Miracle-dz/UniFAR-FTUs huggingface dataset:
Being Central on the Cheap: Stability in Heterogeneous Multiagent Centrality Games

We study strategic network formation games in which agents attempt to form (costly) links in order to maximize their network centrality. Our model derives from Jackson and Wolinsky's symmetric connection model, but allows for heterogeneity in agent utilities by replacing decay centrality (implicit in the Jackson-Wolinsky model) by a variety of classical centrality and game-theoretic measures of centrality. We are primarily interested in characterizing the asymptotically pairwise stable networks, i.e. those networks that are pairwise stable for all sufficiently small, positive edge costs. We uncover a rich typology of stability: - we give an axiomatic approach to network centrality that allows us to predict the stable network for a rich set of combination of centrality utility functions, yielding stable networks with features reminiscent of structural properties such as "core periphery" and "rich club" networks. - We show that a simple variation on the model renders it universal, i.e. every network may be a stable network. - We also show that often we can infer a significant amount about agent utilities from the structure of stable networks.
The Determinants of Buyout Returns: Does Transaction Strategy Matter?

Using an original dataset of fully monetized LBOs initiated from 1990 to 2006, we examine the emergence of an entrepreneurial transaction strategy focused on revenue growth and its incidence relative to more “classic” strategies focused on operating efficiencies. We additionally show how the conventional focus on returns measured at an IPO or acquisition frequently overstates actual realized returns to sponsors. Using this return data, we evaluate how “classic” and “entrepreneurial” strategies are associated with sponsors' equity returns. Among successful LBOs, LBOs that enhance operating efficiencies produce the highest “exit” returns; however, LBO sponsors commonly fail to monetize these returns due to delays associated with liquidating portfolio positions. In contrast, LBOs that focus on growing revenues are associated with higher fully realized equity returns, suggesting more sustainable value-creation for sponsors and their investors.
Whistleblowing: Incentives and Situational Determinants

Law makers increasingly try to capitalize on individuals having acquired knowledge of corporate crimes or other misconduct by inducing them to blow the whistle. In a laboratory experiment we measure the effectiveness of incentives on the willingness to report such misconduct to a sanctioning authority. We find that fines for non-reporting insiders, rewards and even simple commands increase the probability of whistleblowing. We find the strongest effect for fines. Situational determinants also influence the willingness to blow the whistle: Insiders who are negatively affected by the misconduct are more likely to blow the whistle than non-affected or profiting insiders. Those (negatively affected) victims are also sensitive to the misconduct's impact on the authority sanctioning the misconduct (public authority or employer): Whistleblowing is more likely if the enforcement authority is negatively affected compared to positively or not affected.
Spherical Interpolated Convolutional Network With Distance–Feature Density for 3-D Semantic Segmentation of Point Clouds

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3-D convolution kernel to extract features from raw 3-D point clouds because of the unstructured property of point clouds. In this article, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3-D convolution operator. In addition, this article analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance–feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of the spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3-D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. The comparison experiments with the traditional grid-shaped 3-D convolution operator demonstrated that the newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. The source codes will be released on https://github.com/IRMVLab/SIConv.
MLP Coursework 4 : Painting Generation Using Conditional Generative Adversarial Nets

We examined the use of modern Generative Adversarial Nets to generate novel images of oil paintings using the Painter By Numbers dataset, in order to improve on previously performed baseline experiments. We implemented Spectral Normalization GAN (SNGAN) and Spectral Normalization GAN with Gradient Penalty, and compared their outputs to our baseline Deep Convolutional GAN. Visually, and quantitatively according to the Sliced Wasserstein Distance metric, we determined that the SN-GAN produced paintings that were most comparable to our training dataset. We then performed a series of experiments to attempt to add supervised conditioning to SN-GAN, the culmination of which is what we believe to be a novel architecture that can generate faces with userspecified characteristics.
Two-sample testing of high-dimensional linear regression coefficients via complementary sketching

We introduce a new method for two-sample testing of high-dimensional linear regression coefficients without assuming that those coefficients are individually estimable. The procedure works by first projecting the matrices of covariates and response vectors along directions that are complementary in sign in a subset of the coordinates, a process which we call 'complementary sketching'. The resulting projected covariates and responses are aggregated to form two test statistics, which are shown to have essentially optimal asymptotic power under a Gaussian design when the difference between the two regression coefficients is sparse and dense respectively. Simulations confirm that our methods perform well in a broad class of settings.
How to train your conditional GAN: An approach using geometrically structured latent manifolds

