Miracle-dz/UniFAR-FTUs huggingface dataset:
Transient Performance Analysis of the $\ell _1$-RLS

The recursive least-squares algorithm with <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-norm regularization (<inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems. Nevertheless few works have studied its stochastic behavior, in particular its transient performance. In this letter, we derive analytical models of the transient behavior of the <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-RLS in the mean and mean-square error sense. Simulation results illustrate the accuracy of these models.
A Double-Linked Blockchain Approach Based on Proof-of-Refundable-Tax Consensus Algorithm

In this paper we propose a double-linked blockchain data structure that greatly improves blockchain performance and guarantees single chain with no forks. Additionally, with the proposed proof-of-refundable-tax (PoRT) consensus algorithm, our approach can construct highly reliable, efficient, fair and stable blockchain operations. The PoRT algorithm adopts a verifiable random function instead of mining to select future block maintainers with the probability proportional to each participant's personal refundable tax. The individual refundable tax serves as an index of the activeness of participation and hence PoRT can effectively prevent Sybil attacks. Also, with the block-completion reward deducted from each maintainer's refundable tax, our blockchain system maintains a stable wealth distribution and avoids the"rich become richer"problem. We have implemented the approach and tested with very promising results.
Sparse Fuzzy Attention for Structured Sentiment Analysis

Attention scorers have achieved success in parsing tasks like semantic and syntactic dependency parsing. However, in tasks modeled into parsing, like structured sentiment analysis,"dependency edges"are very sparse which hinders parser performance. Thus we propose a sparse and fuzzy attention scorer with pooling layers which improves parser performance and sets the new state-of-the-art on structured sentiment analysis. We further explore the parsing modeling on structured sentiment analysis with second-order parsing and introduce a novel sparse second-order edge building procedure that leads to significant improvement in parsing performance.
Towards the impact of GMC collisions on the star formation rate

Collisions between giant molecular clouds (GMCs) are one of the pathways for massive star formation, due to the high densities created. However the enhancement of the star formation rate (SFR) is not well constrained. In this study we perform a parameter study of cloud-cloud collisions, and investigate how the resulting SFR depends on the details of set-up. Our parameter study explores variations in: collision speed; magnetic field inclination (with respect to the collisional axis); and resolution, as defined by the number of cells per Jeans length. In all our collision simulations we find a factor of 2-3 increase in the SFR compared to our no collision simulation, with star formation beginning sooner with a) high collisional velocities, b) parallel orientation between the magnetic field and collision axis, c) and lower resolution. The mean virial parameter of high density (and thus possible star-forming) gas increases with collisional velocity, but has little variation with magnetic field inclination. The alignment of the velocity and magnetic field remains uniform in low density environments but becomes more perpendicular with increasing density, indicating the compression of the magnetic field by collapsing gas. Comparing the trends in the SFR with other GMC collision studies, we find good agreement with studies that account for the gravitational boundedness of the gas in their star formation algorithm, but not with those that simply form stars above a prescribed density threshold. This suggests that the latter approach should be used with caution when modelling star formation on resolved cloud scales.
Extracellular Vesicles as Novel Diagnostic and Prognostic Biomarkers for Parkinson’s Disease

The elderly population will significantly increase in the next decade and, with it, the proportion of people affected by age-related diseases. Among them, one of the most invalidating is Parkinson’s disease (PD), characterized by motor- and non-motor dysfunctions which strongly impair the quality of life of affected individuals. PD is characterized by the progressive degeneration of dopaminergic neurons, with consequent dopamine depletion, and the accumulation of misfolded α-synuclein aggregates. Although 150 years have passed since PD first description, no effective therapies are currently available, but only palliative treatments. Importantly, PD is often diagnosed when the neuronal loss is elevated, making difficult any therapeutic intervention. In this context, two key challenges remain unanswered: (i) the early diagnosis to avoid the insurgence of irreversible symptoms; and (ii) the reliable monitoring of therapy efficacy. Research strives to identify novel biomarkers for PD diagnosis, prognosis, and therapeutic follow-up. One of the most promising sources of biomarkers is represented by extracellular vesicles (EVs), a heterogeneous population of nanoparticles, released by all cells in the microenvironment. Brain-derived EVs are able to cross the blood-brain barrier, protecting their payload from enzymatic degradation, and are easily recovered from biofluids. Interestingly, EV content is strongly influenced by the specific pathophysiological status of the donor cell. In this manuscript, the role of EVs as source of novel PD biomarkers is discussed, providing all recent findings concerning relevant proteins and miRNAs carried by PD patient-derived EVs, from several biological specimens. Moreover, the contribution of mitochondria-derived EVs will be dissected. Finally, the promising possibility to use EVs as source of markers to monitor PD therapy efficacy will be also examined. In the future, larger cohort studies will help to validate these EV-associated candidates, that might be effectively used as non-invasive and robust source of biomarkers for PD.
An iterative approach to improving solution quality for AC optimal power flow problems

