100 pc. Of those 125, 106 targets show a core which coincides within 1.2 arcsec with the optical nucleus. Although we observed mostly cores, around one third of the detected galaxies features jetted morphologies. The detected radio core luminosities of the sample range between ∼1034 and 1040 erg s−1. LINERs and Seyferts are the most luminous sources, whereas H ii galaxies are the least. LINERs show FR I-like core-brightened radio structures while Seyferts reveal the highest fraction of symmetric morphologies. The majority of H ii galaxies have single radio core or complex extended structures, which probably conceal a nuclear starburst and/or a weak active nucleus (seven of them show clear jets). ALGs, which are typically found in evolved ellipticals, although the least numerous, exhibit on average the most luminous radio structures, similar to LINERs.
Self Supervised Learning for Object Localisation in 3D Tomographic Images

While a lot of work is dedicated to self-supervised learning, most of it is dealing with 2D images of natural scenes and objects. In this paper, we focus on \textit{volumetric} images obtained by means of the X-Ray Computed Tomography (CT). We describe two pretext training tasks which are designed taking into account the specific properties of volumetric data. We propose two ways to transfer a trained network to the downstream task of object localization with a zero amount of manual markup. Despite its simplicity, the proposed method shows its applicability to practical tasks of object localization and data reduction.
Optimization-based Trajectory Planning for Tethered Marsupial Robots

This paper presents a non-linear optimization method for trajectory planning in a marsupial robot configuration. Particularly, the paper addresses the planning problem of an unmanned aerial vehicle (UAV) linked to an unmanned ground vehicle (UGV) by means of a tether. The result is a collision-free trajectory for UAV and tether, assuming the UGV position is static. The optimizer takes into account constraints related to the UAV, UGV and tether positions, obstacles and temporal aspects of the motion such as limited robot velocities and accelerations, and finally the tether state, which is not required to be tense. The problem is formulated in a weighted multi-objective optimization framework. Results from simulated scenarios demonstrate that the approach is able to generate obstacle free and smooth trajectories for the UAV and tether from the marsupial system.
A Dust Trap in the Young Multiple System HD 34700

Millimeter observations of disks around young stars reveal substructures indicative of gas pressure traps that may aid grain growth and planet formation. We present Submillimeter Array observations of HD 34700: two Herbig Ae stars in a close binary system (Aa/Ab, ∼0.25 au), surrounded by a disk presenting a large cavity and spiral arms seen in scattered light, and two distant, lower-mass companions. These observations include 1.3 mm continuum emission and the 12CO 2–1 line at ∼05 (178 au) resolution. They resolve a prominent azimuthal asymmetry in the continuum and Keplerian rotation of a circumbinary disk in the 12CO line. The asymmetry is located at a radius of 155+11 −7 au, consistent with the edge of the scattered-light cavity, being resolved in both radius (72 +14 −15 au) and azimuth (FWHM = 64°+8 −7). The strong asymmetry in millimeter continuum emission could be evidence for a dust trap, together with the more symmetric morphology of 12CO emission and small grains. We hypothesize an unseen circumbinary companion responsible for the cavity in scattered light and creating a vortex at the cavity edge that manifests in dust trapping. The disk mass has limitations imposed by the detection of 12CO and nondetection of 13CO. We discuss its consequences for the potential past gravitational instability of this system, likely accounting for the rapid formation of a circumbinary companion. We also report the discovery of resolved continuum emission associated with HD 34700B (projected separation ∼1850 au), which we explain through a circumstellar disk.
Network-Constrained Transactive Control for Multi- Microgrids-based Distribution Networks with SOPs.

Different from most transactive control studies only focusing on economic aspect, this paper develops a novel network-constrained transactive control (NTC) framework that can address both economic and secure issues for a multi-microgrids-based distribution network considering uncertainties. In particular, we innovatively integrate a transactive energy market with the novel power-electronics device (i.e., soft open point) based AC power flow regulation technique to improve economic benefits for individual microgrids and meanwhile ensure the security of the entire distribution network. In this framework, a dynamic two-timescale NTC model consisting of slow-timescale pre-scheduling and real-time scheduling stages is formulated to work against multiple system uncertainties. Moreover, the original bilevel game problems are transformed into single-level mixed-integer second-order cone programming problems through KKT conditions, duality, linearization and relaxation techniques to avoid iterations of transitional methods, so as to improve computational efficiency. Finally, numerical simulations on a modified 33-bus test system with 3 MGs verify the effectiveness of the proposed framework.
STReSSD: Sim-To-Real from Sound for Stochastic Dynamics

