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Cosmic voids identified in the spatial distribution of galaxies provide complementary information to two-point statistics. In particular, constraints on the neutrino mass sum, ∑ m ν , promise to benefit from the inclusion of void statistics. We perform inference on the CMASS Northern Galactic Cap sample of the third-generation Sloan Digital Sky Survey/Baryon Oscillation Spectroscopic Survey (BOSS) with the aim of constraining ∑ m ν . We utilize the void size function, the void–galaxy cross power spectrum, and the galaxy auto power spectrum. To extract constraints from these summary statistics we use a simulation-based approach, specifically implicit likelihood inference. We populate approximate gravity-only, particle-neutrino cosmological simulations with an expressive halo occupation distribution model. With a conservative scale cut of k max = 0.15 h Mpc − 1 and a Planck-inspired Lambda cold dark matter prior, we find upper bounds on ∑ m ν of 0.43 and 0.35 eV from the galaxy auto power spectrum and the full data vector, respectively (95% credible interval). Relaxing the scale cut to k max = 0.2 h Mpc − 1 , we find 0.28 and 0.32 eV. Generally, void statistics appear to add modest but nonzero information beyond the auto power spectrum. We also substantiate the usual assumption that the void size function is Poisson distributed. [ABSTRACT FROM AUTHOR]

Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar's brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling. [ABSTRACT FROM AUTHOR]

Galaxies play a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs, spanning cosmologically significant volumes. For such a daunting task, methods that can produce a direct mapping between dark matter halos from dark matter-only simulations and galaxies are strongly preferred, as producing mocks from full-fledged hydrodynamical simulations or semi-analytical models is too expensive. Here, we present a graph-neural-network-based model that is able to accurately predict key properties of galaxies such as stellar mass, g − r color, star formation rate, gas mass, stellar metallicity, and gas metallicity, purely from dark matter properties extracted from halos along the full assembly history of the galaxies. Tests based on the TNG300 simulation of the IllustrisTNG project show that our model can recover the baryonic properties of galaxies to high accuracy, over a wide redshift range (z = 0–5), for all galaxies with stellar masses more massive than 109 M ⊙ and their progenitors, with strong improvements over the state-of-the-art methods. We further show that our method makes substantial strides toward providing an understanding of the implications of the IllustrisTNG galaxy formation model. [ABSTRACT FROM AUTHOR]

The discovery of the optical counterpart, along with the gravitational waves (GWs) from GW170817, of the first binary neutron star merger has opened up a new era for multimessenger astrophysics. Combining the GW data with the optical counterpart, also known as AT 2017gfo and classified as a kilonova, has revealed the nature of compact binary merging systems by extracting enriched information about the total binary mass, the mass ratio, the system geometry, and the equation of state. Even though the detection of kilonovae has brought about a revolution in the domain of multimessenger astronomy, there has been only one kilonova from a GW-detected binary neutron star merger event confirmed so far, and this limits the exact understanding of the origin and propagation of the kilonova. Here, we use a conditional variational autoencoder (CVAE) trained on light-curve data from two kilonova models having different temporal lengths, and consequently, generate kilonova light curves rapidly based on physical parameters of our choice with good accuracy. Once the CVAE is trained, the timescale for light-curve generation is of the order of a few milliseconds, which is a speedup of the generation of light curves by 1000 times as compared to the simulation. The mean squared error between the generated and original light curves is typically 0.015 with a maximum of 0.08 for each set of considered physical parameters, while having a maximum of ≈0.6 error across the whole parameter space. Hence, implementing this technique provides fast and reliably accurate results. [ABSTRACT FROM AUTHOR]

We investigate the utility of deep learning for modeling the clustering of particles that are aerodynamically coupled to turbulent fluids. Using a Lagrangian particle module within the Athena++ hydrodynamics code, we simulate the dynamics of particles in the Epstein drag regime within a periodic domain of isotropic forced hydrodynamic turbulence. This setup is an idealized model relevant to the collisional growth of micron- to millimeter-sized dust particles in early-stage planet formation. The simulation data are used to train a U-Net deep-learning model to predict gridded three-dimensional representations of the particle density and velocity fields, given as input the corresponding fluid fields. The trained model qualitatively captures the filamentary structure of clustered particles in a highly nonlinear regime. We assess model fidelity by calculating metrics of the density field (the radial distribution function) and of the velocity field (the relative velocity and the relative radial velocity between particles). Although trained only on the spatial fields, the model predicts these statistical quantities with errors that are typically <10%. Our results suggest that, given appropriately expanded training data, deep learning could complement direct numerical simulations in predicting particle clustering within turbulent flows. [ABSTRACT FROM AUTHOR]