The existence of multiple solutions to AC optimal power flow (ACOPF) problems has been noted for decades. Existing solvers are generally successful in finding local solutions, which satisfy first and second order optimality conditions, but may not be globally optimal. In this paper, we propose a simple iterative approach to improve the quality of solutions to ACOPF problems. First, we call an existing solver for the ACOPF problem. From the solution and the associated dual variables, we form a partial Lagrangian. Then we optimize this partial Lagrangian and use its solution as a warm start to call the solver again for the ACOPF problem. By repeating this process, we can iteratively improve the solution quality, moving from local solutions to global ones. We show the effectiveness of our algorithm on standard IEEE networks. The simulation results show that our algorithm can escape from local solutions to achieve global optimums within a few iterations.
Antimicrobial Activity against Oral Pathogens Confirms the Use of Musa paradisiaca Fruit Stalk in Ethnodentistry

Background Ethnodentistry is the use of indigenous oral cleansing agents such as plant parts by local folks not only to maintain oral hygiene but also to treat oral infections. Mostly, ethnodentistry is inspired by traditions and belief systems of local communities. Musa paradisiaca is extensively cultivated and used in many cultures for its nutritional and medicinal values. In Ghana, the fruit stalk of Musa paradisiaca is used as an oral cleansing agent to maintain oral hygiene; yet this folk claim remains to be ascertained scientifically. Objective The study assessed the antibacterial and antifungal effects of three extract fractions (aqueous, ethanol, and ethyl acetate fractions) of Musa paradisiaca fruit stalk against Lactobacillus acidophilus, Aggregatibacter actinomycetemcomitans, and Candida albicans, common oral pathogens implicated in dental caries and periodontitis. Materials and Methods Aqueous, ethanol, and ethyl acetate fractions of Musa paradisiaca fruit stalk were prepared by cold maceration and qualitatively screened for their phytochemical composition. Antimicrobial effects of the three extract fractions were assessed by using serial broth dilutions at increasing concentrations (62.5, 125, 250, 500, and 1000 µg/ml) and compared to standard antimicrobial agents (erythromycin, doxycycline, and fluconazole). Subsequently, the absorbances of the microbial suspensions treated with increasing concentrations of the extract fractions were measured at 450 nm, and the cell densities were determined. Results Except for the aqueous extract, which was less effective in decreasing microbial growth, the ethyl acetate and the ethanol extract fractions demonstrated antimicrobial efficacies comparable to those of the standard drugs. All three extract fractions demonstrated concentration-dependent growth inhibitory effects on the tested oral pathogens although not as effective as the standard drugs used. Conclusion Musa paradisiaca fruit stalk has demonstrated antimicrobial effects against Lactobacillus acidophilus, Aggregatibacter actinomycetemcomitans, and Candida albicans, common oral pathogens implicated in dental caries and periodontitis, and this finding confirms in part folk use of Musa paradisiaca fruit stalk as a traditional dental care agent. Thus, the fruit stalk of Musa paradisiaca could be explored for use as a cheap and readily available dental care agent for people entrapped in the poverty line.
Talking Space: inference from spatial linguistic meanings

This paper concerns the intersection of natural language and the physical space around us in which we live, that we observe and/or imagine things within. Many important features of language have spatial connotations, for example, many prepositions (like in, next to, after, on, etc.) are fundamentally spatial. Space is also a key factor of the meanings of many words/phrases/sentences/text, and space is a, if not the key, context for referencing (e.g. pointing) and embodiment. We propose a mechanism for how space and linguistic structure can be made to interact in a matching compositional fashion. Examples include Cartesian space, subway stations, chesspieces on a chess-board, and Penrose’s staircase. The starting point for our construction is the DisCoCat model of compositional natural language meaning, which we relax to accommodate physical space. We address the issue of having multiple agents/objects in a space, including the case that each agent has different capabilities with respect to that space, e.g., the specific moves each chesspiece can make, or the different velocities one may be able to reach. Once our model is in place, we show how inferences drawing from the structure of physical space can be made. We also how how linguistic model of space can interact with other such models related to our senses and/or embodiment, such as the conceptual spaces of colour, taste and smell, resulting in a rich compositional model of meaning that is close to human experience and embodiment in the world.
On the realization of subgroups of $PGL(2,F)$, and their automorphism groups, as Galois groups over function fields

