Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of life of postgraduate students stricto sensu in dentistry and sociodemographic factors

Introduction: Quality of life (QL) is subjective and must be based on a multidimensional construct and on positive and negative factors in the individual’s life. Objective: The objective of this study was to evaluate the QL of the students of the Postgraduate Program in Dentistry (PPGO) of the Federal University of Ceará. Material and method: Cross-sectional study with a quantitative approach, conducted with 88 students regularly enrolled in PPGO. The QL was measured by the World Health Organization Questionnaire for Quality of Life-bref (WHOQOL-bref). For a better analysis of the QL scores, they were categorized into three levels: dissatisfaction, uncertainty and satisfaction. A multinomial logistic regression model was used. Result: The majority of the respondents (55.7%) was classified in the area of satisfaction with QL, with 14,05 total average scores, corresponding to 62.8% of the total. By associating the socio-demographic data with the categories of analysis of QL, it was observed relevant in single students (p=0.005), those who considered themselves religious (p=0.041) and those who did not have a postgraduate scholarship (p=0.025), being evaluated in the area of dissatisfaction/uncertainty about the QL. Conclusion: It was concluded that marital status, religion and scholarship in postgraduate school were relevant in the QL of stricto sensu postgraduate students in Dentistry. Descriptors: Quality of life; students, dental; education.
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems

Semi-parametric regression models are used in several applications which require comprehensibility without sacrificing accuracy. Examples are spline interpolation in geophysics, or non-linear time series problems, where the system includes for instance a linear and non-linear component. We discuss here the use of a finite Determinantal Point Process (DPP) sampling for approximating semi-parametric models in two cases. On the one hand, in the case of large training data sets, DPP sampling is used to reduce the number of model parameters. On the other hand, DPPs can determine experimental designs in the case of the optimal interpolation models. Recently, Barthelme, Tremblay, Usevich, and Amblard introduced a novel representation of finite DPP's. They formulated extended $L$-ensembles that can conveniently represent for instance partial-projection DPPs and suggest their use for optimal interpolation. With the help of this formalism, we derive a key identity illustrating the implicit regularization effect of determinantal sampling for semi-parametric regression and interpolation. Also, a novel projected Nystrom approximation is defined and used to derive a bound on the expected risk for the corresponding approximation of semi-parametric regression. This work naturally extends similar results obtained for kernel ridge regression.
Rise and fall of post-starburst galaxies in Magneticum Pathfinder

Post-starburst (PSB) galaxies belong to a short-lived transition population between star-forming (SF) and quiescent galaxies. Deciphering their heavily discussed evolutionary pathways is paramount to understanding galaxy evolution. We aim to determine the dominant mechanisms governing PSB evolution in both the field and in galaxy clusters. Using the cosmological hydrodynamical simulation suite Magneticum Pathfinder, we identify 647 PSBs with z ∼ 0 stellar mass $M_* \ge 5 \times 10^{10} \, \mathrm{M_{\odot }}$ . We track their galactic evolution, merger history, and black hole activity over a time-span of $3.6\,$ Gyr. Additionally, we study cluster PSBs identified at different redshifts and cluster masses. Independent of environment and redshift, we find that PSBs, like SF galaxies, have frequent mergers. At z = 0, $89{{\ \rm per\ cent}}$ of PSBs have experienced mergers and $65{{\ \rm per\ cent}}$ had at least one major merger within the last $2.5\,$ Gyr, leading to strong star formation episodes. In fact, $23{{\ \rm per\ cent}}$ of z = 0 PSBs were rejuvenated during their starburst. Following the mergers, field PSBs are generally shutdown via a strong increase in active galactic nucleus (AGN) feedback (power output $P_{\rm AGN,PSB} \ge 10^{56}\,$ erg Myr−1). We find agreement with observations for both stellar mass functions and z = 0.9 line-of-sight phase space distributions of PSBs in galaxy clusters. Finally, we find that z ≲ 0.5 cluster PSBs are predominantly infalling, especially in high-mass clusters and show no signs of enhanced AGN activity. Thus, we conclude that the majority of cluster PSBs are shutdown via an environmental quenching mechanism such as ram-pressure stripping, while field PSBs are mainly quenched by AGN feedback.
Study of genetic mutations and dynamic spread of SARS-CoV-2 pandemic and prediction of its evolution according to the SIR model

