Miracle-dz/UniFAR-FTUs huggingface dataset:
Elastic Founder Graphs Improved and Enhanced

Indexing labeled graphs for pattern matching is a central challenge of pangenomics. Equi et al. (Algorithmica, 2022) developed the Elastic Founder Graph ($\mathsf{EFG}$) representing an alignment of $m$ sequences of length $n$, drawn from alphabet $\Sigma$ plus the special gap character: the paths spell the original sequences or their recombination. By enforcing the semi-repeat-free property, the $\mathsf{EFG}$ admits a polynomial-space index for linear-time pattern matching, breaking through the conditional lower bounds on indexing labeled graphs (Equi et al., SOFSEM 2021). In this work we improve the space of the $\mathsf{EFG}$ index answering pattern matching queries in linear time, from linear in the length of all strings spelled by three consecutive node labels, to linear in the size of the edge labels. Then, we develop linear-time construction algorithms optimizing for different metrics: we improve the existing linearithmic construction algorithms to $O(mn)$, by solving the novel exclusive ancestor set problem on trees; we propose, for the simplified gapless setting, an $O(mn)$-time solution minimizing the maximum block height, that we generalize by substituting block height with prefix-aware height. Finally, to show the versatility of the framework, we develop a BWT-based $\mathsf{EFG}$ index and study how to encode and perform document listing queries on a set of paths of the graphs, reporting which paths present a given pattern as a substring. We propose the $\mathsf{EFG}$ framework as an improved and enhanced version of the framework for the gapless setting, along with construction methods that are valid in any setting concerned with the segmentation of aligned sequences.
Exploring Smart Commercial Building Occupants’ Perceptions and Notification Preferences of Internet of Things Data Collection in the United States

Data collection through the Internet of Things (IoT) devices, or smart devices, in commercial buildings enables possibilities for increased convenience and energy efficiency. However, such benefits face a large perceptual challenge when being implemented in practice, due to the different ways occupants working in the buildings understand and trust in the data collection. The semi-public, pervasive, and multi-modal nature of data collection in smart buildings points to the need to study occupants’ understanding of data collection and notification preferences. We conduct an online study with 492 participants in the US who report working in smart commercial buildings regarding: 1) awareness and perception of data collection in smart commercial buildings, 2) privacy notification preferences, and 3) potential factors for privacy notification preferences. We find that around half of the participants are not fully aware of the data collection and use practices of IoT even though they notice the presence of IoT devices and sensors. We also discover many misunderstandings around different data practices. The majority of participants want to be notified of data practices in smart buildings, and they prefer push notifications to passive ones such as websites or physical signs. Surprisingly, mobile app notification, despite being a popular channel for smart homes, is the least preferred method for smart commercial buildings.
The Impact of Feature Selection and Transformation on Machine Learning Methods in Determining the Credit Scoring

Banks utilize credit scoring as an important indicator of financial strength and eligibility for credit. Scoring models aim to assign statistical odds or probabilities for predicting if there is a risk of nonpayment in relation to many other factors which may be involved in. This paper aims to illustrate the beneficial use of the eight machine learning (ML) methods (Support Vector Machine, Gaussian Naive Bayes, Decision Trees, Random Forest, XGBoost, K-Nearest Neighbors, Multi-layer Perceptron Neural Networks) and Logistic Regression in finding the default risk as well as the features contributing to it. An extensive comparison is made in three aspects: (i) which ML models with and without its own wrapper feature selection performs the best; (ii) how feature selection combined with appropriate data scaling method influences the performance; (iii) which of the most successful combination (algorithm, feature selection, and scaling) delivers the best validation indicators such as accuracy rate, Type I and II errors and AUC. An open-access credit scoring default risk data sets on German and Australian cases are taken into account, for which we determine the best method, scaling, and features contributing to default risk best and compare our findings with the literature ones in related. We illustrate the positive contribution of the selection method and scaling on the performance indicators compared to the existing literature.
Exploring spin antisymmetrized geminal power Ansätze for strongly correlated spin systems

The antisymmetrized geminal power (AGP), a wave function equivalent to number-projected Hartree--Fock--Bogoliubov (HFB), and number-projected Bardeen--Cooper--Schrieffer (BCS) when working in the paired (natural orbitals) basis, has proven to be an excellent reference for strong pairing interactions. Several correlation methods have also been applied on top of AGP. In this work, we show how AGP can also be applied to spin systems by simply basing its formulation on a spin $su(2)$ algebra. We here implement spin AGP and spin AGP-based correlation techniques and benchmark them on the XXZ and $\mathrm{J_1-J_2}$ Heisenberg models, both in 1 and 2 dimensions. Our results indicate that spin AGP is a promising starting point for modeling spin systems.
Introducing a Research Program for Quantum Humanities: Applications

