Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence of a Proximal-like Algorithm in the Presence of Computational Errors

We study the convergence of a proximal-like minimization algorithm using Bregman functions. We extend the convergence results by Censor and Zenios (1992) and by Chen and Teboulle (1993) by showing that the convergence of the algorithm is preserved in the presence of computational errors.
Fiber optic probes for linear and nonlinear Raman applications – Current trends and future development

This review focuses on fiber optic probes for linear and nonlinear Raman spectroscopy, especially for medical applications. It aims at providing an overview over contemporary technology, recent first clinical trials, and helps identifying future developments necessary to bring the emerging technology to clinical end users. After a short introduction to linear and nonlinear Raman spectroscopic modalities, general design considerations will be discussed and compared to common fiber probe setups. Subsequently, examples for medical applications of fiber optic Raman probes will be given concentrating on probes for linear Raman spectroscopy as these devices are technologically more mature compared to their counterparts based on nonlinear Raman spectroscopy. The review also includes a brief summary of first multimodal fiber optic probes and highlights their benefits for clinical applications. Finally, probes are introduced which employ either nonlinear Raman spectroscopy or surface enhanced spectroscopy.
Optical and infrared observations of bright comets in the range 0.5 micrometers to 20 micrometers

The paper reviews IR observations of comets Ikeya-Seki 1965 VIII, Bennett 1970 II, Kohoutek 1973 XII, Bradfield 1974 III, Kobayashi-Berger-Milon 1975 IX, West 1976 VI, Bradfield 1980t, and P/Encke. It is shown that four of the five dusty comets observed (1970 II, 1974 III, 1976 VI, and 1980t) exhibited a silicate signature at 10 and 18 microns, indicating the presence of small refractory grains less than 5 microns in radius, and that the antitail of the fifth (1973 XII) did not have the silicate feature, suggesting the presence of large particles more than 30 microns in radius. A detailed study of Comet 1975 IX is discussed which indicates thermal emission from large grains. The dependence of comet brightness on diaphragm diameter is found to suggest a dust model in which the number of grains satisfies the continuity equation.
Mothers' and fathers' reflective functioning and its association with parenting behaviors and cortisol reactivity during a conflict interaction with their adolescent children.

We assessed parental reflective functioning (PRF) with the Parent Development Interview-Revised and investigated its association with parenting behaviors, that is, autonomy support and psychological control (operationalized in terms of behaviors promoting and undermining autonomy relatedness), and stress responses (cortisol reactivity) during a parent-child conflict interaction task (Family Interaction Task). Participants were 40 mothers and 28 fathers, who took part in the study together with their adolescent children (N = 49). Mothers had significantly lower PRF and displayed more psychologically controlling behaviors in the interaction with their children than fathers. Rather than sex per se, high levels of PRF were the best predictors of autonomy support, whereas lower levels of PRF predicted more psychological control. Higher levels of PRF were also the best predictor for lower levels of parenting stress. Stress in the context of parenting was neither related to autonomy support nor to psychological control, which were best predicted by divorced family status and, for psychological control only, by parental sex. The findings point to the potential utility of interventions aimed at improving PRF, especially in divorced families, given its protective effect on parenting behaviors and stress in the context of parenting. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Identity and uncertainty: art-mediated medical student reflections in a time of transition

ABSTRACT Medical education comprises intense periods of transition, which can significantly impact student well-being, as well as personal and professional development. In 2020, medical students navigating transitions from pre-clinical to clinical roles were also experiencing the historic forces of the COVID-19 pandemic and ongoing societal reckoning with systemic injustice and racism, likely heightening the usual challenges associated with these transitions. Reflection has been suggested as a tool for facilitating such transitions, and arts-mediated approaches hold promise in inspiring authentic reflection, yet they are rarely used to prompt medical student reflection. This article describes common themes in medical students’ reflections on a specific period of transition during a unique moment in history, via qualitative analysis of their narrative responses to visual arts-mediated reflective prompts. The authors used a visual arts-based activity to explore medical students’ hopes and concerns as they transitioned to clinical clerkships between the 2019–2020 and 2020–2021 academic years at one academic institution. Qualitative analysis using an exploratory constructivist approach revealed that students’ reflections often focused on identity within three main themes: the personal self, the professional self, and the social self. Within these categories, subthemes included uncertainty and concerns focusing on medical training and knowledge, the sense of hope and value inherent to their social connections, critiques of the culture of medical education, and reflections on complicity and responsibility in racial injustice. This article not only provides a cross-sectional snapshot of the experiences of medical students during a historic moment, but also provides themes to guide discussions on training transitions and describes a low-cost, adaptable approach to facilitating deep exploration and reflection on tumultuous moments in training.
Liver and spleen volumes are associated with prognosis of compensated and decompensated cirrhosis and parallel its natural history