In this work, we aim to study that the dynamics behavior for cumulative number of SARS-CoV-2 pandemic can provide information on the overall behavior of the spread over daily time.The cumulative data can be synthesized in an empirical form obtained from a Susceptible-Infected-Recovered (SIR) model previously studied on a Euclidean network. From the study we carried out, we can conclude that the SIR model on the Euclidean network can reproduce data from several countries with a deviation of precision for given parameter values. This gives an insight into the different agents that influence the behavior of SARS-CoV-2 especially during the virus mutation period. We are thus trying to analyze the effect of genetic mutations in different countries, and how a specific mutation can make the virus more contagious.
Deep Template Matching for Pedestrian Attribute Recognition with the Auxiliary Supervision of Attribute-wise Keypoints

Pedestrian Attribute Recognition (PAR) has aroused extensive attention due to its important role in video surveillance scenarios. In most cases, the existence of a particular attribute is strongly related to a partial region. Recent works design complicated modules, e.g., attention mechanism and proposal of body parts to localize the attribute corresponding region. These works further prove that localization of attribute specific regions precisely will help in improving performance. However, these part-information-based methods are still not accurate as well as increasing model complexity which makes it hard to deploy on realistic applications. In this paper, we propose a Deep Template Matching based method to capture body parts features with less computation. Further, we also proposed an auxiliary supervision method that use human pose keypoints to guide the learning toward discriminative local cues. Extensive experiments show that the proposed method outperforms and has lower computational complexity, compared with the state-of-the-art approaches on large-scale pedestrian attribute datasets, including PETA, PA-100K, RAP, and RAPv2 zs.
Identifying Causal Effects in Experiments with Social Interactions and Non-compliance

This paper shows how to use a randomized saturation experimental design to identify and estimate causal effects in the presence of social interactions--one person's treatment may affect another's outcome--and one-sided non-compliance--subjects can only be offered treatment, not compelled to take it up. Two distinct causal effects are of interest in this setting: direct effects quantify how a person's own treatment changes her outcome, while indirect effects quantify how her peers' treatments change her outcome. We consider the case in which social interactions occur only within known groups, and take-up decisions do not depend on peers' offers. In this setting we point identify local average treatment effects, both direct and indirect, in a flexible random coefficients model that allows for both heterogenous treatment effects and endogeneous selection into treatment. We go on to propose a feasible estimator that is consistent and asymptotically normal as the number and size of groups increases.
Genetic dissection of the different roles of hypothalamic kisspeptin neurons in regulating female reproduction

The brain regulates fertility through gonadotropin-releasing hormone (GnRH) neurons. Estradiol induces negative feedback on pulsatile GnRH/luteinizing hormone (LH) release and positive feedback generating preovulatory GnRH/LH surges. Negative and positive feedback are postulated to be mediated by kisspeptin neurons in arcuate and anteroventral periventricular (AVPV) nuclei, respectively. Kisspeptin-specific ERα knockout mice exhibit disrupted LH pulses and surges. This knockout approach is neither location-specific nor temporally-controlled. We utilized CRISPR-Cas9 to disrupt ERα in adulthood. Mice with ERα disruption in AVPV kisspeptin neurons have typical reproductive cycles but blunted LH surges, associated with decreased excitability of these neurons. Mice with ERα knocked down in arcuate kisspeptin neurons showed disrupted cyclicity, associated with increased glutamatergic transmission to these neurons. These observations suggest activational effects of estradiol regulate surge generation and maintain cyclicity through AVPV and arcuate kisspeptin neurons, respectively, independent from its role in the development of hypothalamic kisspeptin neurons or puberty onset. Significant Statement The brain regulates fertility through gonadotropin-releasing hormone (GnRH) neurons. Ovarian estradiol regulates GnRH pulses (negative feedback) and the GnRH surge release that ultimately triggers ovulation (positive feedback). Kisspeptin neurons in the arcuate and anteroventral periventricular nuclei are postulated to convey negative and positive feedback to GnRH neurons, respectively. Kisspeptin-specific ERα knockout mice exhibited disrupted negative and positive feedback. However, it is not clear what roles each kisspeptin population plays, and not possible to separate their roles during development vs adulthood in this model. Here we utilized CRISPR-Cas9 to disrupt ERα in each population in adulthood. We found activational effects of estradiol regulate surge generation and maintain cyclicity through AVPV and arcuate kisspeptin neurons, respectively, independent from estradiol action during development.
The nature of giant clumps in high-z discs: a deep-learning comparison of simulations and observations