Conditional generative modeling typically requires capturing one-to-many mappings between the inputs and outputs. However, vanilla conditional GANs (cGAN) tend to ignore the variations of the latent seeds which results in mode-collapse. As a solution, recent works have moved towards comparatively expensive models for generating diverse outputs in a conditional setting. In this paper, we argue that the limited diversity of the vanilla cGANs is not due to a lack of capacity, but a result of non-optimal training schemes. We tackle this problem from a geometrical perspective and propose a novel training mechanism that increases both the diversity and the visual quality of the vanilla cGAN. The proposed solution does not demand architectural modifications and paves the way for more efficient architectures that target conditional generation in multi-modal spaces. We validate the efficacy of our model against a diverse set of tasks and show that the proposed solution is generic and effective across multiple datasets.
Factors Affecting Sleep Quality in Firefighters

Background: Identifying factors affecting the quality of sleep in firefighters is of significant importance. Good sleep is one of the essential needs of firefighters, and poor sleep quality impairs their performance and decision making. The present study aimed to evaluate sleep quality and its relation with ergonomic, demographic, and psychological factors in firefighters employed in Qazvin. Methods: 118 firefighters from seven stations participated in this study. Data were gathered by questionnaires including demographic, Pittsburgh Sleep Quality Index, Beck Depression Inventory, HSE questionnaire for occupational stress, and Nordic questionnaire. Data were compared between two groups of firefighters with good and poor sleep quality. Then the data were analyzed using regression analysis. Results: 59.3% of firefighters had poor quality of sleep. Univariate analysis showed a significant correlation between sleep quality with musculoskeletal disorders, shift work, body mass index (BMI), and stress (P<0.05). In multivariate analysis, all variables except the occupational stress maintained their significant relationship with sleep quality. Conclusion: this study showed that musculoskeletal disorders (MSDs), shift work, and high body mass index are associated with poor sleep quality in firefighters. Proper timely interventions are suggested to prevent the complications of sleep disorders in firefighters.
Modelling population III stars for seminumerical simulations

Theoretically modelling the 21-cm signals caused by Population III stars (Pop III stars) is the key to extracting fruitful information on Pop III stars from current and forthcoming 21-cm observations. In this work we develop a new module of Pop III stars in which the escape fractions of ionizing photons and Lyman-Werner (LW) photons, photo-heating by UV radiation, and LW feedback are consistently incorporated. By implementing the module into a public 21-cm semi-numerical simulation code, 21CMFAST, we demonstrate 21-cm signal calculations and investigate the importance of Pop III star modelling. What we find is that the contribution from Pop III stars to cosmic reionization significantly depends on the treatment of the escape fraction. With our escape fraction model, Pop III stars hardly contribute to reionization because less massive halos, whose escape fraction are high, cannot host Pop III stars due to LW feedback. On the other hand, Pop III stars well contribute to reionization with the conventional constant escape fraction. We also find that UV photo-heating has non-negligible impact on the 21-cm global signal and the 21-cm power spectrum if the ionization fraction of the Universe is higher than roughly 1 percent. In this case, the strength of the 21-cm global signal depends on the photo-heating efficiency and thus on the Pop III star mass. We conclude that detailed modelling of Pop III stars is imperative to predict 21-cm observables accurately for future observations.
Model-independent calibrations of gamma-ray bursts using machine learning

We alleviate the circularity problem, whereby gamma-ray bursts are not perfect distance indicators, by means of a new model-independent technique based on Bézier polynomials. We use the well consolidate Amati and Combo correlations. We consider improved calibrated catalogues of mock data from differential Hubble rate points. To get our mock data, we use those machine learning scenarios that well adapt to gamma-ray bursts, discussing in detail how we handle small amounts of data from our machine learning techniques. We explore only three machine learning treatments, i.e. linear regression, neural network, and random forest, emphasizing quantitative statistical motivations behind these choices. Our calibration strategy consists in taking Hubble’s data, creating the mock compilation using machine learning and calibrating the aforementioned correlations through Bézier polynomials with a standard chi-square analysis first and then by means of a hierarchical Bayesian regression procedure. The corresponding catalogues, built up from the two correlations, have been used to constrain dark energy scenarios. We thus employ Markov chain Monte Carlo numerical analyses based on the most recent Pantheon supernova data, baryonic acoustic oscillations, and our gamma-ray burst data. We test the standard ΛCDM model and the Chevallier–Polarski–Linder parametrization. We discuss the recent H0 tension in view of our results. Moreover, we highlight a further severe tension over Ωm and we conclude that a slight evolving dark energy model is possible.
Prognostic significance of lymph node metastasis in triple negative ductal carcinoma of the breast : a retrospective cohort study

