Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning based Brain Tumour Segmentation

Artificial Intelligence has changed our outlook towards the whole world and it is regularly used to better understand all the data and information that surrounds us in our everyday lives. One such application of Artificial Intelligence in real world scenarios is extraction of data from various images and interpreting it in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application image segmentation has a variety of applications extending from complex medical field to regular pattern identification. The aim of this paper is to research about several FCNN based Semantic Segmentation techniques to develop a deep learning model that is able to segment tumours in brain MRI images to a high degree of precision and accuracy. The aim is to try several different architecture and experiment with several loss functions to improve the accuracy of our model and obtain the best model for our classification including newer loss function like dice loss function, hierarchical dice loss function cross entropy, etc.
Ammonium Ferric Citrate induced Ferroptosis in Non-Small-Cell Lung Carcinoma through the inhibition of GPX4-GSS/GSR-GGT axis activity

The morbidity and mortality rates associated with non-small-cell lung carcinoma (NSCLC) are increasing every year, placing new demands on existing therapies and drugs. Ammonium ferric citrate (AFC) is often used as a food additive for iron supplementation; however, to our knowledge, no studies have investigated whether AFC can induce ferroptosis in NSCLC. In this study, we demonstrated that specific concentrations of AFC effectively inhibit the proliferation and invasion of lung cancer cell lines in vitro using a cell proliferation inhibition test, a transwell assay, and flow cytometry analysis of cell cycle and apoptosis. In addition, AFC significantly induced oxidative stress injury in lung cancer cell lines. A quantitative polymerase chain reaction assay showed that AFC markedly reduced the expression levels of cell growth factors, negative regulators of ferroptosis, and autophagy regulators. Lastly, a protein-protein interaction analysis revealed that glutathione peroxidase 4 (GPX4) exerted its biological role through the regulation of the GSS/GSR complex and downstream GGT family proteins. When the expression of GPX4 changes, its biological activities, such as the glutathione metabolic process, cellular biosynthetic process, cellular response to chemical stimulus, and antioxidant activity, change accordingly, thereby affecting the survival quality and physiological and biochemical activities of cells. Overall, this study verifies that AFC has the biological activity of activating oxidative stress injury in NSCLC cell lines, leading to a decrease in their autophagy and inducing ferroptosis. We also confirmed that the GPX4-GSS/GSR-GGT axis is a crucial target of AFC-induced ferroptosis.
Development and validation of a comprehensive radiomics nomogram for prognostic prediction of primary hepatic sarcomatoid carcinoma after surgical resection

Objective: This study aimed to establish and validate a radiomics nomogram comprised of clinical factors and radiomics signatures to predict prognosis of primary hepatic sarcomatoid carcinoma (PHSC) patients after surgical resection. Methods: In this retrospective study, 79 patients with pathological confirmation of PHSC and underwent surgical resection were recruited. A radiomics nomogram was developed by radiomics signatures and independent clinical risk factors selecting from multivariate Cox regression. All patients were stratified as high risk and low risk by nomogram. Model performance and clinical usefulness were assessed by C-index, calibration curve, decision curve analysis (DCA) and survival curve. Results: A total of 79 PHSC were included with 1-year and 3-year overall survival rates of 63.3% and 35.4%, respectively. The least absolute shrinkage and selection operator (LASSO) method selected 3 features. Multivariate Cox analysis found six independent prognostic factors. The radiomics nomogram showed a significant prediction value with overall survival (HR: 7.111, 95%CI: 3.933-12.858, P<0.001). C-index of nomogram was 0.855 and 0.829 in training and validation set, respectively. Decision curve analysis validated the clinical utility of this nomogram. There was a significant difference in the 1-year and 3-year survival rates of stratified high-risk and low-risk patients in the whole cohort (30.6% vs. 90.1% and 5.6% vs. 62.4%, respectively, P < 0.001). Conclusion: This radiomics nomogram serve as a potential tool for predicting prognosis of PHSC after surgical resection, and help to identify high risk patients who may obtain feeble survival benefit from surgical resection.
To Partition, or Not to Partition, That is the Join Question in a Real System

An efficient implementation of a hash join has been a highly researched problem for decades. Recently, the radix join has been shown to have superior performance over the alternatives (e.g., the non-partitioned hash join), albeit on synthetic microbenchmarks. Therefore, it is unclear whether one can simply replace the hash join in an RDBMS or use the radix join as a performance booster for selected queries. If the latter, it is still unknown when one should rely on the radix join to improve performance. In this paper, we address these questions, show how to integrate the radix join in Umbra, a code-generating DBMS, and make it competitive for selective queries by introducing a Bloom-filter based semi-join reducer. We have evaluated how well it runs when used in queries from more representative workloads like TPC-H. Surprisingly, the radix join brings a noticeable improvement in only one out of all 59 joins in TPC-H. Thus, with an extensive range of microbenchmarks, we have isolated the effects of the most important workload factors and synthesized the range of values where partitioning the data for the radix join pays off. Our analysis shows that the benefit of data partitioning quickly diminishes as soon as we deviate from the optimal parameters, and even late materialization rarely helps in real workloads. We thus, conclude that integrating the radix join within a code-generating database rarely justifies the increase in code and optimizer complexity and advise against it for processing real-world workloads.
Systematicity and Arbitrariness in Language: Saussurean Rhapsody

