Miracle-dz/UniFAR-FTUs huggingface dataset:
Gaussian process models for process monitoring and control

One problem of special interest both in industry and the engineering community is that of using the enormous amounts of data routinely generated and recorded in efficient process monitoring and control strategies. In statistical terms this is related to identifying those variables which exhibit unwanted or unusual process variability so that remedial action can be taken. To this end, a common approach in the literature is to reduce the problem dimensionality by using latent variable models. Customarily, the latent variables are a function of all of the original variables and monitoring is carried out in the reduced space. Within this context, this thesis explores the development of models in which the latent factors are a function of a subset, only, of the original observations. By doing that, the advantages of monitoring in a reduced subspace are retained but there there are also additional gains in model interpretability. The idea arises from the sparse representation of the mapping matrix between latent and original variables in a linear factor analysis (FA) model. An extension of principal component analysis (PCA) to monitor nonlinear systems is proposed by using a a Gaussian Process Latent Variable model [Lawrence, 2005], GPLVM, as a starting point. Its application in a process control problem is also introduced. Using a Gaussian process, GP , as the backbone, we define a Gaussian Process Functional Factor Analysis model which maps subsets of the latent variables to the observed data-space; a study of the model asymptotic properties is given. Several parameter inference methods as well as a model selection procedure via penalty functions are also proposed. There are several scientific disciplines involved in the problem at hand. Chemical engineers refer to it as a sub-field of Process Control known asMultivariate Statistical Process Control. It is also an area of tremendous success in process Chemometrics where it has grown very rapidly over the last two decades. In Statistics, it touches the topics of latent variable models and variable selection methods. And within the Machine Learning community is classified as an Unsupervised Learning problem.
Z2 gauge theory of electron fractionalization in the t,t' - J model with uniaxial anisotropy

We consider a strongly correlated spin model with a positive uniaxial anisotropy. We show that in 2+1 dimensions this model is equivalent to spinon and holon excitations coupled to a Z2 Ising gauge field. The Z2 gauge field represents the vortex monopole excitations of the X–Y model. As a function of the hole concentration the X–Y model has two phases: a spin wave phase and a free vortex monopole phase. In the spin wave phase the spinons and the holons are weakly interacting, giving rise to a spin–charge separated phase which at zero temperature is superconducting. When the hole concentration increases the Z2 excitations (the monopole currents) become free, giving rise to holon–spinon binding. The destruction of superconductivity at zero temperature coincides with the appearance of the free monopole currents.
Leray residue for singular varieties

1. Residue form. We begin with recalling the construction of the Leray residue form [Le]. Let K be a smooth hypersurface of a complex manifold M . Suppose that K is locally given by an equation f = 0 where f is holomorphic and df does not vanish along K. Let ω ∈ Ω(M \K) be a complex valued C∞-form with the first order pole on K, i.e. f ω extends to a smooth form on M . If ω is closed then it can be written (locally) in the form
Terahertz sources based on intracavity parametric frequency down-conversion using quasi-phase-matched gallium arsenide

