Miracle-dz/UniFAR-FTUs huggingface dataset:
Nearest neighbour distributions: New statistical measures for cosmological clustering

The use of summary statistics beyond the two-point correlation function to analyze the non-Gaussian clustering on small scales is an active field of research in cosmology. In this paper, we explore a set of new summary statistics -- the $k$-Nearest Neighbor Cumulative Distribution Functions ($k{\rm NN}$-${\rm CDF}$). This is the empirical cumulative distribution function of distances from a set of volume-filling, Poisson distributed random points to the $k$--nearest data points, and is sensitive to all connected $N$--point correlations in the data. The $k{\rm NN}$-${\rm CDF}$ can be used to measure counts in cell, void probability distributions and higher $N$--point correlation functions, all using the same formalism exploiting fast searches with spatial tree data structures. We demonstrate how it can be computed efficiently from various data sets - both discrete points, and the generalization for continuous fields. We use data from a large suite of $N$-body simulations to explore the sensitivity of this new statistic to various cosmological parameters, compared to the two-point correlation function, while using the same range of scales. We demonstrate that the use of $k{\rm NN}$-${\rm CDF}$ improves the constraints on the cosmological parameters by more than a factor of $2$ when applied to the clustering of dark matter in the range of scales between $10h^{-1}{\rm Mpc}$ and $40h^{-1}{\rm Mpc}$. We also show that relative improvement is even greater when applied on the same scales to the clustering of halos in the simulations at a fixed number density, both in real space, as well as in redshift space. Since the $k{\rm NN}$-${\rm CDF}$ are sensitive to all higher order connected correlation functions in the data, the gains over traditional two-point analyses are expected to grow as progressively smaller scales are included in the analysis of cosmological data.
A randomized double-blinded sham-controlled cross-over trial of tined-lead sacral nerve stimulation testing for chronic constipation

Objectives Sacral nerve stimulation (SNS) may provide long-term symptom relief to patients suffering from chronic constipation. Patients are currently selected for SNS using a 2-week peripheral nerve evaluation (PNE) comprising stimulation by temporary leads. However, only 40% of test responders receive long-term benefit from treatment meaning that healthcare costs per successfully treated patient are too high. The primary objective was to assess tined-lead testing to predict benefit from SNS for chronic constipation. Patients and methods A randomized double-blind sham-controlled cross-over design evaluated enhanced PNE (ePNE) using tined quadripolar electrode leads over 6 weeks. The design differentiated between patients with discriminate and indiscriminate responses to testing. A score improvement of 25% or more was considered to be a positive response within a stimulation period. The primary outcome was the proportion of patients showing a reduction of at least 0.5 in constipation symptom score at 6 months. Results A total of 45 patients were randomized, of whom 29 (64.4%) were test-phase responders. Of these, 27 were implanted providing permanent SNS. During ePNE, seven (18%) were discriminate responders, 22 (56%) were indiscriminate responders and 10 (26%) were nonresponders. Six patients were withdrawn during the test phase because of infection or noncompliance. At 6 months, there was no significant difference in primary outcome between discriminate and indiscriminate responders (60 vs. 57%, P=0.76). The study was terminated prematurely because of a persistent infection rate of 10 (22%) during ePNE of which nine (20%) were severe. Conclusion ePNE is a poor predictor of treatment response at 6 months. This suggests a strong and persistent placebo response during both SNS PNE and treatment. An extended 6-week PNE poses a high risk of infection.
Asymptotic behavior of stochastic currents under large deviation scaling with mean field interaction and vanishing noise

We study the large deviation behavior of a system of diffusing particles with a mean field interaction, described through a collection of stochastic differential equations, in which each particle is driven by a vanishing independent Brownian noise. An important object in the description of the asymptotic behavior, as the number of particles approach infinity and the noise intensity approaches zero, is the stochastic current associated with the interacting particle system in the sense of Flandoli et al. (2005). We establish a joint large deviation principle (LDP) for the path empirical measure for the particle system and the associated stochastic currents in the simultaneous large particle and small noise limit. Our work extends recent results of Orrieri (2018), in which the diffusion coefficient is taken to be identity, to a setting of a state dependent and possibly degenerate noise with the mean field interaction influencing both the drift and diffusion coefficients, and allows for a stronger topology on the space of stochastic currents in the LDP. Proof techniques differ from Orrieri (2018) and rely on methods from stochastic control, theory of weak convergence, and representation formulas for Laplace functionals of Brownian motions.
Observers and observations in physics theories