In fact, the whole system of language is based on the irrational principle of the arbitrariness of the sign, which would lead to the worst sort of complication if applied without restriction. But the mind contrives to introduce a principle of order and regularity into certain parts of the mass of signs, and this is the role of relative motivation. Since the mechanism is but a partial correction of a system that is by nature chaotic, however, we adopt the viewpoint imposed by the very nature of language and study it as it limits arbitrariness.
Educational assortative mating and the decline of hypergamy in 27 European countries: An examination of trends through cohorts

Background: Theories of partner selection emphasize the principal role of available partners in the relationship market. As education is a common socioeconomic attribute through which individuals choose a mate, macrostructure theory highlights the importance of the asymmetric change in educational attainment seen in Europe. As women increasingly participate in tertiary education, this restructuring might result in the decline of traditional hypergamous unions. Objective: We aim to verify previous results confirming the decline of educational hypergamy and the rise of hypogamy, which has been found to be related to women’s growing educational advantage. We also wish to provide a current picture of this process in Europe by looking at the youngest cohort available during the analysis. Methods: We pooled nine waves of the European Social Survey and examined trends in seven cohorts. Apart from simple percentage differences in education, we reconstructed the indices of female educational advantage (F-index) and the prevalence of hypergamy (H-index), with provided correlation statistics and fitted linear trend lines. Results: Our results corroborated the findings of previous multi-country analyses. In nearly all selected countries, women were more present in higher education than men, resulting in a uniform increase in the female educational advantage. Parallel to this, hypergamy declined through the cohorts, which correlated with women’s emerging educational lead. Contribution: Our results verify the findings from previous years, using a newer dataset and detailed cohort perspective, and confirm the decline of hypergamy as women gain advantage in education, with only four countries out of 27 showing some remnants of a male educational lead.
Laminar Mixing Performances of a Stirred Tank Equipped with Helical Ribbon Agitator Subjected to Steady and Unsteady Rotational Speed

This paper investigates the way of improving the mixing of highly viscous Newtonian fluids in a tank using suitable operating conditions (unsteady stirring approaches). The agitator used is a non standard helical ribbon impeller fitted with an anchor at the bottom. The degree of homogeneity in the tank is followed using a conductivity method after a tracer injection. It is shown that the use of time-dependent rotational speed during the mixing process allows energy savings. For the unsteady stirring approaches tested, energy savings can reach up to 60% compared to the energy required to obtain the same mixing time with constant impeller rotational speed. Finally, a model is provided which allows the prediction of the mixing process of the agitated system with Newtonian fluids for both steady and unsteady stirring approaches.
On the origin of the mass–metallicity gradient relation in the local Universe

In addition to the well-known gas phase mass-metallicity relation (MZR), recent spatially-resolved observations have shown that local galaxies also obey a mass-metallicity gradient relation (MZGR) whereby metallicity gradients can vary systematically with galaxy mass. In this work, we use our recently-developed analytic model for metallicity distributions in galactic discs, which includes a wide range of physical processes -- radial advection, metal diffusion, cosmological accretion, and metal-enriched outflows -- to simultaneously analyse the MZR and MZGR. We show that the same physical principles govern the shape of both: centrally-peaked metal production favours steeper gradients, and this steepening is diluted by the addition of metal-poor gas, which is supplied by inward advection for low-mass galaxies and by cosmological accretion for massive galaxies. The MZR and the MZGR both bend at galaxy stellar mass $\sim 10^{10} - 10^{10.5}\,\rm{M_{\odot}}$, and we show that this feature corresponds to the transition of galaxies from the advection-dominated to the accretion-dominated regime. We also find that both the MZR and MZGR strongly suggest that low-mass galaxies preferentially lose metals entrained in their galactic winds. While this metal-enrichment of the galactic outflows is crucial for reproducing both the MZR and the MZGR at the low-mass end, we show that the flattening of gradients in massive galaxies is expected regardless of the nature of their winds.
Gaia EDR3 view on galactic globular clusters

