Miracle-dz/UniFAR-FTUs huggingface dataset:
Malaria Threatens to Bounce Back in Abergele District, Northeast Ethiopia: Five-Year Retrospective Trend Analysis from 2016-2020 in Nirak Health Center

Background In Sub-Saharan African countries, malaria is a leading cause of morbidity and mortality. In Ethiopia, malaria is found in three-fourths of its land mass with more than 63 million people living in malaria endemic areas. Nowadays, Ethiopia is implementing a malaria elimination program with the goal of eliminating the disease by 2030. To assist this goal, the trends of malaria cases should be evaluated with a function of time in different areas of the country to develop area-specific evidence-based interventions. Therefore, this study was aimed at analysing a five year trend of malaria in Nirak Health Center, Abergele District, Northeast Ethiopia, from 2016 to 2020. Methods A retrospective study was conducted at Nirak Health Center, Abergele District, Northeast Ethiopia from February to April 2021. Five-year (2016 to 2020) retrospective data were reviewed from the malaria registration laboratory logbook. The sociodemographic and malaria data were collected using a predesigned data collection sheet. Data were entered, cleaned, and analysed using SPSS version 26. Results In the five-year period, a total of 19,433 malaria suspected patients were diagnosed by microscopic examination. Of these, 6,473 (33.3%) were positive for malaria parasites. Of the total confirmed cases, 5,900 (91.2%) were P. falciparum and 474 (7.2%) were P. vivax. Majority of the cases were males (62.2%) and in the age group of 15-45 years old (52.8%). The findings of this study showed an increasing trend in malaria cases in the past five years (2016-2020). The maximum number of confirmed malaria cases reported was in the year 2020, while the minimum number of confirmed malaria cases registered was in 2016. Regarding the seasonal distribution of malaria, the highest number of malaria cases (55.2%) was observed in Dry season (September to January) and also the least (15.9%) was observed in Autumn (March to May) replaced by the least (21.6%) was observed in Rainy season (June to August), that is, the major malaria transmission season in Ethiopia and the least (15.9%) was observed in autumn (March to May). Conclusion The trends of malaria in Nirak Health Center showed steadily increasing from the year 2016–2020, and the predominant species isolated was P. falciparum. This showed that the malaria control and elimination strategy in the area were not properly implemented or failed to achieve its designed goal. Therefore, this finding alarms the local governments and other stack holders urgently to revise their intervention strategies and take action in the locality.
Infection prevention requirements for the medical care of immunosuppressed patients: recommendations of the Commission for Hospital Hygiene and Infection Prevention (KRINKO) at the Robert Koch Institute

In Germany, guidelines for hygiene in hospitals are given in form of recommendations by the Commission for Hospital Hygiene and Infection Prevention (Kommission für Krankenhaushygiene und Infektionsprävention, “KRINKO”). The KRINKO and its voluntary work are legitimized by the mandate according to § 23 of the Infection Protection Act (Infektionsschutzgesetz, “IfSG”). The original German version of this document was published in February 2021 and has now been made available to the international professional public in English. The guideline provides recommendations on infection prevention and control for immunocompromised individuals in health care facilities. This recommendation addresses not only measures related to direct medical care of immunocompromised patients, but also management aspects such as surveillance, screening, antibiotic stewardship, and technical/structural aspects such as patient rooms, air quality, and special measures during renovations.
Benefits and safety of landiolol for rapid rate control in patients with atrial tachyarrhythmias and acute decompensated heart failure

Abstract Atrial tachyarrhythmias often occur in patients with worsening heart failure (HF), and the development of atrial tachyarrhythmias in acute decompensated HF (ADHF) causes an uncontrolled heart rate (HR) and leads to further exacerbation of HF and persistence of a decompensated HF state. Landiolol, a short-acting intravenous beta-1 blocker, shows very high cardiac beta-1 selectivity and has a very short elimination half-life of approximately 4 min. As shown in several reports, the benefit of intravenous landiolol is that it lowers the ventricular rate early after the start of use without markedly deteriorating haemodynamics. After the cardiac status is stabilized by rapid control of HR, subsequent basic HF pharmacotherapy and rhythm control therapies will be effective for improving outcomes. Because of the pharmacokinetic properties of landiolol, if the patient suffers an adverse reaction such as hypotension or bradycardia, such effects can be quickly reversed by tapering the dose or discontinuing use altogether. Based on several clinical studies, this review discusses the efficacy, safety and role of intravenous landiolol in acute HR control in patients with atrial tachyarrhythmias and ADHF.
Processing Method Affects Oral and Gut Microbiome Composition