Quantum computing is a rapidly developing field in the second wave of quantum development, with the potential to revolutionize a wide range of industries and fields of study. As the capabilities of quantum computers continue to advance, they have the potential to significantly impact society and the way we live, work, and think. This makes it important for scholars from a variety of disciplines to come together and consider the implications of these technologies. How this was done has already been explained and published in an abstract way in a joint research paper. But how exactly these abstract theoretical approaches come into an implementation could not be shown so far. The present article shows exactly this.
Geometry of Language

In this article, we present a fresh perspective on language, combining ideas from various sources, but mixed in a new synthesis. As in the minimalist program, the question is whether we can formulate an elegant formalism, a universal grammar or a mechanism which explains significant aspects of the human faculty of language, which in turn can be considered a natural disposition for the evolution and deployment of the diverse human languages. We describe such a mechanism, which differs from existing logical and grammatical approaches by its geometric nature. Our main contribution is to explore the assumption that sentence recognition takes place by forming chains of tokens representing words, followed by matching these chains with pre-existing chains representing grammatical word orders. The aligned chains of tokens give rise to two- and three-dimensional complexes. The resulting model gives an alternative presentation for subtle rules, traditionally formalized using categorial grammar.
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges

In this work we present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on prior work, the system retrieves trial publications matching a query specifying a combination of condition, intervention(s), and outcome(s), and ranks these according to sample size and estimated study quality.The top-k such studies are passed through a neural multi-document summarization system, yielding a synopsis of these trials. We consider two architectures: A standard sequence-to-sequence model based on BART, and a multi-headed architecture intended to provide greater transparency and controllability to end-users.Both models produce fluent and relevant summaries of evidence retrieved for queries, but their tendency to introduce unsupported statements render them inappropriate for use in this domain at present.The proposed architecture may help users verify outputs allowing users to trace generated tokens back to inputs. The demonstration video can be found at https://vimeo.com/735605060The prototype, source code, and model weights are available at: https://sanjanaramprasad.github. io/trials-summarizer/
Structural properties of composites of polyvinylidene fluoride and mechanically activated BaTiO3 particles

Nanocomposites of electroactive ceramics and ferroelectric polymers exploit favorable features of the matrix polymer and the nanostructured filler to produce new functional materials for pressure and IR sensors. In this study, the influence of mechanical activation of barium titanate (BaTiO3) particles on the structural properties of BaTiO3/polyvinylidene fluoride (PVDF) nanocomposites was investigated. Nanocomposite films were prepared by the solution casting method and characterized by scanning electron microscopy, x-ray diffraction and Raman spectroscopy. It was found that mechanically activated fillers promote the formation of a ferroelectric β-phase during crystallization of PVDF.
Challenges in Explanation Quality Evaluation

While much research focused on producing explanations, it is still unclear how the produced explanations' quality can be evaluated in a meaningful way. Today's predominant approach is to quantify explanations using proxy scores which compare explanations to (human-annotated) gold explanations. This approach assumes that explanations which reach higher proxy scores will also provide a greater benefit to human users. In this paper, we present problems of this approach. Concretely, we (i) formulate desired characteristics of explanation quality, (ii) describe how current evaluation practices violate them, and (iii) support our argumentation with initial evidence from a crowdsourcing case study in which we investigate the explanation quality of state-of-the-art explainable question answering systems. We find that proxy scores correlate poorly with human quality ratings and, additionally, become less expressive the more often they are used (i.e. following Goodhart's law). Finally, we propose guidelines to enable a meaningful evaluation of explanations to drive the development of systems that provide tangible benefits to human users.
A Multi-layer Perceptron Neural Network for Predicting the Diagnosis of Osteoporosis in Women Using Physical Activity Factors

Background: Osteoporosis (OP) is a bone disease caused by a decrease in bone mineral density (BMD). OP is common in women because BMD gradually decreases after age 35. OP due to decreased BMD is highly likely to cause fatal traumatic injuries such as hip fracture. Purpose: To develop and evaluate a multi-layer perceptron neural network model that predicts OP using physical characteristics and activity factors of adult women over the age of 35 whose BMD begins to decline. Method: Data from KNHANES were used to develop a multi-layer perceptron model for predicting OP. Data preprocessing included variable selection and sample balancing, and LASSO was used for feature selection. The model used 5 hidden layers, dropout and batch normalization and was evaluated using evaluation scores such as accuracy and recall score calculated from a confusion matrix. Result: Models were trained and evaluated to predict OP using selected features including age, quality of life index, weight, grip strength and average working hours per week. The model achieved 76.8% accuracy, 74.5% precision, 80.5% recall, 77.4% F1 score, and 74.8% ROC AUC. Conclusion: A multi-layer perceptron neural network for predicting OP diagnosis using physical characteristics and activity factors in women aged 35 years or older showed relatively good performance. Since the selected variables can be easily measured through surveys, assessment tool, and digital hand dynamometer, this model will be useful for screening elderly women with OP or not in areas with poor medical facilities or difficult access.
Archetypal analysis of COVID-19 in Montana, USA, March 13, 2020 to April 26, 2022