The role of observers and observations in physics theories is considered in the light of Godel's incompleteness theorem. Incompleteness arises in Godel's theorem with self-referential propositions, when the system asks to define itself in its own terms. Self-referencing occurs in physics whenever the observer is recognized as being part of the observed system, so the acts of observation are also observables and should become part of the phenomena considered by the theory. This is emphasized by the fact that in many instances the same physical phenomenon may be viewed in more than one way, hence its interpretation depends on the mode of observation. Observations of observations imply a potentially endless hierarchy of levels of observations. Each higher level suggests a larger overview with more profound insight into the universe, empirically implying essentially new discoveries and realizations. New discoveries imply new first principles in the foundation of the theory, so the theory remains open and cannot be complete.
Rock Crawlers in Baltic Amber (Notoptera: Mantophasmatodea)

Abstract A fourth species of rock crawler (Notoptera: Mantophasmatodea: Mantophasmatidae) is described and figured from an individual preserved in middle Eocene (Lutetian) Baltic amber. Adicophasma grylloblattoides Arillo and Engel, new species, is distinguished from its close relative, A. spinosum Engel and Grimaldi (reinstated), by the reduced pedicel, absence of spines on the maxillae, absence of mesofemoral spination, and proportions of the thoracic segments. The fossil shares with A. spinosum the presence of profemoral spination (confirmed by a new photograph of the holotype) and absence of the dorsal profemoral carina, characters that differentiate Adicophasma from the monotypic Raptophasma; it shares with all Baltic amber Notoptera the absence of the setal fan on the arolium. As noted by previous authors, the former order Mantophasmatodea is related to modern Grylloblattodea, whereas Mesozoic and Paleozoic grylloblattodeans represent a stem group to both. As such, Grylloblattodea and Mantophasmatodea are considered suborders of a single order, Notoptera Crampton (sensu novum), following the recommendation of Engel and Grimaldi (2004). The names for three rock crawlers are emended in order that the specific epithet may match the gender of the generic name: A. spinosum, Mantophasma zephyrum Zompro et al., and Tanzaniophasma subsolanum (Zompro et al.) (nomina emendata). Raptophasmatinae and Ensiferophasmatidae are new synonyms of Mantophasmatidae, while Tanzaniophasmatidae and Austrophasmatidae are newly demoted in rank to a subfamily and tribe of Mantophasmatidae, respectively. A hierarchical classification of Polyneoptera is appended.
Toward precision cosmochronology

The continuous cooling of a white dwarf is punctuated by events that affect its cooling rate. Probably the most significant of those is the crystallization of its core, a phase transition that occurs once the C/O interior has cooled down below a critical temperature. This transition releases latent heat as well as gravitational energy due to the redistribution of the C and O ions during solidification, thereby slowing down the evolution of the white dwarf. The unambiguous observational signature of core crystallization - a pile-up of objects in the cooling sequence - was recently reported. However, existing evolution models struggle to quantitatively reproduce this signature, casting doubt on their accuracy when used to measure the ages of stellar populations. The timing and amount of the energy released during crystallization depend on the exact form of the C/O phase diagram. Using the advanced Gibbs-Duhem integration method and state-of-the-art Monte Carlo simulations of the solid and liquid phases, we have obtained a very accurate version of this phase diagram, allowing a precise modeling of the phase transition. Despite this improvement, the magnitude of the crystallization pile-up remains underestimated by current evolution models. We conclude that latent heat release and O sedimentation alone are not sufficient to explain the observations and that other unaccounted physical mechanisms, possibly $^{22}$Ne phase separation, play an important role.
Isolation of microalgae species from arid environments and evaluation of their potentials for biodiesel production