We present a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By combining likelihood heterodyning, automatically differentiable, and accelerator-compatible waveforms, and gradient-based Markov Chain Monte Carlo sampling enhanced by normalizing flows, we achieve full Bayesian parameter estimation for real events like GW150914 and GW170817 within a minute of sampling time. Our framework does not require pretraining or explicit reparameterizations and can be generalized to handle higher dimensional problems. We present the details of our implementation and discuss trade-offs and future developments in the context of other proposed strategies for real-time parameter estimation. Our code for running the analysis is publicly available on GitHub at https://github.com/kazewong/jim. [ABSTRACT FROM AUTHOR]

The pyramid wave front sensor (PyWFS) has become increasingly popular to use in adaptive optics (AO) systems due to its high sensitivity. The main drawback of the PyWFS is that it is inherently nonlinear, which means that classic linear wave front reconstruction techniques face a significant reduction in performance at high wave front errors, particularly when the pyramid is unmodulated. In this paper, we consider the potential use of neural networks (NNs) to replace the widely used matrix vector multiplication (MVM) control. We aim to test the hypothesis that the NN's ability to model nonlinearities will give it a distinct advantage over MVM control. We compare the performance of a MVM linear reconstructor against a dense NN, using daytime data acquired on the Subaru Coronagraphic Extreme Adaptive Optics system (SCExAO) instrument. In a first set of experiments, we produce wavefronts generated from 14 Zernike modes and the PyWFS responses at different modulation radii (25, 50, 75, and 100 mas). We find that the NN allows for a far more precise wave front reconstruction at all modulations, with differences in performance increasing in the regime where the PyWFS nonlinearity becomes significant. In a second set of experiments, we generate a data set of atmosphere-like wavefronts, and confirm that the NN outperforms the linear reconstructor. The SCExAO real-time computer software is used as baseline for the latter. These results suggest that NNs are well positioned to improve upon linear reconstructors and stand to bring about a leap forward in AO performance in the near future. [ABSTRACT FROM AUTHOR]

Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase coverage of an event, limiting their use for triggering rapid follow-up of ongoing phenomena. In this work, we introduce a neural network with a single recurrent layer designed explicitly for early photometric classification of supernovae (SNe). Our algorithm leverages transfer learning to account for model misspecification, host-galaxy photometry to solve the data-scarcity problem soon after discovery, and a custom weighted loss to prioritize accurate early classification. We first train our algorithm using state-of-the-art transient and host-galaxy simulations, then adapt its weights and validate it on the spectroscopically confirmed SNe Ia, SNe II, and SNe Ib/c from the Zwicky Transient Facility Bright Transient Survey. On observed data, our method achieves an overall accuracy of 82% ± 2% within 3 days of an event's discovery, and an accuracy of 87% ± 5% within 30 days of discovery. At both early and late phases, our method achieves comparable or superior results to the leading classification algorithms with a simpler network architecture. These results help pave the way for rapid photometric and spectroscopic follow-up of scientifically valuable transients discovered in massive synoptic surveys. [ABSTRACT FROM AUTHOR]

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine-learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but they must be trained carefully on large and representative data sets. We present a new "hump" of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter-only simulations of (100 h −1 cMpc)3 with different cosmological parameters (Ω m and σ 8) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof of concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation, count-in-cells, and void probability functions, and we probe nonlinear and linear scales across 0.68 < R <27 h −1 cMpc. We find our neural networks can both marginalize over the uncertainties in astrophysics to constrain cosmology to 3%–8% error across various types of galaxy selections, while simultaneously learning about the SC-SAM astrophysical parameters. This work encompasses vital first steps toward creating algorithms able to marginalize over the uncertainties in our galaxy formation models and measure the underlying cosmology of our Universe. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and it offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io. [ABSTRACT FROM AUTHOR]