Sound is an information-rich medium that captures dynamic physical events. This work presents STReSSD, a framework that uses sound to bridge the simulation-to-reality gap for stochastic dynamics, demonstrated for the canonical case of a bouncing ball. A physically-motivated noise model is presented to capture stochastic behavior of the balls upon collision with the environment. A likelihood-free Bayesian inference framework is used to infer the parameters of the noise model, as well as a material property called the coefficient of restitution, from audio observations. The same inference framework and the calibrated stochastic simulator are then used to learn a probabilistic model of ball dynamics. The predictive capabilities of the dynamics model are tested in two robotic experiments. First, open-loop predictions anticipate probabilistic success of bouncing a ball into a cup. The second experiment integrates audio perception with a robotic arm to track and deflect a bouncing ball in real-time. We envision that this work is a step towards integrating audio-based inference for dynamic robotic tasks. Experimental results can be viewed at https://youtu.be/b7pOrgZrArk.
Optimal Online Algorithms for File-Bundle Caching and Generalization to Distributed Caching

We consider a generalization of the standard cache problem called file-bundle caching, where different queries (tasks), each containing l ≥ 1 files, sequentially arrive. An online algorithm that does not know the sequence of queries ahead of time must adaptively decide on what files to keep in the cache to incur the minimum number of cache misses. Here a cache miss refers to the case where at least one file in a query is missing among the cache files. In the special case where l = 1, this problem reduces to the standard cache problem. We first analyze the performance of the classic least recently used (LRU) algorithm in this setting and show that LRU is a near-optimal online deterministic algorithm for file-bundle caching with regard to competitive ratio. We then extend our results to a generalized (h,k)-paging problem in this file-bundle setting, where the performance of the online algorithm with a cache size k is compared to an optimal offline benchmark of a smaller cache size h < k. In this latter case, we provide a randomized O(l ln k/k-h)-competitive algorithm for our generalized (h, k)-paging problem, which can be viewed as an extension of the classic marking algorithm. We complete this result by providing a matching lower bound for the competitive ratio, indicating that the performance of this modified marking algorithm is within a factor of 2 of any randomized online algorithm. Finally, we look at the distributed version of the file-bundle caching problem where there are m ≥ 1 identical caches in the system. In this case, we show that for m = l + 1 caches, there is a deterministic distributed caching algorithm that is (l2 + l)-competitive and a randomized distributed caching algorithm that is O(l ln (2l + 1)-competitive when l ≥ 2. We also provide a general framework to devise other efficient algorithms for the distributed file-bundle caching problem and evaluate the performance of our results through simulations.
Searching for the brightest stars in galaxies outside the Local Group

This paper introduces a technique for searching for bright massive stars in galaxies beyond the Local Group. To search for massive stars, we processed the results of stellar photometry from the Hubble Space Telescope (HST) images using the DAOPHOT and DOLPHOT packages. The results of such searches are demonstrated with examples of the galaxies DDO 68, M94 and NGC 1672. In the galaxy DDO 68, the LBV star changes its brightness, and massive stars in M94 can be identified by excess in the Hα band. For the galaxy NGC 1672, we measure the distance for the first time by the TRGB method, which enabled determining the luminosities of the brightest stars, likely hypergiants, in the young star formation region. So far, we have performed stellar photometry on HST images of 320 northern sky galaxies located at a distance less than 12 Mpc. This allowed us to identify 53 galaxies with probable hypergiants. Further photometric and spectral observations of these galaxies are planned to search for massive stars.
Maximum Matchings and Popularity