We use deep learning to explore the nature of observed giant clumps in high-redshift disc galaxies, based on their identification and classification in cosmological simulations. Simulated clumps are detected using the 3D gas and stellar densities in the VELA zoom-in cosmological simulation suite, with ${\sim}25\ \rm {pc}$ maximum resolution, targeting main-sequence galaxies at 1 < z < 3. The clumps are classified as long-lived clumps (LLCs) or short-lived clumps (SLCs) based on their longevity in the simulations. We then train neural networks to detect and classify the simulated clumps in mock, multicolour, dusty, and noisy HST-like images. The clumps are detected using an encoder–decoder convolutional neural network (CNN), and are classified according to their longevity using a vanilla CNN. Tests using the simulations show our detector and classifier to be ${\sim}80{{\ \rm per\ cent}}$ complete and ${\sim}80{{\ \rm per\ cent}}$ pure for clumps more massive than ∼107.5 M⊙. When applied to observed galaxies in the CANDELS/GOODS S+N fields, we find both types of clumps to appear in similar abundances in the simulations and the observations. LLCs are, on average, more massive than SLCs by ∼0.5 dex, and they dominate the clump population above Mc ≳ 107.6 M⊙. LLCs tend to be found closer to the galactic centre, indicating clump migration to the centre or preferential formation at smaller radii. The LLCs are found to reside in high-mass galaxies, indicating better clump survivability under supernova feedback there, due to clumps being more massive in these galaxies. We find the clump masses and radial positions in the simulations and the observations to agree within a factor of 2.
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains

Convolutional neural networks (CNNs) have proven to be highly successful at a range of image-to-image tasks. CNNs can be computationally expensive, which can limit their applicability in practice. Model pruning can improve computational efficiency by sparsifying trained networks. Common methods for pruning CNNs determine what convolutional filters to remove by ranking filters on an individual basis. However, filters are not independent, as CNNs consist of chains of convolutions, which can result in sub-optimal filter selection. We propose a novel pruning method, LongEst-chAiN (LEAN) pruning, which takes the interdependency between the convolution operations into account. We propose to prune CNNs by using graph-based algorithms to select relevant chains of convolutions. A CNN is interpreted as a graph, with the operator norm of each convolution as distance metric for the edges. LEAN pruning iteratively extracts the highest value path from the graph to keep. In our experiments, we test LEAN pruning for several image-to-image tasks, including the well-known CamVid dataset. LEAN pruning enables us to keep just 0.5%-2% of the convolutions without significant loss of accuracy. When pruning CNNs with LEAN, we achieve a higher accuracy than pruning filters individually, and different pruned substructures emerge.
Magnetic anisotropy of individual maghemite mesocrystals