Abstract Objectives Conducting research on the microbiome has become prevalent, particularly in the area of precision nutrition as nutritional strategies targeting the microbiome are increasingly explored. However, differences in sample processing methods for generating microbial genetic sequencing data could influence observed microbial composition. The objective of this study was to process human oral and gut samples using several common methods to examine the influence of each on microbiota composition. Methods We collected fecal, saliva, and dental swab samples from 10 individuals. Fecal samples were 1) fresh, 2) lyophilized (freeze-dried), 3) frozen then ground in liquid nitrogen, or 4) stored at room temperature (RT) for 72 hours, frozen, then ground in liquid nitrogen. Saliva and dental swabs were 1) fresh (saliva), 2) subjected to the Human Microbiome Project (HMP) initial processing protocol (saliva), or 3) frozen whole upon collection and split into different fractions after thawing: supernatant, pellet, or mixed supernatant and pellet (both saliva and dental). We performed 16S rRNA gene sequencing, used QIIME2 to identify amplicon sequence variants (ASVs) and calculate diversity metrics, and used MaAsLin2 to evaluate the effect of treatment types on relative abundances of microbes. Results For fecal samples, we found significant differences in the relative abundances of 51 ASVs between fresh and lyophilized, four ASVs between fresh and liquid nitrogen post-72 hour at RT, and two ASVs between fresh and liquid nitrogen treated samples. There were differences among both saliva and dental swab fractions. For saliva, the relative abundances of 15 ASVs differed significantly between both mixed and pellet fractions and mixed and supernatant fractions. For dental swabs, the relative abundances of 19 ASVs differed significantly between mixed and pellet fractions and 10 between mixed and supernatant fractions. For all reported significant results, q < 0.05. Conclusions Relative abundances are significantly affected by processing methods for both gut and oral microbiome samples. Therefore it is crucial to exercise caution when comparing microbial composition results across studies that use different processing methods. Funding Sources Research reported in this abstract was supported by a NIH NIDDK T32 Award.
Measuring Representational Harms in Image Captioning

Previous work has largely considered the fairness of image captioning systems through the underspecified lens of “bias. ” In contrast, we present a set of techniques for measuring five types of representational harms, as well as the resulting measurements obtained for two of the most popular image captioning datasets using a state-of-the-art image captioning system. Our goal was not to audit this image captioning system, but rather to develop normatively grounded measurement techniques, in turn providing an opportunity to reflect on the many challenges involved. We propose multiple measurement techniques for each type of harm. We argue that by doing so, we are better able to capture the multi-faceted nature of each type of harm, in turn improving the (collective) validity of the resulting measurements. Throughout, we discuss the assumptions underlying our measurement approach and point out when they do not hold.
Benefits of Additive Noise in Composing Classes with Bounded Capacity

We observe that given two (compatible) classes of functions $\mathcal{F}$ and $\mathcal{H}$ with small capacity as measured by their uniform covering numbers, the capacity of the composition class $\mathcal{H} \circ \mathcal{F}$ can become prohibitively large or even unbounded. We then show that adding a small amount of Gaussian noise to the output of $\mathcal{F}$ before composing it with $\mathcal{H}$ can effectively control the capacity of $\mathcal{H} \circ \mathcal{F}$, offering a general recipe for modular design. To prove our results, we define new notions of uniform covering number of random functions with respect to the total variation and Wasserstein distances. We instantiate our results for the case of multi-layer sigmoid neural networks. Preliminary empirical results on MNIST dataset indicate that the amount of noise required to improve over existing uniform bounds can be numerically negligible (i.e., element-wise i.i.d. Gaussian noise with standard deviation $10^{-240}$). The source codes are available at https://github.com/fathollahpour/composition_noise.
Separable and Equitable Hypergraphs