Let $G$ be a bipartite graph where every node has a strict ranking of its neighbors. For every node, its preferences over neighbors extend naturally to preferences over matchings. Matching $N$ is more popular than matching $M$ if the number of nodes that prefer $N$ to $M$ is more than the number that prefer $M$ to $N$. A maximum matching $M$ in $G$ is a "popular max-matching" if there is no maximum matching in $G$ that is more popular than $M$. Such matchings are relevant in applications where the set of admissible solutions is the set of maximum matchings and we wish to find a best maximum matching as per node preferences. It is known that a popular max-matching always exists in $G$. Here we show a compact extended formulation for the popular max-matching polytope. So when there are edge costs, a min-cost popular max-matching in $G$ can be computed in polynomial time. This is in contrast to the min-cost popular matching problem which is known to be NP-hard. We also consider Pareto-optimality, which is a relaxation of popularity, and show that computing a min-cost Pareto-optimal matching/max-matching is NP-hard.
Investigating the automation of building permit checks through 3D GeoBIM information

The automation of building permits has great relevance within the worldwide digitalization efforts. The reuse of models produced by designers and the integration within geoinformation, necessary for many regulations, would make the process more effective. However, a gap is left by most of studies and pilots adopting conceptual approaches. In this paper, rather than taking a top-down approach, we departed from the data as available and made the necessary inferences, which gave the opportunity to tackle basic and common issues often preventing a smooth automatic processing. While the case study is specific in location, regulations and input models, the type of issues encountered in data integration and extraction of incomplete and partially invalid data is exemplary for automated code compliance checking in general. As such, guidelines were proposed accordingly, together with a tool to work with such models and a possible strategy to add georeferencing information to the IFC models.
Automatic Diﬀerentiation in PCF

We study the correctness of automatic diﬀerentiation (AD) in the context of a higher-order, Turing-complete language (PCF with real numbers), both in forward and reverse mode. Our main result is that, under mild hypotheses on the primitive functions included in the language, AD is almost everywhere correct, that is, it computes the derivative or gradient of the program under consideration except for a set of Lebesguemeasure zero. Stated otherwise,thereare inputs on which AD is incorrect, but theprobability of randomly choosing one suchinput is zero.Ourresultisin factmore precise, inthatthesetof failurepoints admits a more explicitdescription:for example, in case the primitive functions are just constants, addition and multiplication, the set of points where AD fails is contained in a countable union of zero sets of polynomials.
Basic level of concepts in formal concept analysis 1: formalization and utilization

ABSTRACT Basic level of concepts represents a significant, thoroughly explored psychological phenomenon in the psychology of concepts. We argue that formal concept analysis provides a convenient formal framework for considerations regarding the basic level. In this first part of our study, we present a direct formalization of a rudimentary view of basic level due to Rosch. We also demonstrate by experiments that among all formal concepts the basic level concepts represent natural and interesting ones and are therefore of interest from the data analysis viewpoint. In the second part, we shall utilize the present approach to examine existing psychological views of basic level and explore relationships among them.
Basic level of concepts and formal concept analysis 2: examination of existing basic level metrics

We exploit the framework developed in the first part of our paper to examine significant approaches to the basic level of concepts proposed in psychological literature. These approaches are mostly presented informally in the literature and we hence provide their formalization. Our formalization and experiments reveal previously unknown relationships between existing approaches to basic level, some of which contradict current psychological beliefs. We argue that from a general perspective, formalization of basic level and related phenomena in the framework of formal concept analysis is beneficial for psychological explorations themselves because it helps put them on a solid ground.
Evaluation of Classical Descriptors coupled to Support Vector Machine Classifier for Phosphate ore Screening monitoring

Phosphorus is an important and finite resource that is utilized mainly to produce phosphate fertilizers that assist in crop production. From phosphate ore to phosphate a process of beneficiation is required to remove the unnecessary minerals contains in the phosphate ore and to increase the grade concentration of mining product. The screening unit is a very important and critical step in this process. However, during this stage, many dysfunctions and anomalies can occur which impact the yield and quality of the product. Hence, it is essential to be monitored for real-time quality control. The purpose of this work is to automate surveillance and anomaly detection on the screening unit by using artificial vision techniques. Classical and supervised image classification approach has been used based on tree manual descriptors; HOG, SIFT, and LBP combined each with the support vector machine classifier. The evaluation of the three combinations shows that the HOG-SVM combination has the best trade-off between both accuracy and runtime.
SHAMan: an intelligent framework for HPC auto-tuning of I/O accelerators