Twenty-five (25) strains of microalgae were isolated and screened for growth, lipid accumulation and biodiesel production from arid environments of North East Nigeria. Isolates that produced biomass concentration (≥ 1.50 g L -1 cell dry weight), accumulated high concentrations of lipids (≥ 18% of the cell biomass) and could be purified on agar plates were selected for further studies. Four strains morphologically identified as Oocystis IA1, Chlorella IA7, Chlorococcum KA9 and Botyrococcus YA5 were selected. The maximum biomass concentrations (g L -1 ) and lipid contents (%) were 3.02, 3.92, 3.59 and 2.89, and 57, 67.23, 63 and 65 dry cell weights for Oocystis IA1, Chlorella IA7, Chlorococcum KA9 and Botyrococcus YA5, respectively. The highest oil productivities (mg L -1 day -1 ) were 135.28 ± 3.32 ( Oocystis IA1), 165.22 ± 3.36 ( Chlorella IA7), 131.76 ± 1.11 ( Chlorococcum KA9) and 140.37 ± 2.13 ( Botyrococcus YA5). Maximum chlorophyll contents (g g -1 cell) were 34.97, 30.00, 39.71 and 32.27, respectively. Fatty acid methyl ester profiles indicated the presence of C16:0, C16:1, C18:0, C18:1, and C18:2. Oleic acid (C18: 1) was predominant, ranging between 73.3 and 85.6%. Biodiesel properties were within the ASTM standards. The present study suggested that the four isolates are good for biodiesel production. Keywords: Biodiesel, Chlorella , Chlorococcum , Botyrococcus sp. , lipid accumulation, microalgae, Oocystis . Abbreviation: FAME, Fatty acids methyl esters.
Lessons on Star-forming Ultra-diffuse Galaxies from the Stacked Spectra of the Sloan Digital Sky Survey

We investigate the on-average properties for 28 star-forming ultra-diffuse galaxies (UDGs) located in low-density environments, by stacking their spectra from the Sloan Digital Sky Survey. These relatively isolated UDGs, with stellar masses of , have the on-average total stellar metallicity [M/H] ∼ −0.82 ± 0.14, iron metallicity [Fe/H] ∼ −1.00 ± 0.16, stellar age t* ∼ 5.2 ± 0.5 Gyr, α-enhancement [α/Fe] ∼ 0.24 ± 0.10, and oxygen abundance 12+log(O/H) ∼ 8.16 ± 0.06, as well as central stellar velocity dispersion 54 ± 12 km s−1. On the star formation rate versus stellar mass diagram, these UDGs are located lower than the extrapolated star-forming main sequence from the massive spirals, but roughly follow the main sequence of low-surface-brightness dwarf galaxies. We find that these star-forming UDGs are not particularly metal-poor or metal-rich for their stellar masses, as compared with the metallicity–mass relations of the nearby typical dwarfs. With the UDG data of this work and previous studies, we also find a coarse correlation between [Fe/H] and magnesium-element enhancement [Mg/Fe] for UDGs: [Mg/Fe] ≃ −0.43(±0.26) [Fe/H] −0.14(±0.40).
Infrared spectra of pyroxenes (crystalline chain silicates) at room temperature

Crystals of pyroxene are common in meteorites but few compositions have been recognized in astronomical environments due to the limited chemistries included in laboratory studies. We present quantitative room-temperature spectra of 17 Mg-, Fe-, and Ca-bearing ortho- and clinopyroxenes, and a Ca-pyroxenoid in order to discern trends indicative of crystal structure and a wide range of composition. Data are produced using a diamond anvil cell: our band strengths are up to six times higher than those measured in KBr or polyethylene dispersions, which include variations in path length (from grain size) and surface reflections that are not addressed in data processing. Pyroxenes have varied spectra: only two bands, at 10.22 and 15.34 μm in enstatite (En99), are common to all. Peak wavelengths generally increase as Mg is replaced by Ca or Fe. However, two bands in MgFe-pyroxenes shift to shorter wavelengths as the Fe component increases from 0 to 60 per cent. A high-intensity band shifts from 11.6 to 11.2 μm and remains at 11.2 μm as Fe increases to 100 per cent; it resembles an astronomical feature normally identified with olivine or forsterite. The distinctive pyroxene bands between 13 and 16 μm show promise for their identification in Mid-Infrared-Instrumentspectra obtained with the James Webb Space Telescope. The many pyroxene bands between 40 and 80 μm could be diagnositic of silicate mineralogy if data were obtained with the proposed Space Infrared Telescope for Cosmology and Astrophysics. Our data indicate that comparison between room-temperature laboratory bands for enstatite and cold ∼10 − K astronomical dust features at wavelengths ≳28 μm can result in the identification of (Mg,Fe)- pyroxenes that contain 7–15 per cent less Fe– than their true values because some temperature shifts mimic some compositional shifts. Therefore some astronomical silicates may contain more Fe, and less Mg, than previously thought.
Weak polynomial identities and their applications