There exist few sources of tunable electromagnetic radiation at frequencies between 0.1–10 THz. Of the sources based on nonlinear optical processes which do exist, the conversion efficiencies are on the order of 10−6 for systems which fit on an optical table. With the advent of quasi-phase-matched (QPM) nonlinear-optical frequency conversion processes, coherent sources of frequency-tunable radiation in the visible and infrared were developed. QPM can be extended to generating tunable THz-frequency radiation via parametric frequency down-conversion where energy is transferred from radiation in the near-infrared (λ ∼ 2 μm) to the far-infrared (λ ∼ 300 μm). GaAs is an excellent nonlinear-optical material for QPM THz generation due to its large nonlinear-optical coefficient, small absorption coefficient at THz frequencies (∼1 order of magnitude smaller than other nonlinear-optical materials), and small mismatch between the optical and THz refractive indices. Three types of micro-structured GaAs have been used to generate THz radiation by parametric frequency down-conversion: (i) orientation-patterned GaAs, OP-GaAs (ii) optically contacted GaAs wafers, OCGaAs, and (iii) diffusion-bonded GaAs plates, DB-GaAs. THz frequencies between 0.5–3.5 THz were generated using the various GaAs samples. THz average powers as large as 1 mW generated from a pump power of 8.5 W, corresponding to an optical-to-THz power conversion efficiency of 1.2 × 10−4, were observed by placing the GaAs inside a doubly resonant synchronously pumped optical parametric oscillator. The quantum conversion efficiencies were as large as 1.2%. The parametric conversion efficiency for THz generation is inherently small since the ratio of the THz and optical frequencies is small. Difference-frequency generation (DFG) between the intracavity signal and idler waves generated the THz radiation. The
The Italian epistemic marker mi sa [to me it knows] compared to so [I know], non so [I don’t know], non so se [I don’t know whether], credo [I believe], penso [I think]

The two studies presented in this paper concern the Italian epistemic marker mi sa [lit. to me it knows], which seems to have no equivalent in other European languages and has received very little attention in the literature. No analysis of the occurrences of mi sa in contemporary spoken corpora can be found (first gap) as well as no investigation on the epistemic relationship between mi sa and (1) the other modal expressions that use the verb sapere [to know] in the first person singular of the simple present, i.e., so [I know], non so [I do not know], non so se [I do not know whether] as well as (2) its supposed synonyms credo [I believe] and penso [I think] (second gap). The two studies are closely intertwined, the first being an exploratory, qualitative pilot study for the second. Study 1 aims to fill the first gap through the analysis of the contemporary Italian spoken corpus KIParla. The quantitative and qualitative analyses revealed five types of occurrences (theoretically reducible to two main ones), the most numerous of which are ‘mi sa che + proposition’. Study 2 aims to fill the second gap through a questionnaire administered online. The quantitative and statistical results showed the epistemic relationships between the six markers: for the majority of the participants, in the epistemic continuum that goes from unknowledge to uncertainty and then to knowledge, (1) non so refers to unknowledge; non so se, mi sa, credo and penso refer to uncertainty; so refers to knowledge; (2) mi sa, credo, penso confirm to be synonyms; (3) non so se is evaluated as much more uncertain than mi sa, credo, penso. These four epistemic markers seem to occupy a different position along the uncertainty continuum ranging between two poles: doubt (high uncertainty) and belief (low uncertainty).
The Minimum Squared Distance Estimator and the Minimum Density Power Divergence Estimator

Abstract Basu et al. (1998) proposed the minimum divergence estimating method which is free from using the painfulkernel density estimator. Their proposed class of density power divergences is indexed by a single parameter which controls the trade-o between robustness and eciency. In this article, (1) we introduce a new large class ofthe minimum squared distance which includes from the minimum Hellinger distance to the minimum L 2 distance.We also show that under certain conditions both the minimum density power divergence estimator(MDPDE) andthe minimum squared distance estimator(MSDE) are asymptotically equivalent and (2) in ﬁnite samples theMDPDE performs better than the MSDE in general but there are some cases where the MSDE performs betterthan the MDPDE when estimating a location parameter or a proportion of mixed distributions.Keywords: Asymptotic equivalence, density power divergence, Hellinger distance, L 2 distance. 1. Introduction Basu et al. (1998) proposed a minimum divergence estimating method which used new density-baseddivergences. Unlike existing methods of this type such as minimum Hellinger distance estimation(Beran, 1977; Simpson, 1987; Tamura and Boos, 1986), Basu
miR-1285-3p targets TPI1 to regulate the glycolysis metabolism signaling pathway of Tibetan sheep Sertoli cells