Like most modern computer systems, High Performance Computing (HPC) machines integrate many highly configurable hardware devices and software components. Finding their optimal parametrization is a complex task, as the size of the parametric space and the non-linear behavior of HPC systems make hand tuning, theoretical modeling or exhaustive sampling unsuitable for most cases. Auto-tuning methods relying on black-box optimization have emerged as a promising solution for finding systems' best parametrization without making any assumption on their behaviors. In this paper, we present the architecture of an auto-tuning framework, called Smart HPC Application MANager (SHAMan), that integrates black-box optimization heuristics to find the optimal parametrization of an Input/Output (I/O) accelerator for a HPC application. We describe the conceptual and technical architecture of the framework and its native support for HPC clusters' ecosystem. We detail in depth the stand-alone optimization engine and its integration as a service provided by a Web application. We deployed and tested the framework by tuning an I/O accelerator developed by the Atos company on a HPC cluster running in production. The tuner's performance is evaluated by optimizing 90 different I/O oriented applications. We show a median improvement of 29% in speed-up compared to the default parametrization and this improvement goes up to 98% for a certain class of applications.
Multi-Label Classification: A Novel approach using decision trees for learning Label-relations and preventing cyclical dependencies: Relations Recognition and Removing Cycles (3RC)

Multi-Label Classification (MLC) is a field of machine learning, which consists of classifying data by assigning to each instance a set of labels instead of one. These labels or classes can have dependencies between them. Omit this information can affect the predictive quality of classification. Considering these dependencies or ignoring them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships between classes. While the second approach can give inconsistent predictions. There are multiple approaches designed to solve multi-label classification tasks, some of them take into consideration labels dependencies and others consider them independent. A new approach called PSI-MC proposes a novel way to learn the relations between labels without fixing a predefined structure. We propose an approach that uses the same principle as the PSI- MC, and which improves the way to eliminate cycles. Finally, we present the results of testing our new approach on four different datasets. According to four measures, our proposed approach called (3RC) is much better than binary relevance, RAKEL and MLKNN approaches.
Parents’ Postsecondary Education Expectations for Students with Autism, Intellectual Disability, and Multiple Disabilities: Findings From NLTS 2012

This study uses data from the National Longitudinal Transition Study 2012 to examine predictors at the individual, family, and school levels associated with parental expectations toward postsecondary education among students with significant support needs, including those with autism spectrum disorder, intellectual disability, and multiple disabilities. Consistent with previous studies, chi-square tests revealed a positive relation between socioeconomic status such as household income or parental education level and parental expectations. Logistic regression analyses showed that whether parents have a college degree and whether students have participated in college entrance or advanced placement tests are factors that are positively associated with parental expectations toward children’s future education.
Ancestry and TPMT-VNTR Polymorphism: Relationship with Hematological Toxicity in Uruguayan Patients with Acute Lymphoblastic Leukemia

6-Mercaptopurine (6-MP) is a thiopurine drug widely used in childhood acute lymphoblastic leukemia (ALL) therapy. Genes such as TPMT and NUDT15 have an outstanding role in 6-MP metabolism. Mutations in both genes explain a significant portion of hematological toxicities suffered by ALL Uruguayan pediatric patients. A variable number tandem repeat in the TPMT promoter (TPMT-VNTR) has been associated with TPMT expression. This VNTR has a conservative architecture (AnBmC). To explore new causes of hematological toxicities related to ALL therapy, we genotyped the TPMT-VNTR of 130 Uruguayan pediatric patients. Additionally, individual genetic ancestry was estimated by 45 ancestry-informative markers (AIMs). Hematological toxicity was measured as the number of leukopenia events and 6-MP dose along the maintenance phase. As previously reported, we found TPMT*2 and TPMT*3C alleles were associated to TPMT-VNTR A2BC and AB2C, respectively. However, contrasting with other reports, TPMT*3A allele was found in a heterogeneous genetic background in linkage equilibrium. Patients carrying more than 5 A repeats present a significant higher number of leukopenia events among patients without TPMT and/or NUDT15 variants. Native American ancestry and the number of A repeats were significantly correlated with the number of leukopenia events. However, the correlation between Native American ancestry and the number of leukopenia events was lost when the number of A repeats was considered as covariate. This suggests that TPMT-VNTR alleles are more relevant than Native American ancestry in the hematological toxicity. Our results emphasize that TPMT-VNTR may be used as a pharmacogenetic biomarker to predict 6-MP-related hematological toxicity in ALL childhood therapy.