Abstract Let R be an associative algebra over a field K generated by a vector subspace V. The polynomial f(x1, . . . , xn) of the free associative algebra K〈x1, x2, . 〉 is a weak polynomial identity for the pair (R, V) if it vanishes in R when evaluated on V. We survey results on weak polynomial identities and on their applications to polynomial identities and central polynomials of associative and close to them nonassociative algebras and on the finite basis problem. We also present results on weak polynomial identities of degree three.
Evolve To Control: Evolution-based Soft Actor-Critic for Scalable Reinforcement Learning

Advances in Reinforcement Learning (RL) have successfully tackled sample efficiency and overestimation bias. However, these methods often fall short of scalable performance. On the other hand, genetic methods provide scalability but depict hyperparameter sensitivity to evolutionary operations. We present the Evolution-based Soft Actor-Critic (ESAC), a scalable RL algorithm. Our contributions are threefold; ESAC (1) abstracts exploration from exploitation by combining Evolution Strategies (ES) with Soft Actor-Critic (SAC), (2) provides dominant skill transfer between offsprings by making use of soft winner selections and genetic crossovers in hindsight and (3) improves hyperparameter sensitivity in evolutions using Automatic Mutation Tuning (AMT). AMT gradually replaces the entropy framework of SAC allowing the population to succeed at the task while acting as randomly as possible, without making use of backpropagation updates. On a range of challenging control tasks consisting of high-dimensional action spaces and sparse rewards, ESAC demonstrates state-of-the-art performance and sample efficiency equivalent to SAC. ESAC demonstrates scalability comparable to ES on the basis of hardware resources and algorithm overhead. A complete implementation of ESAC with notes on reproducibility and videos can be found at the project website this https URL.
Synthetic Spectra of Rotating Stars

Many early-type stars have oblate surfaces, spatial temperature variations, and spectral line broadening that indicate large rotational velocities. Rotation ought to have a significant effect on the full spectra of such stars. To infer structural and life history parameters from their spectra, one must integrate specific intensity over the two-dimensional surfaces of corresponding stellar models. Toward this end, we offer Paint the Atmospheres of Rotating Stars—an integration scheme based on models that incorporate solid body rotation, Roche mass distribution, and collinearity of gravity and energy flux. The scheme features a closed-form expression for the azimuthal integral, a high-order numerical approximation of the longitudinal integral, and a precise calculation of surface effective temperature at rotation rates up to 99.9% of the Keplerian limit. Extensions of the scheme include synthetic color–magnitude diagrams and planetary transit curves.
A Bayesian framework for case-cohort Cox regression: application to dietary epidemiology.

The case-cohort study design bypasses resource constraints by collecting certain expensive covariates for only a small subset of the full cohort. Weighted Cox regression is the most widely used approach for analyzing case-cohort data within the Cox model, but is inefficient. Alternative approaches based on multiple imputation and nonparametric maximum likelihood suffer from incompatibility and computational issues respectively. We introduce a novel Bayesian framework for case-cohort Cox regression that avoids the aforementioned problems. Users can include auxiliary variables to help predict the unmeasured expensive covariates with a prediction model of their choice, while the models for the nuisance parameters are nonparametrically specified and integrated out. Posterior sampling can be carried out using procedures based on the pseudo-marginal MCMC algorithm. The method scales effectively to large, complex datasets, as demonstrated in our application: investigating the associations between saturated fatty acids and type 2 diabetes using the EPIC-Norfolk study. As part of our analysis, we also develop a new approach for handling compositional data in the Cox model, leading to more reliable and interpretable results compared to previous studies. The performance of our method is illustrated with extensive simulations, including the use of synthetic data generated by resampling from the application dataset. The code used to produce the results in this paper can be found at this https URL .
Physical Layer Security of Large Reflecting Surface Aided Communications With Phase Errors