A k -hypergraph is separable if its vertices admit a certain labeling, and is equitable if the edges of the complete k -hypergraph admit a certain labeling. We show that these classes of hypergraphs are mutually exclusive. We conjecture a characterization of equitable hypergraphs and hence also of separable hypergraphs, and prove it for graphs, multipartite k -hypergraphs for all k , paving k -matroids and binary k -matroids for all k , and 3-matroids.
Variance Reduction for Inverse Trace Estimation via Random Spanning Forests

The trace $\tr(q(\ma{L} + q\ma{I})^{-1})$, where $\ma{L}$ is a symmetric diagonally dominant matrix, is the quantity of interest in some machine learning problems. However, its direct computation is impractical if the matrix size is large. State-of-the-art methods include Hutchinson's estimator combined with iterative solvers, as well as the estimator based on random spanning forests (a random process on graphs). In this work, we show two ways of improving the forest-based estimator via well-known variance reduction techniques, namely control variates and stratified sampling. Implementing these techniques is easy, and provides substantial variance reduction, yielding comparable or better performance relative to state-of-the-art algorithms.
Indexing Temporal Relations for Range-Duration Queries

Temporal information plays a crucial role in many database applications, however support for queries on such data is limited. We present an index structure, termed RD-index, to support range-duration queries over interval timestamped relations, which constrain both the range of the tuples’ positions on the timeline and their duration. RD-index is a grid structure in the two-dimensional space, representing the position on the timeline and the duration of timestamps, respectively. Instead of using a regular grid, we consider the data distribution for the construction of the grid in order to ensure that each grid cell contains approximately the same number of intervals. RD-index features provable bounds on the running time of all the operations, allow for a simple implementation, and supports very predictable query performance. We benchmark our solution on a variety of datasets and query workloads, investigating both the query rate and the behavior of the individual queries. The results show that RD-index performs better than the baselines on range-duration queries, for which it is explicitly designed. Furthermore, it outperforms state of the art indexes also on mixed workloads containing queries that constrain either only the duration or the range along with range-duration queries. Finally, the size of the RD-index is in all settings smaller than the competitors.
Reconstructing Ultrametric Trees from Noisy Experiments

The problem of reconstructing evolutionary trees or phylogenies is of great interest in computational biology. A popular model for this problem assumes that we are given the set of leaves (current species) of an unknown binary tree and the results of `experiments' on triples of leaves (a,b,c), which return the pair with the deepest least common ancestor. If the tree is assumed to be an ultrametric (i.e., all root-leaf paths have the same length), the experiment can be equivalently seen to return the closest pair of leaves. In this model, efficient algorithms are known for tree reconstruction. In reality, since the data on which these `experiments' are run is itself generated by the stochastic process of evolution, these experiments are noisy. In all reasonable models of evolution, if the branches leading to the leaves in a triple separate from each other at common ancestors that are very close to each other in the tree, the result of the experiment should be close to uniformly random. Motivated by this, we consider a model where the noise on any triple is just dependent on the three pairwise distances (referred to as distance based noise). Our results are the following: 1. Suppose the length of every edge in the unknown tree is at least $\tilde{O}(\frac{1}{\sqrt n})$ fraction of the length of a root-leaf path. Then, we give an efficient algorithm to reconstruct the topology of the tree for a broad family of distance-based noise models. Further, we show that if the edges are asymptotically shorter, then topology reconstruction is information-theoretically impossible. 2. Further, for a specific distance-based noise model--which we refer to as the homogeneous noise model--we show that the edge weights can also be approximately reconstructed under the same quantitative lower bound on the edge lengths.
Flatten the Curve: Efficiently Training Low-Curvature Neural Networks