Given the potential consequences of infectious diseases, it is important to understand how broad scale incidence variability influences the probability of localized outbreaks. Often, these infectious disease data can involve complex spatial patterns intermixed with temporal trends. Archetypal Analysis is a method to mine complex spatiotemporal epidemiological data, and can be used to discover the dynamics of spatial patterns. The application of Archetypal Analysis to epistemological data is relatively new, and here we present one of the first applications using COVID-19 data from March 13, 2020 to April 26, 2022 in the counties of Montana, USA. We present three views of the data set with Archetypal Analysis. First, we evaluate the entire 56 county data set. Second, we compute mutual information of the 56 counties time series to remove counties whose dynamics are mainly independent from most of the other counties. We choose the top 17 counties ranked in terms of increasing total mutual information. Finally, to compare how population size might influence results, we conducted an analysis with 10 of the largest counties. Using the Archetypal Analysis results, we analyze the disease outbreaks across Montana, comparing and contrasting the three different cases and showing how certain counties can be found in distinct sets of archetypes. Using the reconstruction time series, we show how each outbreak had a unique trajectory across the state in terms of the archetypes.
High-power cw bismuth-fiber lasers

Continuous-wave lasing of Bi-doped fiber lasers in a wavelength range of 1150-1215 nm with a power of up to 15 W has been obtained for the first time. The unsaturable optical losses in Bi-doped fibers have been revealed and their influence on the Bi-laser operation is investigated. Frequency doubling of the Bi-fiber laser radiation is demonstrated that can be used as a yellow light source (wavelength of 580 nm).
Integrated analyses of single-cell RNA-seq public data reveal the gene regulatory network landscape of respiratory epithelial and peripheral immune cells in COVID-19 patients

Introduction: Infection with SARS-CoV-2 leads to coronavirus disease 2019 (COVID-19), which can result in acute respiratory distress syndrome and multiple organ failure. However, its comprehensive influence on pathological immune responses in the respiratory epithelium and peripheral immune cells is not yet fully understood. Methods: In this study, we integrated multiple public scRNA-seq datasets of nasopharyngeal swab and peripheral blood results to investigate the gene regulatory networks (GRNs) of healthy individuals and COVID-19 patients with mild/moderate and severe disease, respectively. Similar and dissimilar regulons were identified within or between epithelial and immune cells during COVID-19 severity progression. The relative transcription factors (TFs) and their targets were used to construct GRNs among different infection sites and conditions. Results: Between respiratory epithelial and peripheral immune cells, different TFs tended to be used to regulate the activity of a cell between healthy individuals and COVID-19 patients, although they had some TFs in common. For example, XBP1, FOS, STAT1, and STAT2 were activated in both the epithelial and immune cells of virus-infected individuals. In contrast, severe COVID-19 cases exhibited activation of CEBPD in peripheral immune cells, while CEBPB was exclusively activated in respiratory epithelial cells. Moreover, in patients with severe COVID-19, CEBPD upregulated S100A8 and S100A9 in CD14 and CD16 monocytes, while S100A9 genes were co-upregulated by different regulators (SPEDEF and ELF3) in goblet and squamous cells. The cell-cell communication analysis suggested that epidermal growth factor receptor signaling among epithelial cells contributes to mild/moderate disease, and chemokine signaling among immune cells contributes to severe disease. Conclusions: This study identified cell type- and condition-specific regulons in a wide range of cell types from the initial infection site to the peripheral blood, and clarified the diverse mechanisms of maladaptive responses to SARS-CoV-2 infection.
Molecular pathology of acute respiratory distress syndrome, mechanical ventilation and abnormal coagulation in severe COVID-19

Systemic inflammation in critically ill patients can lead to serious consequences such as acute respiratory distress syndrome (ARDS), a condition characterized by the presence of lung inflammation, edema, and impaired gas exchange, associated with poor survival. Understanding molecular pathobiology is essential to improve critical care of these patients. To this end, we use multimodal profiles of SARS-CoV-2 infected hospitalized participants to the Biobanque Quebecoise de la COVID-19 (BQC-19) to characterize endophenotypes associated with different degrees of disease severity. Proteomic, metabolomic, and genomic characterization supported a role for neutrophil-associated procoagulant activity in severe COVID-19 ARDS that is inversely correlated with sphinghosine-1 phosphate plasma levels. Fibroblast Growth Factor Receptor (FGFR) and SH2-containing transforming protein 4 (SHC4) signaling were identified as molecular features associated with endophenotype 6 (EP6). Mechanical ventilation in EP6 was associated with alterations in lipoprotein metabolism. These findings help define the molecular mechanisms related to specific severe outcomes, that can be used to identify early unfavorable clinical trajectories and treatable traits to improve the survival of critically ill patients.
NN50 and pNN50, two time-domain heart rate variability parameters were associated with 30-day all-cause mortality in patients admitted to intensive care unit: A Retrospective Study of the MIMIC-IV Database