Blue Horizontal Branch stars (BHBs) are ideal tracers to probe the global structure of the milky Way (MW), and the increased size of the BHB star sample could be helpful to accurately calculate the MW's enclosed mass and kinematics. Large survey telescopes have produced an increasing number of astronomical images and spectra. However, traditional methods of identifying BHBs are limited in dealing with the large scale of astronomical data. A fast and efficient way of identifying BHBs can provide a more significant sample for further analysis and research. Therefore, in order to fully use the various data observed and further improve the identification accuracy of BHBs, we have innovatively proposed and implemented a Bi-level attention mechanism-based Transformer multimodal fusion model, called Bi-level Attention in the Transformer with Multimodality (BATMM). The model consists of a spectrum encoder, an image encoder, and a Transformer multimodal fusion module. The Transformer enables the effective fusion of data from two modalities, namely image and spectrum, by using the proposed Bi-level attention mechanism, including cross-attention and self-attention. As a result, the information from the different modalities complements each other, thus improving the accuracy of the identification of BHBs. The experimental results show that the F 1 score of the proposed BATMM is 94.78%, which is 21.77% and 2.76% higher than the image and spectral unimodality, respectively. It is therefore demonstrated that higher identification accuracy of BHBs can be achieved by means of using data from multiple modalities and employing an efficient data fusion strategy. [ABSTRACT FROM AUTHOR]

Rapidly growing catalogs of compact binary mergers from advanced gravitational wave detectors allow us to explore the astrophysics of massive stellar binaries. Merger observations can constrain the uncertain parameters that describe the underlying processes in the evolution of stars and binary systems in population models. In this paper, we demonstrate that binary black hole populations—in particular, their detection rates, chirp masses, and redshifts—can be used to measure cosmological parameters describing the redshift-dependent star formation rate and metallicity distribution. We present a method that uses artificial neural networks to emulate binary population synthesis computer models, and construct a fast, flexible, parallelizable surrogate model that we use for inference. [ABSTRACT FROM AUTHOR]

The Wind spacecraft has yielded several decades of high-resolution magnetic field data, a large fraction of which displays small-scale structures. In particular, the solar wind is full of wavelike fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks, that have implications for the time evolution of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that these fluctuations have on solar wind evolution. Given the large volume of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed. To this end, a subset of Wind data is labeled and used as a training set for a multibranch 1D convolutional neural network aimed at classifying circularly polarized wave modes. Using this algorithm, a preliminary statistical study of 1 yr of data is performed, yielding about 300,000 wave intervals out of about 5,000,000 solar wind intervals. The wave intervals come about more often in the fast solar wind and at higher temperatures, and the number of waves per day is highly periodic. This machine learning–based approach to wave detection has the potential to be a powerful, inexpensive way to catalog waves throughout decades of spacecraft data. [ABSTRACT FROM AUTHOR]

Modern X-ray telescopes have detected hundreds of thousands of X-ray sources in the universe. However, current methods to classify these sources using the X-ray data themselves suffer problems—detailed X-ray spectroscopy of individual sources is too time consuming, while hardness ratios often lack accuracy, and can be difficult to use effectively. These methods fail to use the power of X-ray CCD detectors to identify X-ray emission lines and distinguish line-dominated spectra (from chromospherically active stars, supernova remnants, etc.) from continuum-dominated ones (e.g., compact objects or active galactic nuclei, AGN). In this paper, we probe the use of artificial neural networks (ANN) in differentiating Chandra spectra of young stars in the Chandra Orion Ultradeep Project (COUP) survey from AGN in the Chandra Deep Field South (CDFS) survey. We use these surveys to generate 100,000 artificial spectra of stars and AGN, and train our ANN models to separate the two kinds of spectra. We find that our methods reach an accuracy of ∼92% in classifying simulated spectra of moderate-brightness objects in typical exposures, but their performance decreases on the observed COUP and CDFS spectra (∼91%), due in large part to the relatively high background of these long-exposure data sets. We also investigate the performance of our methods with changing properties of the spectra such as the net source counts, the relative contribution of background, the absorption column of the sources, etc. We conclude that these methods have substantial promise for application to large X-ray surveys. [ABSTRACT FROM AUTHOR]