Abstract Objective Cirrhosis is characterized by the complex interplay among biological, histological and haemodynamic events. Liver and spleen remodelling occur throughout its natural history, but the prognostic role of these volumetric changes is unclear. We evaluated the relationship between volumetric changes assessed by multidetector computerised tomography (MDCT) and landmark features of cirrhosis. Methods We included consecutive cirrhotic patients who underwent liver transplantation (LT) or hepatocellular carcinoma (HCC) resection in whom dynamic MDCT was available. Different volumetric indices were calculated. Fibrosis was evaluated by the collagen proportional area and Laennec sub‐stages. Correlation and logistic regression analysis were performed to explore associations of volumetric indexes and fibrosis with key prognostic features across the clinical stages of cirrhosis. Results 185 patients were included (146 LT; 39 HCC); the predominant aetiology was viral hepatitis (51.35%); 65.9% had decompensated disease and 85.08% clinically significant portal hypertension (CSPH). The standardised liver volume and liver‐spleen volume ratio negatively correlated with Model for End‐stage Liver Disease (MELD), albumin and hepatic venous pressure gradient (HVPG) and were significantly lower in decompensated patients. The liver segmental volume ratio (segments I–III/segments IV–VIII) best captured the characteristic features of the compensated phase, showing a positive correlation with HVPG and a good discrimination between patients with and without CSPH and varices. Volumetric changes and fibrosis severity were independently associated with key prognostic events, with no association between these two parameters. Conclusions Liver and spleen volumetric indices evolve differently along the natural history of cirrhosis and are associated with key prognostic factors in each phase, regardless of fibrosis severity and portal hypertension.
Urban rats as carriers of invasive Salmonella Typhimurium sequence type 313, Kisangani, Democratic Republic of Congo

Background Invasive non-typhoidal Salmonella (iNTS–mainly serotypes Enteritidis and Typhimurium) are major causes of bloodstream infections in children in sub-Saharan Africa, but their reservoir remains unknown. We assessed iNTS carriage in rats in an urban setting endemic for iNTS carriage and compared genetic profiles of iNTS from rats with those isolated from humans. Methodology/Principal findings From April 2016 to December 2018, rats were trapped in five marketplaces and a slaughterhouse in Kisangani, Democratic Republic of the Congo. After euthanasia, blood, liver, spleen, and rectal content were cultured for Salmonella. Genetic relatedness between iNTS from rats and humans—obtained from blood cultures at Kisangani University Hospital—was assessed with multilocus variable-number tandem repeat (VNTR) analysis (MLVA), multilocus sequence typing (MLST) and core-genome MLST (cgMLST). 1650 live-capture traps yielded 566 (34.3%) rats (95.6% Rattus norvegicus, 4.4% Rattus rattus); 46 (8.1%) of them carried Salmonella, of which 13 had more than one serotype. The most common serotypes were II.42:r:- (n = 18 rats), Kapemba (n = 12), Weltevreden and Typhimurium (n = 10, each), and Dublin (n = 8). Salmonella Typhimurium belonged to MLST ST19 (n = 7 rats) and the invasive ST313 (n = 3, isolated from deep organs but not from rectal content). Sixteen human S. Typhimurium isolates (all ST313) were available for comparison: MLVA and cgMLST revealed two distinct rat-human clusters involving both six human isolates, respectively, i.e. in total 12/16 human ST313 isolates. All ST313 Typhimurium isolates from rats and humans clustered with the ST313 Lineage 2 isolates and most were multidrug resistant; the remaining isolates from rats including S. Typhimurium ST19 were pan-susceptible. Conclusion The present study provides evidence of urban rats as potential reservoirs of S. Typhimurium ST313 in an iNTS endemic area in sub-Saharan Africa.
Sion: Elastic Serverless Cloud Storage

Cloud object storage such as AWS S3 is cost-effective and highly elastic but relatively slow, while high-performance cloud storage such as AWS ElastiCache is expensive and provides limited elasticity. We present a new cloud storage service called ServerlessMem-ory, which stores data using the memory of serverless functions. ServerlessMemory employs a time-window-based data placement strategy to effectively segregate old and new data and provides high elasticity, performance, and a pay-per-access cost model with extremely low cost for a new memory-based storage. We then design and implement Sion 1 , a persistent and elastic cloud storage system, which seamlessly couples the function-based ServerlessMemory layer with a persistent, inexpensive cloud object store layer. Sion enables durability despite function failures using a fast parallel recovery scheme built on the auto-scaling functionality of a FaaS (Function-as-a-Service) platform. We evaluate Sion extensively using three real-world applications and results show that Sion achieves 27 .
Ultramassive Black Holes Formed by Triple Quasar Mergers at z ∼ 2