Objective: Our study aims to evaluate the association between heart rate variability (HRV) and short and long-term prognosis in patients admitted to intensive care unit (ICU). Methods and Results: Adult patients continuously monitored for over 24h in ICUs from the MIMIC-IV Waveform Database were recruited in our study. Twenty HRV-related variables (8 time-domain, 6 frequency-domain; and 6 nonlinear variables) were calculated based on RR intervals. The association between HRV and 30-day all-cause mortality was assessed. Ninety-three patients met the inclusion criteria and were classified into 30-day survivor group and non-survivor groups based on their survival status. The 30-day all-cause mortality rate was 17.2%. NN50 and pNN50 were both significantly higher in non-survivors compared to survivors, whereas the rest of the time-domain, frequency domain and non-linear HRV parameters did not differ significantly between the two groups (all P >0.05). In addition, at 180 days after admission, non-survivors had significantly higher levels of NN50 and rMSSD than the survivors. However, NN50 was not an independent predictor of 30-day all-cause mortality in patients by multivariate COX regression analysis (HR, 1.0; 95% CI, 1.000 - 1.001; P =0.594). The Area Under the Curve (AUC), cut-off value, sensitivity and specificity of NN50 for predicting 30-day all-cause mortality using ROC were 0.67, 799, 0.813 and 0.584, respectively. Plotting Kaplan-Meier analysis using this cut-off value showed that patients with high NN50 had considerably greater 30-day all-cause mortality than those with low NN50 (P < 0.001). Conclusion: NN50 and pNN50 are associated with elevated 30-day all-cause mortality in ICU patients but are not independent predictors of all-cause mortality using multivariate COX regression analysis.
Lattice implementation of some recursive parameter-estimation algorithms†

Linear dynamic models of plants are usually parametrized by the coefficients of difference equations. Lattice structures and their reflection coefficients provide an alternative parametrization that offers several advantages, including numerical robustness, computational efficiency and ease of hardware implementation. The recursive square-root normalized lattice versions of the following well-known parameter-estimation algorithms are presented ; recursive least-squares, recursive instrumental variable, extended least-squares, and recursive maximum-likelihood
Stochastic backscatter in large-eddy simulations of boundary layers

The ability of a large-eddy simulation to represent the large-scale motions in the interior of a turbulent flow is well established. However, concerns remain for the behaviour close to rigid surfaces where, with the exception of low-Reynolds-number flows, the large-eddy description must be matched to some description of the flow in which all except the larger-scale ‘inactive’ motions are averaged. The performance of large-eddy simulations in this near-surface region is investigated and it is pointed out that in previous simulations the mean velocity profile in the matching region has not had a logarithmic form. A number of new simulations are conducted with the Smagorinsky (1963) subgrid model. These also show departures from the logarithmic profile and suggest that it may not be possible to eliminate the error by adjustments of the subgrid lengthscale. An obvious defect of the Smagorinsky model is its failure to represent stochastic subgrid stress variations. It is shown that inclusion of these variations leads to a marked improvement in the near-wall flow simulation. The constant of proportionality between the magnitude of the fluctuations in stress and the Smagorinsky stresses has been empirically determined to give an accurate logarithmic flow profile. This value provides an energy backscatter rate slightly larger than the dissipation rate and equal to idealized theoretical predictions (Chasnov 1991).
Preheated restorative composite resin for luting ceramic laminate veneers: An optimized technique report

Resin cements are traditionally used to lute ceramic laminate veneers due to their lower viscosity, which facilitates a fast restoration seating. However, resin cements have lower mechanical properties compared to restorative composite resins. Thus, restorative composite resin is an alternative luting agent with lower marginal degradation as a potential advantage for clinical longevity. This article presents an application of preheated restorative composite resin for adhesive luting of laminate veneers with a predictable clinical technique for seating and marginal quality. By addressing important factors that influence film thickness, the predictable presented workflow should overcome this major concern when luting with restorative composite resin, therefore enabling the benefits of using a restorative material with better mechanical properties without the drawback of higher film thickness. Considering the clinical evidence that the adhesive interface between the dental substrate and restoration is the weak link of adhesive indirect restorations, bonding the restoration with preheated restorative composite resins (PRCR) may provide an interface filled with a restorative resin material, presenting optimized mechanical properties. Key words:Resin cements, ceramic laminate veneers.