Interest in creating magnetic metamaterials has led to methods for growing superstructures of magnetic nanoparticles. Mesoscopic crystals of maghemite ($\gamma\text{-Fe}_2\text{O}_3$) nanoparticles can be arranged into highly ordered body-centered tetragonal lattices of up to a few micrometers. Although measurements on disordered ensembles have been carried out, determining the magnetic properties of individual mesoscopic crystals is challenging due to their small total magnetic moment. Here, we overcome these challenges by utilizing sensitive dynamic cantilever magnetometry to study individual micrometer-sized $\gamma\text{-Fe}_2\text{O}_3$ mesocrystals. These measurements reveal an unambiguous cubic anisotropy, resulting from the crystalline anisotropy of the constituent maghemite nanoparticles and their alignment within the mesoscopic lattice. The signatures of anisotropy and its orgins come to light because we combine the self-assembly of highly ordered mesocrystals with the ability to resolve their individual magnetism. This combination is promising for future studies of the magnetic anisotropy of other nanoparticles, which are too small to investigate individually.
Adversarial Robustness Against Image Color Transformation within Parametric Filter Space

We propose Adversarial Color Enhancement (ACE), a novel approach to generating non-suspicious adversarial images by optimizing a color transformation within a parametric filter space. The filter we use approximates human-understandable color curve adjustment, constraining ACE with a single, continuous function. This property gives rise to a principled adversarial action space explicitly controlled by filter parameters. Existing color transformation attacks are not guided by a parametric space, and, consequently, additional pixel-related constraints such as regularization and sampling are necessary. These constraints make methodical analysis difficult. In this paper, we carry out a systematic robustness analysis of ACE from both the attack and defense perspectives by varying the bound of the color filter parameters. We investigate a general formulation of ACE and also a variant targeting particularly appealing color styles, as achieved with popular image filters. From the attack perspective, we provide extensive experiments on the vulnerability of image classifiers, but also explore the vulnerability of segmentation and aesthetic quality assessment algorithms, in both the white-box and black-box scenarios. From the defense perspective, more experiments provide insight into the stability of ACE against input transformation-based defenses and show the potential of adversarial training for improving model robustness against ACE.
Fenchel-Moreau identities on self-dual cones

A pointed convex cone in a Hilbert space naturally induces a partial order, and further a notion of nondecreasingness for functions. We consider extended real-valued functions defined on the cone. Monotone conjugates for these functions can be defined in an analogous way to the standard convex conjugate. The only difference is that the supreme is taken over the cone instead of the entire space. The main result is the corresponding Fenchel-Moreau biconjugation identity for proper, convex, lower semicontinuous and nondecreasing functions provided the cone is perfect. Essentially, a cone is called perfect if it is self-dual and each face of the cone is self-dual in its own span. When the cone is merely self-dual, the Fenchel-Moreau identity still holds for such a function under an additional assumption that its domain has nonempty interior.
Unsupervised Explanation Generation for Machine Reading Comprehension

With the blooming of various Pre-trained Language Models (PLMs), Machine Reading Comprehension (MRC) has embraced significant improvements on various benchmarks and even surpass human performances. However, the existing works only target on the accuracy of the final predictions and neglect the importance of the explanations for the prediction, which is a big obstacle when utilizing these models in real-life applications to convince humans. In this paper, we propose a self-explainable framework for the machine reading comprehension task. The main idea is that the proposed system tries to use less passage information and achieve similar results compared to the system that uses the whole passage, while the filtered passage will be used as explanations. We carried out experiments on three multiple-choice MRC datasets, and found that the proposed system could achieve consistent improvements over baseline systems. To evaluate the explainability, we compared our approach with the traditional attention mechanism in human evaluations and found that the proposed system has a notable advantage over the latter one.
Losing the trace to find dynamical Newton or Planck constants

We show that promoting the trace part of the Einstein equations to a trivial identity results in the Newton constant being an integration constant. Thus, in this formulation the Newton constant is a global dynamical degree of freedom which is also a subject to quantization and quantum fluctuations. This is similar to what happens to the cosmological constant in the unimodular gravity where the trace part of the Einstein equations is lost in a different way. We introduce a constrained variational formulation of these modified Einstein equations. Then, drawing on analogies with the Henneaux-Teitelboim action for unimodular gravity, we construct different general-covariant actions resulting in these dynamics. The inverse of dynamical Newton constant is canonically conjugated to the Ricci scalar integrated over spacetime. Surprisingly, instead of the dynamical Newton constant one can formulate an equivalent theory with a dynamical Planck constant. Finally, we show that an axion-like field can play a role of the gravitational Newton constant or even of the quantum Planck constant.
A Survey on Recent Advances in Sequence Labeling from Deep Learning Models