Let F be any field. We give a short and elementary proof that any finite subgroup G of PGL(2, F ) occurs as a Galois group over the function field F (x). We also develop a theory of descent to subfields of F . This enables us to realize the automorphism groups of finite subgroups of PGL(2, F ) as Galois groups.
Polynomial decompositions with invariance and positivity inspired by tensors

We present a framework to decompose real multivariate polynomials while preserving invariance and positivity. This framework has been recently introduced for tensor decompositions, in particular for quantum many-body systems. Here we transfer results about decomposition structures, invariance under permutations of variables, positivity, rank inequalities and separations, approximations, and undecidability to real polynomials. Specifically, we define invariant decompositions of polynomials and characterize which polynomials admit such decompositions. We then include positivity: We define invariant separable and sum-of-squares decompositions, and characterize the polynomials similarly. We provide inequalities and separations between the ranks of the decompositions, and show that the separations are not robust with respect to approximations. For cyclically invariant decompositions, we show that it is undecidable whether the polynomial is nonnegative or sum-of-squares for all system sizes. Our work sheds new light on polynomials by putting them on an equal footing with tensors, and opens the door to extending this framework to other tensor product structures.
Detecting Layout Templates in Complex Multiregion Files

Spreadsheets are among the most commonly used file formats for data management, distribution, and analysis. Their widespread employment makes it easy to gather large collections of data, but their flexible canvas-based structure makes automated analysis difficult without heavy preparation. One of the common problems that practitioners face is the presence of multiple, independent regions in a single spreadsheet, possibly separated by repeated empty cells. We define such files as "multiregion" files. In collections of various spreadsheets, we can observe that some share the same layout. We present the Mondrian approach to automatically identify layout templates across multiple files and systematically extract the corresponding regions. Our approach is composed of three phases: first, each file is rendered as an image and inspected for elements that could form regions; then, using a clustering algorithm, the identified elements are grouped to form regions; finally, every file layout is represented as a graph and compared with others to find layout templates. We compare our method to state-of-the-art table recognition algorithms on two corpora of real-world enterprise spreadsheets. Our approach shows the best performances in detecting reliable region boundaries within each file and can correctly identify recurring layouts across files.
An Accurate Comprehensive Approach to Substructure: I. Accreted Subhaloes

This is the first of a series of three Papers devoted to the study of halo substructure in hierarchical cosmologies by means of the CUSP formalism. In the present Paper we derive the properties of subhaloes and diffuse dark matter (dDM) accreted onto haloes and their progenitors. Specifically, we relate the dDM present at any time in the inter-halo medium of the real Universe or a cosmological simulation with the corresponding free-streaming mass or the halo resolution mass, respectively, and establish the link between subhaloes and their seeds in the initial density field. By monitoring the collapse and virialisation of haloes, we derive from first principles and with no single free parameter the abundance and radial distribution of dDM and subhaloes accreted onto them. Our predictions are in excellent agreement with the results of simulations, but for the predicted fraction of accreted dDM, which is larger than reported in previous works as they only count the dDM accreted onto the final halo, not onto its progenitors. The derivation pursued here clarifies the origin of some key features of substructure. Overall, our results demonstrate that CUSP is a powerful tool for understanding halo substructure and extending the results of simulations to haloes with arbitrary masses, redshifts and formation times in any hierarchical cosmology endowed with random Gaussian density perturbations.
Does deep brain stimulation improve Parkinson’s disease-related lower urinary tract symptoms and voiding dysfunction?

Parkinson’s disease (PD), caused by degeneration of dopaminergic neurons, leads to motor and autonomic symptoms. A large proportion of PD patients experience lower urinary tract symptoms (LUTS) and voiding dysfunction, associated with poor quality of life. Deep brain stimulation (DBS) is an adjunctive therapy used in combination with medication for Parkinsonian motor symptoms and is currently being explored to treat PD-related LUTS. This review discusses the current literature on the effects of DBS on lower urinary tract function and whether this modality can be used to modulate PD-related LUTS and voiding dysfunction.
IGNNITION: Bridging the Gap between Graph Neural Networks and Networking Systems

Recent years have seen the vast potential of graph neural networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, recommender systems). In particular, GNNs are becoming increasingly popular in the field of networking, as graphs are intrinsically present at many levels (e.g., topology, routing). The main novelty of GNNs is their ability to generalize to other networks unseen during training, which is an essential feature for developing practical machine learning (ML) solutions for networking. However, implementing a functional GNN prototype is currently a cumbersome task that requires strong skills in neural network programming. This poses an important barrier to network engineers that often do not have the necessary ML expertise. In this article, we present IGNNITION, a novel open source framework that enables fast prototyping of GNNs for networking systems. IGNNITION is based on an intuitive high-level abstraction that hides the complexity behind GNNs, while still offering great flexibility to build custom GNN architectures. To showcase the versatility and performance of this framework, we implement two state-of-the-art GNN models applied to different networking use cases. Our results show that the GNN models produced by IGNNITION are equivalent in terms of accuracy and performance to their native implementations in TensorFlow.
Reactive and Safe Road User Simulations using Neural Barrier Certificates