We use the data from Gaia Early Data Release 3 (EDR3) to study the kinematic properties of Milky Way globular clusters. We measure the mean parallaxes and proper motions (PM) for 170 clusters, determine the PM dispersion profiles for more than 100 clusters, uncover rotation signatures in more than 20 objects, and find evidence for radial or tangential PM anisotropy in a dozen richest clusters. At the same time, we use the selection of cluster members to explore the reliability and limitations of the Gaia catalogue itself. We find that the formal uncertainties on parallax and PM are underestimated by $10{-}20{{\ \rm per\ cent}}$ in dense central regions even for stars that pass numerous quality filters. We explore the spatial covariance function of systematic errors, and determine a lower limit on the uncertainty of average parallaxes and PM at the level 0.01 mas and 0.025 mas yr$^{-1}$ , respectively. Finally, a comparison of mean parallaxes of clusters with distances from various literature sources suggests that the parallaxes for stars with $G>13$ (after applying the zero-point correction suggested by Lindegren et al.) are overestimated by $\sim 0.01\pm 0.003$ mas. Despite these caveats, the quality of Gaia astrometry has been significantly improved in EDR3 and provides valuable insights into the properties of star clusters.
General $\delta$-shell interactions for the two-dimensional Dirac operator: self-adjointness and approximation

In this work we consider the two-dimensional Dirac operator with general local singular interactions supported on a closed curve. A systematic study of the interaction is performed by decomposing it into a linear combination of four elementary interactions: electrostatic, Lorentz scalar, magnetic, and a fourth one which can be absorbed by using unitary transformations. We address the self-adjointness and the spectral description of the underlying Dirac operator, and moreover we describe its approximation by Dirac operators with regular potentials.
Benzyl alcohol and titanium tetrachloride - A versatile reaction system for the nonaqueous and low-temperature preparation of crystalline and luminescent titania nanoparticles

The reaction between TiCl4 and benzyl alcohol is a simple and nonaqueous procedure for the synthesis of highly crystalline titania nanoparticles at temperatures as low as 40 °C. XRD measurements prove the exclusive presence of the anatase phase. The particle growth depends strongly on temperature so that with the appropriate thermal conditions the particle size can be selectively adjusted in the range of 4−8 nm. Fine-tuning of the particle size is possible by a proper choice of the relative amounts of benzyl alcohol and titanium tetrachloride. Lowering the titanium tetrachloride concentration leads to a considerable decrease of particle size. BET measurements show particularly high surface areas, up to 345 m2/g for the smallest particles and 115 m2/g for the calcined material. TEM investigations reveal that the nanoparticles are nearly uniform in size and shape. The as-synthesized particles display only minor agglomeration, whereas the calcined material consists of completely nonagglomerated particles, wi.
Efficient Riccati recursion for optimal control problems with pure-state equality constraints

A novel approach to efficiently treat pure-state equality constraints in optimal control problems (OCPs) using a Riccati recursion algorithm is proposed. The proposed method transforms a pure-state equality constraint into a mixed state-control constraint such that the constraint is expressed by variables at a certain previous time stage. It is showed that if the solution satisfies the second-order sufficient conditions of the OCP with the transformed mixed state-control constraints, it is a local minimum of the OCP with the original pure-state constraints. A Riccati recursion algorithm is derived to solve the OCP using the transformed constraints with linear time complexity in the grid number of the horizon, in contrast to a previous approach that scales cubically with respect to the total dimension of the pure-state equality constraints. Numerical experiments on the whole-body optimal control of quadrupedal gaits that involve pure-state equality constraints owing to contact switches demonstrate the effectiveness of the proposed method over existing approaches.
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation

Measuring the similarity between two different sentential arguments is an important task in argument mining. However, one of the challenges in this field is that the dataset must be annotated using expertise in a variety of topics, making supervised learning with labeled data expensive. In this paper, we investigated whether this problem could be alleviated through transfer learning. We first adapted a pretrained language model to a domain of interest using self-supervised learning. Then, we fine-tuned the model to a task of measuring the similarity between sentences taken from different domains. Our approach improves a correlation with human-annotated similarity scores compared to competitive baseline models on the Argument Facet Similarity dataset in an unsupervised setting. Moreover, we achieve comparable performance to a fully supervised baseline model by using only about 60% of the labeled data samples. We believe that our work suggests the possibility of a generalized argument clustering model for various argumentative topics.
Error estimates for DeepOnets: A deep learning framework in infinite dimensions