The highly non-linear nature of deep neural networks causes them to be susceptible to adversarial examples and have unstable gradients which hinders inter-pretability. However, existing methods to solve these issues, such as adversarial training, are expensive and often sacriﬁce predictive accuracy. In this work, we consider curvature, which is a mathematical quantity which encodes the degree of non-linearity. Using this, we demonstrate low-curvature neural networks (LCNNs) that obtain drastically lower curvature than standard models while exhibiting similar predictive performance, which leads to improved robustness and stable gradients, with only a marginally increased training time. To achieve this, we minimize a data-independent upper bound on the curvature of a neural network, which decomposes overall curvature in terms of curvatures and slopes of its constituent layers. To efﬁciently minimize this bound, we introduce two novel architectural components: ﬁrst, a non-linearity called centered-softplus that is a stable variant of the softplus non-linearity, and second, a Lipschitz-constrained batch normalization layer. Our experiments show that LCNNs have lower curvature, more stable gradients and increased off-the-shelf adversarial robustness when compared to their standard high-curvature counterparts, all without affecting predictive performance. Our approach is easy to use and can be readily incorporated into existing neural network models.
Weak dipole moments of heavy fermions with flavor violation induced by Z' gauge bosons

A calculation of weak dipole moments of charged fermions of the Standard Model (SM) at the one-loop level, in the context of a general effective extended neutral current model with flavor changing $Z^\prime f_i \bar{f}_j$ vertices, is presented. We give numerical predictions for the anomalous weak magnetic dipole moment (AWMDM) $a^w_{fi}$, and the weak electric dipole moment (WEDM) $d^w_{f_i}$, for the $\tau$ lepton and $t$ quark. For several $Z^\prime$ gauge bosons considered, we find that, for the $\tau$ lepton, the best prediction for the real part of $a^w_{\tau}$ is of the order of $10^{-9}$, while the imaginary part is four orders of magnitude below. The highest value for the WEDM, $d^w_{\tau}$, corresponds to $10^{-26}$ $e$-cm, for its real part, and the imaginary part is three orders of magnitude below. On the other hand, we found for the top quark, that the best prediction for the real part of $a^w_t$ is of the order of $10^{-7}$ and its imaginary part is of the order of $10^{-11}$. We also found that $d^w_t$ is of the order of $10^{-26}$ $e$-cm for its real part, and its imaginary part can be as high as $10^{-31}$ $e$-cm.
An Intelligent Assistant for Converting City Requirements to Formal Specification

As more and more monitoring systems have been deployed to smart cities, there comes a higher demand for converting new human-specified requirements to machine-understandable formal specifications automatically. However, these human-specific requirements are often written in English and bring missing, inaccurate, or ambiguous information. In this paper, we present City Spec [1], an intelligent assistant system for requirement specification in smart cities. CitySpec not only helps overcome the language differences brought by English requirements and formal specifications, but also offers solutions to those missing, inaccurate, or ambiguous information. The goal of this paper is to demonstrate how CitySpec works. Specifically, we present three demos: (1) interactive completion of requirements in CitySpec; (2) human-in-the-loop correction while CitySepc encounters exceptions; (3) online learning in CitySpec.
QONNX: Representing Arbitrary-Precision Quantized Neural Networks

We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce support for low precision quantization in existing ONNX-based quantization formats by leveraging integer clipping, resulting in two new backward-compatible variants: the quantized operator format with clipping and quantize-clip-dequantize (QCDQ) format. We then introduce a novel higher-level ONNX format called quantized ONNX (QONNX) that introduces three new operators -- Quant, BipolarQuant, and Trunc -- in order to represent uniform quantization. By keeping the QONNX IR high-level and flexible, we enable targeting a wider variety of platforms. We also present utilities for working with QONNX, as well as examples of its usage in the FINN and hls4ml toolchains. Finally, we introduce the QONNX model zoo to share low-precision quantized neural networks.
Normalization, square roots, and the exponential and logarithmic maps in geometric algebras of less than 6D