Objective: The purpose of this study was to identify the prognostic significance of lymph node (LN) metastasis in triple negative breast cancer (TNBC). Methods: A retrospective analysis of 206 TNBC was conducted. Clinicopathological characteristics between different LN statuses were compared by Chi-square test. Survival differences among pT stages, pN stages, lymphovascular invasion (LVI) etc. were compared by Log-rank test. All variables with statistical significance in the univariate analysis were investigated in multivariate analysis by COX regression. Results: The mean age of 206 TNBC patients was 49.80 ± 10.06 years old. The mean size of tumor was 2.96 ± 2.01 cm. There were altogether 83 cases (40.3%) with positive LNs, among which 50 cases in pN1 stage, 24 cases in pN2 stage and 9 cases in pN3 stage. Tumor size and LVI were correlated with LN metastasis. Tumor size (P = 0.023 for DFS, 0.012 for OS), LVI (P = 0.001 for DFS, 0.003 for OS) and LN status (negative or positive) (P = 0.017 for DFS, 0.008 for OS) rather than pN stage (P = 0.090 for DFS, 0.061 for OS) were independent prognostic factors of TNBC. There were no statistically significant differences in the prognosis among pN1-pN3 regardless of tumor size or LVI status. Conclusions: For TNBC, LN status was an independent prognostic factor. But for cases with LN metastasis, the prognosis was not greatly affected by the number of positive LNs. Perhaps apart from lymphatic metastasis, hematogenous metastasis played an important role in TNBC progression, even at an early stage.
Detailed Chromospheric Activity Nature of KIC 9641031

Abstract This study depends on KIC 9641031 eclipsing binary with a chromospherically active component. There are three type variations, such as geometrical variations due to eclipses, sinusoidal variations due to the rotational modulations, and also flares, in the light curves. Taking into account results obtained from observations in the Kepler Mission Database, we discuss the details of chromospheric activity. The sinusoidal light variations due to rotational modulation and the flare events were modelled. 92 different data subsets separated using the analytic models were modelled separately to obtain the cool spot configuration. Acording to the model, there are two active regions separated by about 180° longitudinally between the latitudes of +50° and +100°. 240 flares, whose parameters were computed, were detected. Using these parameters, the OPEA model was derived, in which the Plateau value was found to be 1.232±0.069 s, and half-life parameter was found as 2291.7 s. The flare frequency N1 was found as 0.41632 h−1, while the flare frequency N2 was found as 0.00027. Considering these parameters together with the orbital period variations demonstrates that the period variations depend on chromospheric activity. Comparing the system with its analogue, the activity level of KIC 9641031 is remarkably lower than the others.
Two-fluid Modeling of Acoustic Wave Propagation in Gravitationally Stratified Isothermal Media

To study acoustic wave propagation and the corresponding energy deposition in partially ionized plasmas, we use a two-fluid computational model that treats neutrals and charged particles (electrons and ions) as two separate fluids. This two-fluid model takes into account the ion–neutral collisions, ionization, and recombination, allowing us to investigate both the collisional and reactive interactions between uncoupled ions and neutrals in the plasmas. In the present numerical simulations, the initial density is specified to reach hydrostatic equilibrium, and as a comparison, chemical equilibrium is also taken into account to provide a density profile that differs from typical hydrostatic equilibrium profiles. External velocity drivers are then imposed to generate monochromatic acoustic waves. As is well known, the upward propagating acoustic waves steepen in gravitationally stratified plasmas due to the exponentially decreasing density, and they heat the plasmas in the nonlinear regimes where kinetic energy is dissipated by shock waves and collisional interactions. In particular, the lower ionization fraction resulting from the present initial chemical equilibrium significantly enhances the heating efficiency. Moreover, the ionization process absorbs a significant amount of energy, and the decoupling between ions and neutrals is also enhanced while considering ionization and recombination. Therefore, simulations without considering ionization and recombination may overestimate the overall heating effects but also underestimate the energy dissipation. The results also suggest that a more accurate ionization and recombination model could be essential for improving the modeling of partially ionized plasmas.
Production of J/Ψ mesons in p–Pb collisions at the Large Hadron Collider

Data from p–Pb collisions reconstructed with the ALICE experiment at √s = 5.02 TeV is analyzed to study J/Ψ production (a possible Quark Gluon Plasma signal) in diﬀerent pT ranges. Results obtained by studying the invariant mass distribution via the dielectron decay channel show a signiﬁcant J/Ψ yield from approximately pT > 2 GeV/c with statistics up to about 10 GeV/c. The signal is observed via Unlike-Sign pairs, and the combinatorial background is estimated via Like-Sign pairs.
Wishful Thinking: Persuasion and Polarization