A deep learning (DL) algorithm is built and tested for its ability to determine centers of star images in HST/WFPC2 exposures, in filters F555W and F814W. These archival observations hold great potential for proper-motion studies, but the undersampling in the camera's detectors presents challenges for conventional centering algorithms. Two exquisite data sets of over 600 exposures of the cluster NGC 104 in these filters are used as a testbed for training and evaluating the DL code. Results indicate a single-measurement standard error from 8.5 to 11 mpix, depending on the detector and filter. This compares favorably to the ∼20 mpix achieved with the customary "effective point spread function (PSF)" centering procedure for WFPC2 images. Importantly, the pixel-phase error is largely eliminated when using the DL method. The current tests are limited to the central portion of each detector; in future studies, the DL code will be modified to allow for the known variation of the PSF across the detectors. [ABSTRACT FROM AUTHOR]

Breakdown of rotational invariance of the primordial power spectrum manifests in the statistical anisotropy of the observed Cosmic Microwave Background (CMB) radiation. Hemispherical power asymmetry in the CMB may be caused due to a dipolar modulation, indicating the presence of a preferred direction. Appropriately rescaled local variance maps of the CMB temperature anisotropy data effectively encapsulate this dipolar pattern. As a first-of-its-kind method, we train Artificial Neural Networks (ANNs) with such local variances as input features to distinguish statistically isotropic CMB maps from dipole-modulated ones. Our trained ANNs are able to predict components of the amplitude times the unit vector of the preferred direction for mixed sets of modulated and unmodulated maps, with goodness-of-fit (R 2) scores >0.97 for full sky and >0.96 for partial sky coverage. On all observed foreground-cleaned CMB maps, the ANNs detect the dipolar modulation signal with overall consistent values of amplitudes and directions. This detection is significant at 97.21%–99.38% C.L. for all full sky maps, and at 98.34%–100% C.L. for all partial sky maps. Robustness of the signal holds across full and partial skies, various foreground cleaning methods, inpainting algorithms, instruments, and all the different periods of observation for Planck and WMAP satellites. The significant and robust detection of the signal, in addition to the consistency of values of amplitude and directions, as found independent of any preexisting methods, further mitigates the criticisms of look-elsewhere effects and a posteriori inferences for the preferred dipole direction in the CMB. [ABSTRACT FROM AUTHOR]

We present a machine-learning framework to accurately characterize the morphologies of active galactic nucleus (AGN) host galaxies within z < 1. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (G a M or N et) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low (0 < z < 0.25), mid (0.25 < z < 0.5), and high (0.5 < z < 1.0). By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for ∼60%–70% of the host galaxies from test sets, with a classification precision of ∼80%–95%, depending on the redshift bin. Specifically, our models achieve a disk precision of 96%/82%/79% and bulge precision of 90%/90%/80% (for the three redshift bins) at thresholds corresponding to indeterminate fractions of 30%/43%/42%. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+G a M or N et framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging surveys. [ABSTRACT FROM AUTHOR]

We report the application of implicit likelihood inference to the prediction of the macroparameters of strong lensing systems with neural networks. This allows us to perform deep-learning analysis of lensing systems within a well-defined Bayesian statistical framework to explicitly impose desired priors on lensing variables, obtain accurate posteriors, and guarantee convergence to the optimal posterior in the limit of perfect performance. We train neural networks to perform a regression task to produce point estimates of lensing parameters. We then interpret these estimates as compressed statistics in our inference setup and model their likelihood function using mixture density networks. We compare our results with those of approximate Bayesian neural networks, discuss their significance, and point to future directions. Based on a test set of 100,000 strong lensing simulations, our amortized model produces accurate posteriors for any arbitrary confidence interval, with a maximum percentage deviation of 1.4% at the 21.8% confidence level, without the need for any added calibration procedure. In total, inferring 100,000 different posteriors takes a day on a single GPU, showing that the method scales well to the thousands of lenses expected to be discovered by upcoming sky surveys. [ABSTRACT FROM AUTHOR]