Geometric algebras of dimension n<6$$ n<6 $$ are becoming increasingly popular for the modeling of 3D and 3 + 1D geometry. With this increased popularity comes the need for efficient algorithms for common operations such as normalization, square roots, and exponential and logarithmic maps. The current work presents a signature‐agnostic analysis of these common operations in all geometric algebras of dimension n<6$$ n<6 $$ and gives efficient numerical implementations in the most popular algebras ℝ4,ℝ3,1,ℝ3,0,1$$ {\mathbb{R}}_4,{\mathbb{R}}_{3,1},{\mathbb{R}}_{3,0,1} $$ , and ℝ4,1$$ {\mathbb{R}}_{4,1} $$ , in the hopes of lowering the threshold for adoption of geometric algebra solutions by code maintainers.
A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing

The knowledge of mixtures’ phase equilibria is crucial in nature and technical chemistry. Phase equilibria calculations of mixtures require activity coefficients. However, experimental data on activity coefficients are often limited due to the high cost of experiments. For an accurate and efficient prediction of activity coefficients, machine learning approaches have been recently developed. However, current machine learning approaches still extrapolate poorly for activity coefficients of unknown molecules. In this work, we introduce a SMILES-to-properties-transformer (SPT), a natural language processing network, to predict binary limiting activity coefficients from SMILES codes. To overcome the limitations of available experimental data, we initially train our network on a large dataset of synthetic data sampled from COSMO-RS (10 million data points) and then fine-tune the model on experimental data (20 870 data points). This training strategy enables the SPT to accurately predict limiting activity coefficients even for unknown molecules, cutting the mean prediction error in half compared to state-of-the-art models for activity coefficient predictions such as COSMO-RS and UNIFACDortmund, and improving on recent machine learning approaches.
Priori Error Estimate of Deep Mixed Residual Method for Elliptic PDEs

In this work, we derive a priori error estimate of the mixed residual method when solving some elliptic PDEs. Our work is the first theoretical study of this method. We prove that the neural network solutions will converge if we increase the training samples and network size without any constraint on the ratio of training samples to the network size. Besides, our results suggest that the mixed residual method can recover high order derivatives better than the deep Ritz method, which has also been verified by our numerical experiments.
Parameterized Completeness Results for Bayesian Inference

We present completeness results for inference in Bayesian networks with respect to two different parameterizations, namely the number of variables and the topological vertex separation number. For this we introduce the parameterized complexity classes $\mathsf{W[1]PP}$ and $\mathsf{XLPP}$, which relate to $\mathsf{W[1]}$ and $\mathsf{XNLP}$ respectively as $\mathsf{PP}$ does to $\mathsf{NP}$. The second parameter is intended as a natural translation of the notion of pathwidth to the case of directed acyclic graphs, and as such it is a stronger parameter than the more commonly considered treewidth. Based on a recent conjecture, the completeness results for this parameter suggest that deterministic algorithms for inference require exponential space in terms of pathwidth and by extension treewidth. These results are intended to contribute towards a more precise understanding of the parameterized complexity of Bayesian inference and thus of its required computational resources in terms of both time and space.
Most, And Least, Compact Spanning Trees of a Graph

We introduce the concept of Most, and Least, Compact Spanning Trees - denoted respectively by T ∗ ( G ) and T # ( G ) - of a simple, connected, undirected and unweighted graph G ( V, E, W ) . For a spanning tree T ( G ) ∈ T ( G ) to be considered T ∗ ( G ) , where T ( G ) represents the set of all the spanning trees of the graph G , it must have the least average inter-vertex pair (shortest path) distances from amongst the members of the set T ( G ) . Similarly, for it to be considered T # ( G ) , it must have the highest average inter-vertex pair (shortest path) distances . In this work, we present an iteratively greedy rank-and-regress method that produces at least one T ∗ ( G ) or T # ( G ) by eliminating one extremal edge per iteration. The rank function for performing the elimination is based on the elements of the matrix of relative forest accessibilities of a graph and the related forest distance [22]. We provide empirical evidence in support of our methodology using some standard graph families: complete graphs, the Erd ˝ os-Renyi random graphs and the Barabási-Albert scale-free graphs; and discuss computational complexity of the underlying methods which incur polynomial time costs.