The origin of rare and elusive ultramassive black holes (UMBH; with M BH > 1010 M ⊙) is an open question. Using the large volume cosmological hydrodynamic simulation ASTRID, we report on the formation of an extremely massive UMBH with M BH ∼ 1011 M ⊙ at z ∼ 2. The UMBH is assembled as a result of two successive mergers of massive galaxies each with stellar mass M * > 3 × 1011 M ⊙ that also produces a bright, rare triple quasar system powered by three ∼109 M ⊙ black holes. The second merger of supermassive black holes (SMBHs) follows the first after 150 Myr. The merger events lead to sustained Eddington accretion onto the central SMBH, forming a UMBH in the center of a massive compact stellar core with M * > 2 × 1012 M ⊙. The strong feedback of the UMBH quenches the surrounding star formation to <10 M ⊙ yr−1 in the inner 50 h −1 kpc region. There are two more UMBHs with M BH > 5 × 1010 M ⊙ at z > 2 in ASTRID that are also produced by major mergers of galaxies, and their progenitors can be observed as quasar triplets of lower luminosity. The rarely observed quasar multiples can be the cradle of UMBHs at high redshift, and likely end up in the center of the most massive clusters.
Predict-and-Update Network: Audio-Visual Speech Recognition Inspired by Human Speech Perception

Audio and visual signals complement each other in human speech perception, so do they in speech recognition. The visual hint is less evident than the acoustic hint, but more robust in a complex acoustic environment, as far as speech perception is concerned. It remains a challenge how we effectively exploit the interaction between audio and visual signals for automatic speech recognition. There have been studies to exploit visual signals as redundant or complementary information to audio input in a synchronous manner. Human studies suggest that visual signal primes the listener in advance as to when and on which frequency to attend to. We propose a Predict-and-Update Network (P&U net), to simulate such a visual cueing mechanism for Audio-Visual Speech Recognition (AVSR). In particular, we first predict the character posteriors of the spoken words, i.e. the visual embedding, based on the visual signals. The audio signal is then conditioned on the visual embedding via a novel cross-modal Conformer, that updates the character posteriors. We validate the effectiveness of the visual cueing mechanism through extensive experiments. The proposed P&U net outperforms the state-of-the-art AVSR methods on both LRS2-BBC and LRS3-BBC datasets, with the relative reduced Word Error Rate (WER)s exceeding 10% and 40% under clean and noisy conditions, respectively.
Transformações de Lorentz e seus invariantes

In this paper, we will show the types of Lorentz transformations, from the most described in books, special Lorentz transformation that relates two inertial systems whose relative velocities are directed along an axis of the respective bases systems. However, we will see a peculiarity that goes unnoticed in this transformation, although they have reported in many books a parallel between the transformation inertial systems, due to the fact that the speed is parallel to an axis, it is actually a semi-parallel processing. The next transformation that we will see is one in which a system moves with a relative speed that has arbitrary direction with respect to a given system, we will show that this transformation may be appointed as non-rotational Lorentz transformation. Before obtain, the later type of transformation, the rotational Lorentz transformation, which is the interface between Special Relativity and General Relativity, we will describe the systems to be rotated, not just inertial systems, show what the characteristics are that define the non-rotational and rotational transformations. The in last topic of this chapter we will also show how the idea of "Thoma’s theory"that uses this transformation to create what he defines as "the proper coordinate axes of the particle"used to obtain the factor 1/2 electron spin. In the last chapter we show how the Lorentz invariants are obtained, quantities measures that are also in different Lorentz reference, with the focus on mass that has erroneously been described in many books, that varies according to the agreement reference system.
Quantum nonlocality in the presence of strong measurement dependence

It is well known that the effect of quantum nonlocality, as witnessed by violation of a Bell inequality, can be observed even when relaxing the assumption of measurement independence, i.e. allowing for the source to be partially correlated with the choices of measurement settings. But what is the minimal amount of measurement independence needed for observing quantum nonlocality? Here we explore this question and consider models with strong measurement-dependent locality, where measurement choices can be perfectly determined in almost all rounds of the Bell test. Yet, we show that quantum nonlocality can still be observed in this scenario, which we conjecture is minimal within the framework we use. We also discuss potential applications in randomness amplification.
Deep Stable Representation Learning on Electronic Health Records