Glycolysis in Sertoli cells (SCs) can provide energy substrates for the development of spermatogenic cells. Triose phosphate isomerase 1 (TPI1) is one of the key catalytic enzymes involved in glycolysis. However, the biological function of TPI1 in SCs and its role in glycolytic metabolic pathways are poorly understood. On the basis of a previous research, we isolated primary SCs from Tibetan sheep, and overexpressed TPI1 gene to determine its effect on the proliferation, glycolysis, and apoptosis of SCs. Secondly, we investigated the relationship between TPI1 and miR-1285-3p, and whether miR-1285-3p regulates the proliferation and apoptosis of SCs, and participates in glycolysis by targeting TPI1. Results showed that overexpression of TPI1 increased the proliferation rate and decreased apoptosis of SCs. In addition, overexpression of TPI1 altered glycolysis and metabolism signaling pathways and significantly increased amount of the final product lactic acid. Further analysis showed that miR-1285-3p inhibited TPI1 by directly targeting its 3’untranslated region. Overexpression of miR-1285-3p suppressed the proliferation of SCs, and this effect was partially reversed by restoration of TPI1 expression. In summary, this study shows that the miR-1285-3p/TPI1 axis regulates glycolysis in SCs. These findings add to our understanding on the regulation of spermatogenesis in sheep and other mammals.
Pan-cancer gene expression analysis of tissue microarray using EdgeSeq oncology biomarker panel and a cross-comparison with HER2 and HER3 immunohistochemical analysis

Molecular and protein biomarker profiling are key to oncology drug development. Antibody-drug conjugates (ADCs) directly deliver chemotherapeutic agents into tumor cells based on unique cancer cell biomarkers. A pan-cancer tissue microarray (TMA) data set and gene panel were validated and gene signature analyses were conducted on normal and cancer tissues to refine selection of ADC targets. Correlation of mRNA and protein levels, and human epidermal growth factor receptor (HER) expression patterns were assessed. An EdgeSeq biomarker panel (2862 genes) was used across 8531 samples (23 solid cancer types/subtypes; 16 normal tissues) with an established TMA data set, and immune cell and cell cycle gene signatures were analyzed. Discriminating gene expression signatures were defined based on pathological classification of cancer subtypes. Correlative analyses of HER2 and HER3 mRNA (EdgeSeq) and protein expression (immunohistochemistry [IHC]) were performed and compared with publicly available data (The Cancer Genome Atlas [TCGA]; Cancer Cell Line Encyclopedia [CCLE]). Gene expression patterns among cancer types in the TMA (EdgeSeq) and TCGA (RNA-seq) were similar. EdgeSeq gene signature analyses aligned with the majority of pathological cancer types/subtypes and identified cancer-specific gene expression patterns. TMA IHC H-scores for HER3 varied across cancer types/subtypes. In a few cancer types, HER3 mRNA and protein expression did not align, including lower liver hepatocellular carcinoma IHC H-score, compared with mRNA. Although all TNBC and ovarian cancer subtypes expressed mRNA, some had lower protein expression. This was seen in TMA and TCGA data sets, but not in CCLE. The EdgeSeq TMA data set can expand upon current biomarker data by including cancers not currently in TCGA. The primary analysis of EdgeSeq and IHC comparison suggested a unique protein-level regulation of HER3 in some tumor subtypes and highlights the importance of investigating protein levels of ADC targets in both tumor and normal tissues.
Endophytic Metarhizium robertsii suppresses the phytopathogen, Cochliobolus heterostrophus and modulates maize defenses