DeepONets have recently been proposed as a framework for learning nonlinear operators mapping between infinite-dimensional Banach spaces. We analyze DeepONets and prove estimates on the resulting approximation and generalization errors. In particular, we extend the universal approximation property of DeepONets to include measurable mappings in non-compact spaces. By a decomposition of the error into encoding, approximation and reconstruction errors, we prove both lower and upper bounds on the total error, relating it to the spectral decay properties of the covariance operators, associated with the underlying measures. We derive almost optimal error bounds with very general affine reconstructors and with random sensor locations as well as bounds on the generalization error, using covering number arguments. We illustrate our general framework with four prototypical examples of nonlinear operators, namely those arising in a nonlinear forced ordinary differential equation, an elliptic partial differential equation (PDE) with variable coefficients and nonlinear parabolic and hyperbolic PDEs. While the approximation of arbitrary Lipschitz operators by DeepONets to accuracy $\epsilon $ is argued to suffer from a ‘curse of dimensionality’ (requiring a neural networks of exponential size in $1/\epsilon $), in contrast, for all the above concrete examples of interest, we rigorously prove that DeepONets can break this curse of dimensionality (achieving accuracy $\epsilon $ with neural networks of size that can grow algebraically in $1/\epsilon $). Thus, we demonstrate the efficient approximation of a potentially large class of operators with this machine learning framework.
Sorting Short Integers

We build boolean circuits of size O(=<2) and depth O(log(=) + < log(<)) for sorting = integers each of <-bits. We build also circuits that sort = integers each of <-bits according to their first : bits that are of size O(=<: (1 + log (=) − log(<))) and depth O(log (=)). This improves on the results of Asharov et al. [3] and resolves some of their open questions.
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent

Recent work has highlighted the role of initialization scale in determining the structure of the solutions that gradient methods converge to. In particular, it was shown that large initialization leads to the neural tangent kernel regime solution, whereas small initialization leads to so called"rich regimes". However, the initialization structure is richer than the overall scale alone and involves relative magnitudes of different weights and layers in the network. Here we show that these relative scales, which we refer to as initialization shape, play an important role in determining the learned model. We develop a novel technique for deriving the inductive bias of gradient-flow and use it to obtain closed-form implicit regularizers for multiple cases of interest.
SQAPlanner: Generating Data-Informed Software Quality Improvement Plans

Software Quality Assurance (SQA) planning aims to define proactive plans, such as defining maximum file size, to prevent the occurrence of software defects in future releases. To aid this, defect prediction models have been proposed to generate insights as the most important factors that are associated with software quality. Such insights that are derived from traditional defect models are far from actionable—i.e., practitioners still do not know what they should do or avoid to decrease the risk of having defects, and what is the risk threshold for each metric. A lack of actionable guidance and risk threshold can lead to inefficient and ineffective SQA planning processes. In this paper, we investigate the practitioners’ perceptions of current SQA planning activities, current challenges of such SQA planning activities, and propose four types of guidance to support SQA planning. We then propose and evaluate our AI-Driven SQAPlanner approach, a novel approach for generating four types of guidance and their associated risk thresholds in the form of rule-based explanations for the predictions of defect prediction models. Finally, we develop and evaluate a visualization for our SQAPlanner approach. Through the use of qualitative survey and empirical evaluation, our results lead us to conclude that SQAPlanner is needed, effective, stable, and practically applicable. We also find that 80 percent of our survey respondents perceived that our visualization is more actionable. Thus, our SQAPlanner paves a way for novel research in actionable software analytics—i.e., generating actionable guidance on what should practitioners do and not do to decrease the risk of having defects to support SQA planning.
Multiwavelength flare observations of the blazar S5 1803+784

The radio, optical, and γ-ray light curves of the blazar S5 1803+784, from the beginning of the Fermi Large Area Telescope (LAT) mission in August 2008 until December 2018, are presented. The aim of this work is to look for correlations among different wavelengths useful for further theoretical studies.We analyzed all the data collected byFermiLAT for this source, taking into account the presence of nearby sources, and we collected optical data from our own observations and public archive data to build themost complete optical and γ-ray light curve possible. Several γ-ray flares (F > 2.3 10−7ph(E > 0.1GeV) cm−2 s−1) with optical coverage were detected, all but one with corresponding optical enhancement; we also found two optical flares without a γ-ray counterpart. We obtained two Swift Target of Opportunity observations during the strong flare of 2015. Radio observations performed with VLBA and EVN through our proposals in the years 2016-2020 were analyzed to search for morphological changes after the major flares. The optical/γ-ray flux ratio at the flare peak varied for each flare. Very minor optical V-I color changes were detected during the flares. The X-ray spectrum was well fitted by a power law with photon spectral index α=1.5, nearly independent of the flux level: no clear correlation with the optical or the γ-ray emission was found. The γ-ray spectral shape was well fitted by a power law with average photon index α= 2.2. These findings support an Inverse Compton origin for the high-energy emission of the source, nearly co-spatial with the optically emitting region. The radio maps showed two new components originating from the core and moving outwards, with ejection epochs compatible with the dates of the two largest γ-ray flares.