Deep learning models have achieved promising disease prediction performance of the Electronic Health Records (EHR) of patients. However, most models developed under the I.I. D. hypothesis fail to consider the agnostic distribution shifts, diminishing the generalization ability of deep learning models to Out-Of-Distribution (OOD) data. In this setting, spurious statistical correlations between procedures and diagnoses that may change in different environments will be exploited, which can cause sub-optimal performances of deep learning models and spurious correlation between historical EHR and future diagnosis. To address this problem, we propose to use a causal representation learning method called Causal Healthcare Embedding (CHE). CHE aims at eliminating the spurious statistical relationship by removing the dependencies between diagnoses and procedures. We introduce the Hilbert-Schmidt Independence Criterion (HSIC) to measure the degree of independence between the embedded diagnosis and procedure features. Based on causal view analyses, we perform the sample weighting technique to get rid of such spurious relationship for the stable learning of EHR across different environments. Moreover, our proposed CHE method can be used as a flexible plug-and-play module to enhance existing deep learning models on EHR. Extensive experiments on two public datasets and five state-of-the-art baselines unequivocally show that CHE can improve the prediction accuracy of deep learning models on out-of-distribution data by a large margin. In addition, the interpretability study shows that CHE could successfully leverage causal structures to reflect a more reasonable contribution of historical records for predictions.
Wavelet-based Loss for High-frequency Interface Dynamics

Generating highly detailed, complex data is a long-standing and frequently considered problem in the machine learning field. However, developing detail-aware generators remains an challenging and open problem. Generative adversarial networks are the basis of many state-of-the-art methods. However, they introduce a second network to be trained as a loss function, making the interpretation of the learned functions much more difficult. As an alternative, we present a new method based on a wavelet loss formulation, which remains transparent in terms of what is optimized. The wavelet-based loss function is used to overcome the limitations of conventional distance metrics, such as L1 or L2 distances, when it comes to generate data with high-frequency details. We show that our method can successfully reconstruct high-frequency details in an illustrative synthetic test case. Additionally, we evaluate the performance when applied to more complex surfaces based on physical simulations. Taking a roughly approximated simulation as input, our method infers corresponding spatial details while taking into account how they evolve. We consider this problem in terms of spatial and temporal frequencies, and leverage generative networks trained with our wavelet loss to learn the desired spatio-temporal signal for the surface dynamics. We test the capabilities of our method with a set of synthetic wave function tests and complex 2D and 3D dynamics of elasto-plastic materials.
Data Provenance via Differential Auditing

Auditing Data Provenance (ADP), i.e., auditing if a certain piece of data has been used to train a machine learning model, is an important problem in data provenance. The feasibility of the task has been demonstrated by existing auditing techniques, e.g., shadow auditing methods, under certain conditions such as the availability of label information and the knowledge of training protocols for the target model. Unfortunately, both of these conditions are often unavailable in real applications. In this paper, we introduce Data Provenance via Differential Auditing (DPDA), a practical framework for auditing data provenance with a different approach based on statistically significant differentials, i.e., after carefully designed transformation, perturbed input data from the target model's training set would result in much more drastic changes in the output than those from the model's non-training set. This framework allows auditors to distinguish training data from non-training ones without the need of training any shadow models with the help of labeled output data. Furthermore, we propose two effective auditing function implementations, an additive one and a multiplicative one. We report evaluations on real-world data sets demonstrating the effectiveness of our proposed auditing technique.
elhmc: An R Package for Hamiltonian Monte Carlo Sampling in Bayesian Empirical Likelihood

In this article, we describe a {\tt R} package for sampling from an empirical likelihood-based posterior using a Hamiltonian Monte Carlo method. Empirical likelihood-based methodologies have been used in Bayesian modeling of many problems of interest in recent times. This semiparametric procedure can easily combine the flexibility of a non-parametric distribution estimator together with the interpretability of a parametric model. The model is specified by estimating equations-based constraints. Drawing an inference from a Bayesian empirical likelihood (BayesEL) posterior is challenging. The likelihood is computed numerically, so no closed expression of the posterior exists. Moreover, for any sample of finite size, the support of the likelihood is non-convex, which hinders the fast mixing of many Markov Chain Monte Carlo (MCMC) procedures. It has been recently shown that using the properties of the gradient of log empirical likelihood, one can devise an efficient Hamiltonian Monte Carlo (HMC) algorithm to sample from a BayesEL posterior. The package requires the user to specify only the estimating equations, the prior, and their respective gradients. An MCMC sample drawn from the BayesEL posterior of the parameters, with various details required by the user is obtained.
A multi-scale framework for neural network enhanced methods to the solution of partial differential equations

In the present work, a multi-scale framework for neural network enhanced methods is proposed for approximation of function and solution of partial diﬀerential equations (PDEs). By introducing the multi-scale concept, the total solution of the target problem could be decomposed into two parts, i.e. the coarse scale solution and the ﬁne scale solution. In the coarse scale, the conventional numerical methods (e.g. ﬁnite element methods) are applied and the coarse scale solution could be obtained. In the ﬁne scale, the neural networks is introduced to formulate the solution. The custom loss functions are developed by taking into account the governing equations and boundary conditions of PDEs, the constraints and the interaction from coarse scale. The proposed methods are illustrated and examined by various of testing cases.