Fungi in the genus Metarhizium (Hypocreales: Clavicipitaceae) are insect-pathogens and endophytes that can benefit their host plant through growth promotion and protection against stresses. Cochliobolus heterostrophus (Drechsler) Drechsler (Pleosporales: Pleosporaceae) is an economically-significant phytopathogenic fungus that causes Southern Corn Leaf Blight (SCLB) in maize. We conducted greenhouse and lab-based experiments to determine the effects of endophytic M. robertsii J.F. Bisch. , Rehner & Humber on growth and defense in maize (Zea mays L.) infected with C. heterostrophus. We inoculated maize seeds with spores of M. robertsii and, at the 3 to 4-leaf stage, the youngest true leaf of M. robertsii-treated and untreated control plants with spores of C. heterostrophus. After 96 h, we measured maize height, above-ground biomass, endophytic colonization by M. robertsii, severity of SCLB, and expression of plant defense genes and phytohormone content. We recovered M. robertsii from 74% of plants grown from treated seed. The severity of SCLB in M. robertsii-treated maize plants was lower than in plants inoculated only with C. heterostrophus. M. robertsii-treated maize inoculated or not inoculated with C. heterostrophus showed greater height and above-ground biomass compared with untreated control plants. Height and above-ground biomass of maize co-inoculated with M. robertsii and C. heterostrophus were not different from M. robertsii-treated maize. M. robertsii modulated the expression of defense genes and the phytohormone content in maize inoculated with C. heterostrophus compared with plants not inoculated with C. heterostrophus and control plants. These results suggest that endophytic M. robertsii can promote maize growth and reduce development of SCLB, possibly by induced systemic resistance mediated by modulation of phytohormones and expression of defense and growth-related genes in maize.
The effects of aging and musicianship on the use of auditory streaming cues

Auditory stream segregation, or separating sounds into their respective sources and tracking them over time, is a fundamental auditory ability. Previous research has separately explored the impacts of aging and musicianship on the ability to separate and follow auditory streams. The current study evaluated the simultaneous effects of age and musicianship on auditory streaming induced by three physical features: intensity, spectral envelope and temporal envelope. In the first study, older and younger musicians and non-musicians with normal hearing identified deviants in a four-note melody interleaved with distractors that were more or less similar to the melody in terms of intensity, spectral envelope and temporal envelope. In the second study, older and younger musicians and non-musicians participated in a dissimilarity rating paradigm with pairs of melodies that differed along the same three features. Results suggested that auditory streaming skills are maintained in older adults but that older adults rely on intensity more than younger adults while musicianship is associated with increased sensitivity to spectral and temporal envelope, acoustic features that are typically less effective for stream segregation, particularly in older adults.
Mathematical Morphology: from theory to applications

Mathematical Morphology allows for the analysis and processing of geometrical structures using techniques based on the fields of set theory, lattice theory, topology, and random functions. It is the basis of morphological image processing, and finds applications in fields including digital image processing (DSP), as well as areas for graphs, surface meshes, solids, and other spatial structures. This book presents an up-to-date treatment of mathematical morphology, based on the three pillars that made it an important field of theoretical work and practical application: a solid theoretical foundation, a large body of applications and an efficient implementation. The book is divided into five parts and includes 20 chapters. The five parts are structured as follows: Part I sets out the fundamental aspects of the discipline, starting with a general introduction, followed by two more theory-focused chapters, one addressing its mathematical structure and including an updated formalism, which is the result of several decades of work. Part II extends this formalism to some non-deterministic aspects of the theory, in particular detailing links with other disciplines such as stereology, geostatistics and fuzzy logic. Part III addresses the theory of morphological filtering and segmentation, featuring modern connected approaches, from both theoretical and practical aspects. Part IV features practical aspects of mathematical morphology, in particular how to deal with color and multivariate data, links to discrete geometry and topology, and some algorithmic aspects; without which applications would be impossible. Part V showcases all the previously noted fields of work through a sample of interesting, representative and varied applications.
A Numerical Method for Incompressible Flow with Heat Transfer

A numerical method for the convective heat transfer problem is developed for low speed flow at mild temperatures. A simplified energy equation is added to the incompressible Navier-Stokes formulation by using Boussinesq approximation to account for the buoyancy force. A pseudocompressibility method is used to solve the resulting set of equations for steady-state solutions in conjunction with an approximate factorization scheme. A Neumann-type pressure boundary condition is devised to account for the interaction between pressure and temperature terms, especially near a heated or cooled solid boundary. It is shown that the present method is capable of predicting the temperature field in an incompressible flow.
Natural language processing and modeling of clinical disease trajectories across brain disorders