The physical layer security (PLS) performance of a wireless communication link through a large reflecting surface (LRS) with phase errors is analyzed. Leveraging recent results that express the LRS-based composite channel as an equivalent scalar fading channel, we show that the eavesdropper’s link is Rayleigh distributed and independent of the legitimate link. The different scaling laws of the legitimate and eavesdroppers signal-to-noise ratios with the number of reflecting elements, and the reasonably good performance even in the case of coarse phase quantization, show the great potential of LRS-aided communications to enhance PLS in practical wireless set-ups.
Epidemiological studies on powdery mildew of mungbean and urdbean

Abstract Powdery mildew of mungbean (Vigna radiata L. Wilczek) and urdbean ( V. mungo L. Hepper) caused by Erysiphe polyponi DC during the winter/spring season is a severe constraint in the production of bean crops in the Chhattisgarh region of Madhya Pradesh. Keeping this in view, studies were made to understand the development of powdery mildew in relation to crops, varieties, weather conditions and their effect on yield. The first appearance of powdery mildew in 33 mungbean and 18 urdbean varieties was 1 week earlier during winter 1992–93 than winter 1991–92. Its development was most rapid each year when the average maximum temperature varied from 27.2 to 30.3°C, relative humidity from 67 to 90% during the morning and 12 to 38% at noon, and wind velocity from 2.3 to 4.1 km/h. A positive correlation occurred between mildew severity and temperatures and wind velocity in most of the varieties. However, the correlation with relative humidity was negative and significant except in a few varieties. The poole.
Investigating the effect of edge modifications on networked control systems

This paper investigates the impact of addition/removal of edges in a complex networked control system, for the purposes of improving its controllability, system performances or robustness to external disturbances. The transfer function formulation we obtain allows to quantify the impact of an edge modification with the $\hinf$ and $\htwo$ norms. For stable networks with positive edge weights, we show that the $\hinf$ norm can be computed exactly for each possible single edge modification, as well as the associated stability margin. For the $\htwo$ norm we instead obtain a lower bound. Since this bound is linked to the trace of the controllability Gramian, it can be used for instance to reduce the energy needed for control. When instead the dynamics is of Laplacian type, then the norms become unbounded. However, the associated displacement systems are stable and for them the effect of edge modifications can be quantified. In particular, in this case we provide an upper bound on the $ \hinf$ norm and compute the exact value of the $ \htwo $ norm for arbitrary edge additions.
A finite sample analysis of the benign overfitting phenomenon for ridge function estimation

Recent extensive numerical experiments in high scale machine learning have allowed to uncover a quite counterintuitive phase transition, as a function of the ratio between the sample size and the number of parameters in the model. As the number of parameters $p$ approaches the sample size $n$, the generalisation error increases, but surprisingly, it starts decreasing again past the threshold $p=n$. This phenomenon, brought to the theoretical community attention in \cite{belkin2019reconciling}, has been thoroughly investigated lately, more specifically for simpler models than deep neural networks, such as the linear model when the parameter is taken to be the minimum norm solution to the least-squares problem, firstly in the asymptotic regime when $p$ and $n$ tend to infinity, see e.g. \cite{hastie2019surprises}, and recently in the finite dimensional regime and more specifically for linear models \cite{bartlett2020benign}, \cite{tsigler2020benign}, \cite{lecue2022geometrical}. In the present paper, we propose a finite sample analysis of non-linear models of \textit{ridge} type, where we investigate the \textit{overparametrised regime} of the double descent phenomenon for both the \textit{estimation problem} and the \textit{prediction} problem. Our results provide a precise analysis of the distance of the best estimator from the true parameter as well as a generalisation bound which complements recent works of \cite{bartlett2020benign} and \cite{chinot2020benign}. Our analysis is based on tools closely related to the continuous Newton method \cite{neuberger2007continuous} and a refined quantitative analysis of the performance in prediction of the minimum $\ell_2$-norm solution.