Miracle-dz/UniFAR-FTUs huggingface dataset:
Dark matter and stellar populations in the central region of early-type galaxies

This thesis work is aimed at the investigation of the dark matter (DM) amount and distribution in early-type galaxies (ETGs), in relation with the stellar properties, constrained by observations of the kinematic fields of galaxies. In particular, we focus on observations of the projected stellar velocity dispersion in the central region of ETGs. A robust determination of the DM amount and distribution is an open issue, which we explore from two complementary points of view. A top-down approach, with priority given to cosmology, based on the predictions of numerical cosmological simulations, and a bottom-up approach, with priority given to the galactic astrophysics. This is accomplished by using the original numerical code JASMINE (Jeans AxiSymmetric Models of galaxies IN Equilibrium), first developed by Posacki et al. (2013), to build dynamical models of ETGs, based on the solution of the Jeans equations. Our Jeans modelling produces axisymmetric (oblate) spheroids with anisotropic stellar orbits. In this thesis work, we focus on spherical isotropic galaxy models, composed of stars, DM and a central supermassive black hole (BH). The stellar component is modelled by a deprojected de Vaucouleurs or Jaffe density profile and satisfies the Faber-Jackson and size-luminosity Scaling Laws. The BH mass, MBH , is related to the total stellar mass, M∗, by the Magorrian relation. The DM component is modelled by the NFW density profile. We explore the effect on the dynamics of stars, due to the stellar and DM properties, such as the assumption of a fixed stellar mass-to-light ratio, Υ∗ dyn, and fixed halo parameters β and c (halo-to-stellar scale radius ratio and halo concentration, respectively). We intend to investigate how much the DM amount and distribution can vary, while reproducing a given value for the projected central velocity dispersion of stars, σe8 (luminosity-weighted within Re/8). Our study suggests that σe8 is not a good diagnostic of the DM properties in ETGs, especially it is not able to constrain the DM amount and distribution at large radii. This is a preliminary work which we will extend through the exploration of different properties of ETGs and through observations of their kinematic fields at larger radii (e.g. Re/2, Re), aiming to better understand their dark and luminous contents.
Impact of the COVID-19 pandemic on suicide and self-harm among patients presenting to the emergency department of a teaching hospital in Nepal

Background: The COVID-19 pandemic is a global challenge that is not just limited to the physical consequences but also a significant degree of a mental health crisis. Self-harm (SH) and suicide are its extreme effects. The aim of this study was to provide an overview of the impact of the COVID-19 pandemic on the occurrence and clinical profile of suicide and SH in our ED. Methods: This is a cross-sectional observational study conducted in the ED of a tertiary care center. Records of all fatal and nonfatal SH patients presenting to the ED during the lockdown period (March 24-June 23, 2020; Period1), matching periods in the previous year (March 24-June 23,2019; Period 2) and 3 months period prior (December 24 2019-March 23, 2020; Period 3) was included by searching the electronic medical record (EMR) system. The prevalence and the clinical profile of the patients were compared between these three periods. Results: A total of 125 (periods 1=55, 2=38, and 3=32) suicide and SH cases were analyzed. The cases of suicide/SH had increased by 44% and 71.9% during the lockdown period in comparison to the period 2 and 3. Organophosphate poisoning was the most common mode. Females were predominant in all three periods with a mean age of 32 (95%CI: 29.3-34.7). There was a significant delay in arrival of the patients in period 1 (p-value=0.045) with increased hospital admission (p-value =0.009) and in-hospital mortality (18.2% vs 2.6 % and 3.1%) (p-value=.001). Conclusion: We found an increase in patients presenting with suicide and SH in our ED during the pandemic which is likely to reflect an increased prevalence of mental illness in the community. We hope that the result will prime all mental health care stakeholders to initiate mental health screening and intervention for the vulnerable population during this period of crisis.
Correlation between Chest CT Severity Scores and the Clinical Parameters of Adult Patients with COVID-19 Pneumonia

Purpose Our aim is to correlate the clinical condition of patients with COVID-19 infection with the 25 Point CT severity score by Chang et al (devised for assessment of ARDS in patients with SARS in 2005). Material and Methods Data of consecutive symptomatic patients who were suspected to have COVID-19 infection and presented to our hospital, was collected from March to April 2020. All patients underwent two consecutive RT-PCR tests and had a non-contrast HRCT scan done at presentation. From the original cohort of 1062 patients, 160 patients were excluded leaving a total number of 902 patients. Results The mean age was 44.2 +/- 11.9 years [85.3%males, 14.7 %females]. CT severity score found to be positively correlated with lymphopenia, increased serum CRP, d-dimer and ferritin levels (p < 0.0001). The oxygen requirements as well as length of hospital stay were increasing with the increase of scan severity. Conclusion The 25-point CT severity score correlates well with the COVID-19 clinical severity. Our data suggest that chest CT scoring system can aid in predicting COVID-19 disease outcome and significantly correlates with lab tests and oxygen requirements.
Explainable Automated Fact-Checking for Public Health Claims

Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.
The impact of the halo spin-concentration relation on disc scaling laws

Galaxy scaling laws, such as the Tully–Fisher, mass-size, and Fall relations, can provide extremely useful clues on our understanding of galaxy formation in a cosmological context. Some of these relations are extremely tight and well described by one single parameter (mass), despite the theoretical existence of secondary parameters such as spin and concentration, which are believed to impact these relations. In fact, the residuals of these scaling laws appear to be almost uncorrelated with each other, posing significant constraints on models where secondary parameters play an important role. Here, we show that a possible solution is that such secondary parameters are correlated amongst themselves, in a way that removes correlations in observable space. In particular, we focus on how the existence of an anti-correlation between the dark matter halo spin and its concentration, which is still debated in simulations, can weaken the correlation of the residuals of the Tully–Fisher and mass-size relations. Interestingly, in using simple analytic galaxy formation models, we find that this happens only for a relatively small portion of the parameter space that we explored, which suggests that this idea could be used to derive constraints on galaxy formation models that have yet to be explored.
An Umbrella Converse for Data Exchange: Applied to Caching, Computing, Shuffling & Rebalancing

The problem of data exchange between multiple nodes with (not necessarily uniform) storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data shuffling, Coded Computing, etc. The goal in such problems is to design communication schemes which accomplish the desired data exchange between the nodes with the optimal (minimum) amount of communication load. In this work, we present a converse to such a general data exchange problem between multiple nodes. The expression of the converse depends only on the number of bits to be moved between different subsets of nodes, and does not assume anything further specific about the parameters in the problem. Specific problem formulations, such as those in Coded Caching, Coded Data Shuffling, Coded Distributed Computing, and some of their variants, naturally can be seen as instances of this generic data exchange problem. Applying our generic converse to such problems, we recover known important converses for these settings and some of their variants in a simpler way. Further, for a generic coded caching problem with multiple transmitters, receivers and cache sizes, we show a new general converse which subsumes many existing results. We also employ our bound to obtain a new tight converse bound for the multi-node removal case in the Coded Data Rebalancing problem, in which nodes must exchange information to ‘rebalance’ a storage cluster after some node failures occur.Due to space restrictions, the full version of this paper, containing proofs and additional results, is made available in [1].
Federated Bayesian Optimization via Thompson Sampling

Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate black-box functions. The massive computational capability of edge devices such as mobile phones, coupled with privacy concerns, has led to a surging interest in federated learning (FL) which focuses on collaborative training of deep neural networks (DNNs) via first-order optimization techniques. However, some common machine learning tasks such as hyperparameter tuning of DNNs lack access to gradients and thus require zeroth-order/black-box optimization. This hints at the possibility of extending BO to the FL setting (FBO) for agents to collaborate in these black-box optimization tasks. This paper presents federated Thompson sampling (FTS) which overcomes a number of key challenges of FBO and FL in a principled way: We (a) use random Fourier features to approximate the Gaussian process surrogate model used in BO, which naturally produces the parameters to be exchanged between agents, (b) design FTS based on Thompson sampling, which significantly reduces the number of parameters to be exchanged, and (c) provide a theoretical convergence guarantee that is robust against heterogeneous agents, which is a major challenge in FL and FBO. We empirically demonstrate the effectiveness of FTS in terms of communication efficiency, computational efficiency, and practical performance.
Efficient wide-field radio interferometry response

Radio interferometers do not measure the sky brightness distribution directly, but measure a modified Fourier transform of it. Imaging algorithms therefore need a computational representation of the linear measurement operator and its adjoint, regardless of the specific chosen imaging algorithm. In this paper, we present a C++ implementation of the radio interferometric measurement operator for wide-field measurements that is based on so-called improved w-stacking. It can provide high accuracy (down to ≈10−12), is based on a new gridding kernel that allows smaller kernel support for given accuracy, dynamically chooses kernel, kernel support, and oversampling factor for maximum performance, uses piece-wise polynomial approximation for cheap evaluations of the gridding kernel, treats the visibilities in cache-friendly order, uses explicit vectorisation if available, and comes with a parallelisation scheme that scales well also in the adjoint direction (which is a problem for many previous implementations). The implementation has a small memory footprint in the sense that temporary internal data structures are much smaller than the respective input and output data, allowing in-memory processing of data sets that needed to be read from disk or distributed across several compute nodes before.
COVID-19 neutralizing antibodies predict disease severity and survival