How can motivated thinking impact strategic information disclosure? We investigate this question by modelling a game of persuasion in which Receiver is a "wishful thinker" in the fashion of Caplin and Leahy (2019). Following reception of Sender's signal, Receiver optimally chooses its subjective belief by trading off the anticipatory utility she derives from being optimistic and the psychological cost of distorting beliefs. We derive the optimal correspondence between Bayesian and motivated belief and characterize conditions on primitives of the model that define whether the persuader is better or worse off when facing a wishful thinker vis-\`a-vis the Bayesian canonical model of Kamenica and Gentzkow (2011). We apply those results to explain some stylized facts such as why financial advisors often mislead their clients and why informational interventions are often inefficient in inducing more investment in preventive health treatments. Finally, we extend the model to a binary majority voting setting in which wishful voters hold heterogeneous partisan preferences. We show that, if voters' preferences are symmetrically distributed around the median voter, optimal public persuasion induces maximum belief polarization in the electorate. This formalizes a new mechanism for the emergence of polarization: as a byproduct of strategic information disclosure to wishful agents.
Short-term Middle-term Long-term Negative Positive Anchor Sample Inter-sample Relation Intra-temporal Relation Negative Sample Positive Sample Anchor Piece Temporal Relation : Sample Relation :

Self-supervised learning achieves superior performance in many domains by extracting useful representations from the unlabeled data. However, most of traditional self-supervised methods mainly focus on exploring the inter-sample structure while less efforts have been concentrated on the underlying intra-temporal structure, which is important for time series data. In this paper, we present SelfTime: a general Self-supervised Time series representation learning framework, by exploring the inter-sample relation and intra-temporal relation of time series to learn the underlying structure feature on the unlabeled time series. Specifically, we first generate the inter-sample relation by sampling positive and negative samples of a given anchor sample, and intra-temporal relation by sampling time pieces from this anchor. Then, based on the sampled relation, a shared feature extraction backbone combined with two separate relation reasoning heads are employed to quantify the relationships of the sample pairs for inter-sample relation reasoning, and the relationships of the time piece pairs for intra-temporal relation reasoning, respectively. Finally, the useful representations of time series are extracted from the backbone under the supervision of relation reasoning heads. Experimental results on multiple real-world time series datasets for time series classification task demonstrate the effectiveness of the proposed method. Code and data are publicly available at https://haoyfan.github. io/.
Vecsigrafo: Corpus-based word-concept embeddings

The proliferation of knowledge graphs and recent advances in Artiﬁcial Intelligence have raised great expectations related to the combination of symbolic and distributional semantics in cognitive tasks. This is particularly the case of knowledge-based approaches to natural language processing as near-human symbolic understanding rely on expressive, structured knowledge representations. Engineered by humans, such knowledge graphs are frequently well curated and of high quality, but at the same time can be labor-intensive, brittle or biased. The work reported in this paper aims to address such limitations, bringing together bottom-up, corpus-based knowledge and top-down, structured knowledge graphs by capturing as embeddings in a joint space the semantics of both words and concepts from large document corpora. To evaluate our results, we perform the largest and most comprehensive empirical study around this topic that we are aware of, analyzing and comparing the quality of the resulting embeddings over competing approaches. We include a detailed ablation study on the different strategies and components our approach comprises and show that our method outperforms the previous state of the art according to standard benchmarks.
Association between health literacy and glycemic control in elderly patients with type 2 diabetes and modifying effect of social support

ABSTRACT Objective: To investigate the association between inadequate functional health literacy, considering the Short Assessment of Health Literacy for Portuguese-speaking Adults, and glycemic control in elderly patients with type 2 diabetes, and to examine this association in low social support settings, according to Medical Outcomes Study . Methods: Cross-sectional study conducted at the diabetes referral center of a university hospital. Participants were recruited among type 2 diabetes patients aged 60 years or older, between May 2013 and November 2014. The primary outcome was the most recent glycated hemoglobin value measured within the last 6 months. Results: A total of 398 elderly patients with type 2 diabetes were evaluated. Of these, 232 were not eligible to participate. The final sample comprised 166 participants. Hierarchical multiple linear regression was performed. The following variables were entered in three blocks: sociodemographic characteristics, clinical variables and health literacy scores. Regression analysis of the interaction between health literacy and social support as a determinant of glycemic control was also performed. Mean age of subjects was 68.0 years (standard deviation of 5.9). Mean glycated hemoglobin value was 8.5% (standard deviation of 1.4). Short assessment of health literacy for Portuguese speaking adults score was independently associated with glycated hemoglobin (B=-0.059; p=0.043). The interaction between social support and health literacy score (p=0.003) was a determinant of glycemic control. Conclusion: Health literacy is associated with glycemic control. Social support may modify the relation between health literacy and glycemic control.
THE PRIMITIVE P-FROBENIUS GROUPS

Let p be a (cid:12)xed prime. A (cid:12)nite primitive permutation group G with every two-point stabilizer G α,β being a p -group is called a primitive p - Frobenius group . Using our earlier results on p -intersection subgroups , we give a complete classi(cid:12)cation of the primitive p -Frobenius groups.