Reactive and safe agent modellings are important for nowadays traffic simulator designs and safe planning applications. In this work, we proposed a reactive agent model which can ensure safety without comprising the original purposes, by learning only high-level decisions from expert data and a low level decentralized controller guided by the jointly learned decentralized barrier certificates. Empirical results show that our learned road user simulation models can achieve a significant improvement in safety comparing to state-of-the-art imitation learning and pure control-based methods, while being similar to human agents by having smaller error to the expert data. Moreover, our learned reactive agents are shown to generalize better to unseen traffic conditions, and react better to other road users and therefore can help understand challenging planning problems pragmatically.
Seroprevalence of Zika virus in pregnant women from central Thailand

Zika virus (ZKV) infection in a pregnant woman, especially during the first trimester, often results in congenital anomalies. However, the pathogenic mechanism is unknown and one-third of ZKV infected pregnancies are asymptomatic. Neutralizing antibodies against ZKV has been reported in 70% of Thai adults, but the prevalence among pregnant women is unknown. Currently, vaccines and specific treatments for ZKV are under development. A better understanding of the immune status of pregnant women will increase the success of effective prevention guidelines. The prevalence of ZKV infection in pregnant women in antenatal care clinics was investigated during the rainy season from May to October 2019 at Siriraj Hospital, Bangkok, Thailand. We recruited 650 pregnant women (39.42% first, 52.26% second and 7.36% third trimester) and found that 30.77% had ZKV-specific IgG, and 39.81% had neutralizing antibodies (nAb) against ZKV (titer ≥10). Specific and neutralizing antibody levels varied by maternal age, trimester, and month. We further characterized the cross-reaction between ZKV and the four Dengue virus (DENV) serotypes by focused reduction neutralization test (FRNT) and found that cross-reactions were common. In conclusion, about 60% of pregnant women who living in central Thailand may be at risk of ZKV infection due to the absence of neutralizing antibodies against ZKV. The functions of cross-reactive antibodies between related viral genotypes require further study. These findings have implications for health care monitoring in pregnant women including determining the risk of ZKV infection, assisting the development of a flavivirus vaccine, and informing the development of preventative health policies.
Improving Older People’s Lives Through Digital Technology and Practices

The Aging and Technology special issue showcases twelve papers spanning a range and diversity of international scholarly research within the field of ageing and technology. The collection of papers demonstrates the positive impact technology can have on the lives of older people including improving cognitive performance, physical and mental health and people’s daily activities and practices. There are still barriers to use, including psychological issues of motivation, attitudes, privacy and trust and social issues involving learning to use the technology. In conclusion, to help overcome these barriers, it is recommended that research and development of technology involves older people as co-developers working with stakeholders from different disciplines and backgrounds.
Invasive and non-invasive assessment of ischaemia in chronic coronary syndromes: translating pathophysiology to clinical practice

Abstract Intracoronary physiology testing has emerged as a valuable diagnostic approach in the management of patients with chronic coronary syndrome, circumventing limitations like inferring coronary function from anatomical assessment and low spatial resolution associated with angiography or non-invasive tests. The value of hyperaemic translesional pressure ratios to estimate the functional relevance of coronary stenoses is supported by a wealth of prognostic data. The continuing drive to further simplify this approach led to the development of non-hyperaemic pressure-based indices. Recent attention has focussed on estimating physiology without even measuring coronary pressure. However, the reduction in procedural time and ease of accessibility afforded by these simplifications needs to be counterbalanced against the increasing burden of physiological assumptions, which may impact on the ability to reliably identify an ischaemic substrate, the ultimate goal during catheter laboratory assessment. In that regard, measurement of both coronary pressure and flow enables comprehensive physiological evaluation of both epicardial and microcirculatory components of the vasculature, although widespread adoption has been hampered by perceived technical complexity and, in general, an underappreciation of the role of the microvasculature. In parallel, entirely non-invasive tools have matured, with the utilization of various techniques including computational fluid dynamic and quantitative perfusion analysis. This review article appraises the strengths and limitations for each test in investigating myocardial ischaemia and discusses a comprehensive algorithm that could be used to obtain a diagnosis in all patients with angina scheduled for coronary angiography, including those who are not found to have obstructive epicardial coronary disease.