COVID-19 exhibits variable symptom severity ranging from asymptomatic to life-threatening, yet the relationship between severity and the humoral immune response is poorly understood. We examined antibody responses in 113 COVID-19 patients and found that severe cases resulting in intubation or death exhibited increased inflammatory markers, lymphopenia, and high anti-RBD antibody levels. While anti-RBD IgG levels generally correlated with neutralization titer, quantitation of neutralization potency revealed that high potency was a predictor of survival. In addition to neutralization of wild-type SARS-CoV-2, patient sera were also able to neutralize the recently emerged SARS-CoV-2 mutant D614G, suggesting protection from reinfection by this strain. However, SARS-CoV-2 sera was unable to cross-neutralize a highly-homologous pre-emergent bat coronavirus, WIV1-CoV, that has not yet crossed the species barrier. These results highlight the importance of neutralizing humoral immunity on disease progression and the need to develop broadly protective interventions to prevent future coronavirus pandemics.
Local Knowledge Powered Conversational Agents

State-of-the-art conversational agents have advanced significantly in conjunction with the use of large transformer-based language models. However, even with these advancements, conversational agents still lack the ability to produce responses that are informative and coherent with the local context. In this work, we propose a dialog framework that incorporates both local knowledge as well as users' past dialogues to generate high quality conversations. We introduce an approach to build a dataset based on Reddit conversations, where outbound URL links are widely available in the conversations and the hyperlinked documents can be naturally included as local external knowledge. Using our framework and dataset, we demonstrate that incorporating local knowledge can largely improve informativeness, coherency and realisticness measures using human evaluations. In particular, our approach consistently outperforms the state-of-the-art conversational model on the Reddit dataset across all three measures. We also find that scaling the size of our models from 117M to 8.3B parameters yields consistent improvement of validation perplexity as well as human evaluated metrics. Our model with 8.3B parameters can generate human-like responses as rated by various human evaluations in a single-turn dialog setting.
Assessment of SE-specific Sentiment Analysis Tools: An Extended Replication Study

Sentiment analysis methods have become popular for investigating human communication, including discussions related to software projects. Since general-purpose sentiment analysis tools do not fit well with the information exchanged by software developers, new tools, specific for software engineering (SE), have been developed. We investigate to what extent SE-specific tools for sentiment analysis mitigate the threats to conclusion validity of empirical studies in software engineering, highlighted by previous research. First, we replicate two studies addressing the role of sentiment in security discussions on GitHub and in question-writing on Stack Overflow. Then, we extend the previous studies by assessing to what extent the tools agree with each other and with the manual annotation on a gold standard of 600 documents. We find that different SE-specific sentiment analysis tools might lead to contradictory results at a fine-grain level, when used 'off-the-shelf'. Conversely, platform-specific tuning or retraining might be needed to take into account differences in platform conventions, jargon, or document lengths.
The STDyn-SLAM: A Stereo Vision and Semantic Segmentation Approach for VSLAM in Dynamic Outdoor Environments

The Visual Simultaneous Localization and Mapping (VSLAM) is a system based on the scene’s features to estimate a map and the system pose. Commonly, VSLAM algorithms are focused on a static environment; however, some dynamic objects are present in the vast majority of real-world applications. This work presents a feature-based SLAM system focused on dynamic environments using convolutional neural networks, optical flow, and depth maps to detect objects in the scene. The proposed system employs a stereo camera as the primary sensor to capture the scene. The neural network is responsible for object detection and segmentation to avoid erroneous maps and wrong system locations. Moreover, the proposed system’s processing time is fast and can run in real-time, running in outdoor and indoor environments. The proposed approach has been compared with state-of-the-art; besides, we present several experimental results outdoors that corroborate the approach’s effectiveness. Our code is available online.
Atrial fibrillation patients undergoing percutaneous coronary intervention: dual or triple antithrombotic therapy with non-vitamin K antagonist oral anticoagulants

Abstract About 20% of all atrial fibrillation (AF) patients develop coronary artery disease, which requires coronary stenting [percutaneous coronary intervention (PCI)]. Thus, this subcohort of AF patients may require aggressive antithrombotic therapy encompassing vitamin K antagonist (VKA) or non-vitamin K antagonist oral anticoagulants (NOAC) plus aspirin and a P2Y12 inhibitor. At present, four clinical Phase IIIb trials using dabigatran, rivaroxaban, apixaban, or edoxaban, were published. These studies assessed the impact of NOACs as a part of DAT therapy vs. triple therapy. Compared with triple therapy, NOAC-based DAT has been shown to be associated with reduced major bleeding as well as intracranial haemorrhages. The benefit, however, is somewhat counterbalanced by a higher risk of stent-related ischaemia during the early phase of dual therapy. Thus, triple therapy after stenting is appropriate for at least 14 days with a maximum of 30 days. Thereafter, DAT including a NOAC is the therapy of choice in AF PCI patients to reduce the risk of bleeding during a 1 year of follow-up compared to VKA-based regimes. The present review summarizes the published study results and demonstrates differences in trial design and reported outcomes.
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians

We provide sample complexity upper bounds for agnostically learning multivariate Gaussians under the constraint of approximate differential privacy. These are the first finite sample upper bounds for general Gaussians which do not impose restrictions on the parameters of the distribution. Our bounds are near-optimal in the case when the covariance is known to be the identity, and conjectured to be near-optimal in the general case. From a technical standpoint, we provide analytic tools for arguing the existence of global "locally small" covers from local covers of the space. These are exploited using modifications of recent techniques for differentially private hypothesis selection. Our techniques may prove useful for privately learning other distribution classes which do not possess a finite cover.
Cusped spaces and quasi-isometries of relatively hyperbolic groups

A group $\Gamma$ with a family of subgroups $\mathbb{P}$ is relatively hyperbolic if $\Gamma$ admits a cusp-uniform action on a proper $\delta$--hyperbolic space. We show that any two such spaces for a given group pair are quasi-isometric, provided the spaces have "constant horospherical distortion," a condition satisfied by Cannon--Cooper's augmented Cayley graph and by all negatively curved symmetric spaces. Consequently the Bowditch boundary admits a canonical quasisymmetric structure, which coincides with the "naturally occurring" quasisymmetric structure of the symmetric space when considering lattices in rank one symmetric spaces. We show that a group $\Gamma$ is a lattice in a negatively curved symmetric space $X$ if and only if a cusped space for $\Gamma$ is quasi-isometric to the symmetric space. We also prove an ideal triangle characterization of the $\delta$--hyperbolic spaces with uniformly perfect boundary due to Meyer and Bourdon--Kleiner. An appendix concerns the equivalence of several definitions of conical limit point found in the literature.
GASP XXIX – unwinding the arms of spiral galaxies via ram-pressure stripping

We present the first study of the effect of ram pressure ‘unwinding’ the spiral arms of cluster galaxies. We study 11 ram-pressure stripped galaxies from GASP (GAs Stripping Phenomena in galaxies) in which, in addition to more commonly observed ‘jellyfish’ features, dislodged material also appears to retain the original structure of the spiral arms. Gravitational influence from neighbours is ruled out and we compare the sample with a control group of undisturbed spiral galaxies and simulated stripped galaxies. We first confirm the unwinding nature, finding that the spiral arm pitch angle increases radially in 10 stripped galaxies and also simulated face-on and edge-on stripped galaxies. We find only younger stars in the unwound component, while older stars in the disc remain undisturbed. We compare the morphology and kinematics with simulated ram-pressure stripping galaxies, taking into account the estimated inclination with respect to the intracluster medium (ICM) and find that in edge-on stripping, unwinding can occur due to differential ram pressure caused by the disc rotation, causing stripped material to slow and ‘pile up’. In face-on cases, gas removed from the outer edges falls to higher orbits, appearing to ‘unwind’. The pattern is fairly short-lived (<0.5 Gyr) in the stripping process, occurring during first infall and eventually washed out by the ICM wind into the tail of the jellyfish galaxy. By comparing simulations with the observed sample, we find that a combination of face-on and edge-on ‘unwinding’ effects is likely to be occurring in our galaxies as they experience stripping with different inclinations with respect to the ICM.
Validation of Oximetry for Diagnosing Obstructive Sleep Apnea in a Clinical Setting

A large epidemiological study using oximetry to analyze obstructive sleep apnea (OSA) and metabolic comorbidities was performed in Japan; however, reliability and validity of oximetry in the Japanese population remains poorly understood. In this study, oximetry data from the epidemiological study were compared with data from clinically performed polysomnography (PSG) and out-of-center sleep testing (OCST) in epidemiological study participants who later attended our outpatient units. The oxygen desaturation index (ODI) from oximetry showed a moderate positive relationship (correlation coefficient r = 0.561, p < 0.001) with apnea/hypopnea data from PSG/OCST. The area under the receiver operating characteristic curve showed moderate accuracy of this method in the detection of moderate-to-severe or severe OSA. However, the optimal ODI thresholds to detect moderate-to-severe OSA and severe OSA were the same (ODI > 20.1). Oximetry may be a useful tool for screening moderate-to-severe or severe sleep apnea. However, it may be difficult to set an appropriate threshold to distinguish between moderate and severe sleep apnea by oximetry alone.