Brain disorders, including neurodegenerative diseases, and mental illnesses, are often difficult to diagnose and study due to clinical and pathological heterogeneity, overlap in clinical manifestations between disorders, and frequent comorbidities, tampering drug development and fundamental research. Hence, there is a clear need for data-driven approaches to disentangle these complex disorders. Here, we established a computational pipeline to process clinical summaries from donors with a wide range of brain disorders that were neuro-pathologically diagnosed by the Netherlands Brain Bank. First, we identified and defined 90 cross-disorder signs and symptoms within cognitive, motor, sensory, psychiatric, and general domains. Second, we trained and optimized natural language processing (NLP) models to identify these signs and symptoms in individual sentences of the extensive clinical summaries from donors of the NBB, resulting in temporal disease trajectories. Third, we studied the temporal manifestation and survival profiles across rare and complex dementias, alpha-synucleinopathies, frontotemporal dementia subtypes, and mental illnesses. Lastly, we trained a recurrent neural network to predict the Neuropathological Diagnosis. Taken together, this integrated approach resulted in a highly unique resource that can facilitate research into cross-disorder symptomatology.
Low-coverage genome sequencing for the detection of clinically relevant copy-number and mtDNA variants

Background: Compared to exome sequencing, genome sequencing is widely appreciated for its superior ability to detect a wide range of genetic variations including copy-number variants (CNVs) and mitochondrial (mtDNA) variants. We assessed whether low-coverage genome sequencing, a considerably cheaper approach, would detect clinically relevant CNVs and mtDNA variants and would thus be a cost-efficient supplement to exome sequencing in rare disease diagnostics. Methods: To assess the level of sequencing depth needed for variant detection, first, 30x mean coverage genome sequencing data were subsampled to 0.5x, 1x, 2x, and 4x coverage files in silico followed by CNV and mtDNA detection. Based on the analysis, 2x short-read sequencing was selected to be performed in 16 patients with putatively pathogenic CNVs or mtDNA variants to assess the empirical sensitivity. Results: For CNV calling, 2x coverage was sufficient to detect all heterozygous CNVs greater than 10kb in size from in silico subsampled data. In experimental data, the results were similar, although a 16kb heterozygous deletion was once not detected. Regarding mtDNA variants, 2x coverage sufficed for variant confident variant calling and heteroplasmy assessment for all samples. Conclusions: Low-coverage genome sequencing may be used to complement exome sequencing for simultaneous mtDNA variant and CNV detection.
Mapping the association of polygenic risk scores with autism and ADHD traits in a single city region

The prevalence of autistic traits and Attention Deficit Hyperactivity Disorder (ADHD) traits is known to vary by location. The genetic and environmental aetiology of these traits may also vary spatially, with geographical environments amplifying or masking genetic influences. To investigate this, we constructed polygenic risk scores for autism and ADHD in participants from the Avon Longitudinal Study of Parents and Children (N=4,255 to 6,165). We estimated the association between polygenic risk and autistic and ADHD trait scores across the area surrounding the city of Bristol in the southwest of the United Kingdom. Mapping the results shows how associations between polygenic risk and trait scores varied by location. Our maps suggest that there is evidence of spatial variation in genetic associations for all traits. For social autistic traits and an autistic trait mean factor score, patterns of association were consistent among risk scores constructed at different p-value thresholds. The patterns for ADHD traits were more variable. We examined similarities between these maps and those of environmental variables associated with the prevalence of autism and ADHD, and found that in many cases the spatial distributions were correlated. This finding of spatial variation in genetic associations for autism and ADHD traits will help us better understand the factors that contribute to the complex interplay between the environment and genetic influence.