Sequence labeling (SL) is a fundamental research problem encompassing a variety of tasks, e.g., part-of-speech (POS) tagging, named entity recognition (NER), text chunking, etc. Though prevalent and effective in many downstream applications (e.g., information retrieval, question answering, and knowledge graph embedding), conventional sequence labeling approaches heavily rely on hand-crafted or language-specific features. Recently, deep learning has been employed for sequence labeling tasks due to its powerful capability in automatically learning complex features of instances and effectively yielding the stat-of-the-art performances. In this paper, we aim to present a comprehensive review of existing deep learning-based sequence labeling models, which consists of three related tasks, e.g., part-of-speech tagging, named entity recognition, and text chunking. Then, we systematically present the existing approaches base on a scientific taxonomy, as well as the widely-used experimental datasets and popularly-adopted evaluation metrics in the SL domain. Furthermore, we also present an in-depth analysis of different SL models on the factors that may affect the performance and future directions in the SL domain.
Non-stationary Online Regression

Online forecasting under a changing environment has been a problem of increasing importance in many real-world applications. In this paper, we consider the meta-algorithm presented in \citet{zhang2017dynamic} combined with different subroutines. We show that an expected cumulative error of order $\tilde{O}(n^{1/3} C_n^{2/3})$ can be obtained for non-stationary online linear regression where the total variation of parameter sequence is bounded by $C_n$. Our paper extends the result of online forecasting of one-dimensional time-series as proposed in \cite{baby2019online} to general $d$-dimensional non-stationary linear regression. We improve the rate $O(\sqrt{n C_n})$ obtained by Zhang et al. 2017 and Besbes et al. 2015. We further extend our analysis to non-stationary online kernel regression. Similar to the non-stationary online regression case, we use the meta-procedure of Zhang et al. 2017 combined with Kernel-AWV (Jezequel et al. 2020) to achieve an expected cumulative controlled by the effective dimension of the RKHS and the total variation of the sequence. To the best of our knowledge, this work is the first extension of non-stationary online regression to non-stationary kernel regression. Lastly, we evaluate our method empirically with several existing benchmarks and also compare it with the theoretical bound obtained in this paper.
IKT$^\omega$ and \L{}ukasiewicz-models

In this note we show first that the first-order logic IK$^\omega$ is sound with regard to the models obtained continuum-valued Łukasiewicz-models for first-order languages by treating the quantifiers as infinitary strong disjunction/conjunction rather than infinitary weak disjunction/conjunction. We then proceed to show that these models cannot be used to provide a new consistency proof for the theory of truth IKT$^\omega$ obtained by expanding IK$^\omega$ with transparent truth since the models are incompatible with transparent truth. Moreover, we also show that whether or not this inconsistency can be reproduced in the sequent calculus for IKT$^\omega$ depends on how vacuous quantification is treated.
A Distance-deviation Consistency and Model-independent Method to Test the Cosmic Distance–Duality Relation

A distance-deviation consistency and model-independent method to test the cosmic distance–duality relation (CDDR) is provided. This method is worthy of attention for two reasons. First, a distance-deviation consistency method is used to pair subsamples: instead of pairing subsamples with redshift deviations smaller than a value of, say, . The redshift deviation between subsamples decreases with the redshift to ensure the distance deviation stays the same. The method selects more subsamples at high redshift, up to z = 2.16, and provides 120 subsample pairs. Second, the model-independent method involves the latest data set of 1048 SNe Ia and 205 strong gravitational lensing systems (SGLS), which are used to obtain the luminosity distances D L and the ratio of angular diameter distance D A , respectively. With the model-independent method, parameters of the CDDR, the SNe Ia light curve, and the SGLS are fitted simultaneously. The result shows that and CDDR is validated at the 1σ confidence level in the form of .