Background. The theme of famine in the USSR and the regions of the country in the early 1930s is relevant in connection with the need to study the sources of the Stalinist modernization of Russia, which created a powerful military-industrial complex during the first five-year plans, which ensured the victory of the Soviet people in the Great Patriotic War. Its creation became possible due to the use of internal sources by the Stalinist regime, the main of which was the Soviet village, including Penza. Bread and raw materials were taken from the countryside with the help of the forcible creation of a collective farm system and forced procurement of agricultural products. This led to a crisis in the agriculture of the USSR and famine in its main grain-producing regions, including Penza region. Materials and methods. Published documents, documents of the Penza State Archive and the central archives of Russia are the sources used in this work. The historical-comparative method was used. Results. In the course of the study, the causes of the famine, its scale and consequences in the regions of the Middle Volga and Lower Volga regions, which later became part of Penza region, were identified. Conclusions. In 1933, famine struck most of the southeastern regions of the region, food difficulties were experienced by all of its rural and urban population. The famine came as a result of collectivization and forced grain procurements in 1932. The victims of the famine in Penza region were mainly rural residents: collective farmers and individual farmers.

In his 1933 article, Bruno Schulz reported a follow-up and reanalysis of the schizophrenic probands and their relatives first studied by Rüdin 20 years earlier that sought to clarify whether schizophrenia was a valid "unit-character" for Mendelian genetic analysis. He proposed a range of subgroupings of probands, particularly traditional subtyping, presence or absence of identifiable causal influences, and outcome. He then compared those subgroupings in several ways, most commonly by the risk for schizophrenia in their siblings and by the level of resemblance among proband-sibling affected pairs. Of his many findings, those of greatest interest included (1) probands with possible and probable physical causes, particularly those with head trauma, had substantially lower risk of illness in siblings, (2) probands with a hebephrenic subtype had a striking elevation of risk for schizophrenia in siblings, (3) probands with psychological causes had higher rates of good outcome, (4) proband-sibling pairs resembled one another for the classical schizophrenic subtypes, and (5) an absence of any cases of schizophrenia in siblings of a small group of schizophrenic probands with birth complications, convulsions, and skull deformities. Schulz used this sample in a fundamentally different way than Rüdin. Rather than seeking for Mendelian transmission patterns, Schulz used family data to evaluate hypothesis about clinical/etiological heterogeneity, thereby presaging many subsequent family studies of psychiatric disorders. While Schulz did not claim to have proved the etiologic heterogeneity of schizophrenia, he raised important questions, still unanswered, about whether schizophrenia is a legitimate "unit-character" appropriate for genetic analysis. [ABSTRACT FROM AUTHOR]

The authors’ objective was to study the main aspects of militia’s activities in 1933. This objective is specified in the following tasks: to highlight the work of militia regarding the introduction of passportization in Ukraine; to reveal certain aspects of its activity during the grain supplies in 1933. The authors of the article highlight for the first time the role of militia in carrying out grain supplies in 1933 in Ukraine. Materials on the implementation of passportization in the Republic have been supplemented through the prism of the tasks and activities of militia in this direction. It has been noted that the system of registration and control of population in the cities of Ukrainian SSR was radically changed at the end of 1932. The exact time for the introduction of passportization was chosen not by chance – collectivization in Ukraine ended and the Holodomor began. The result of these processes was a massive flight of peasants from villages who tried to survive and escape from the Holodomor. The main feature of the new passport system was the fact that passports were issued only to those residents of cities, workers’ settlements, state-owned enterprises and new buildings who reached the age of 16 years. An absolute majority of villagers did not have the opportunity to get this document and therefore became attached to their places of residence. Practical conduction of passportization of the population in Ukrainian SSR revealed an unfavorable demographic situation in the country and the presence of a large number of declassed persons who did not have a permanent job or committed crimes. The introduction of the passport system positively affected the organization of combating crime and ensuring state security, the registration of receptees was established. At the same time, passportization was accompanied by numerous repressions of judicial and extrajudicial nature. Militia being a part of the State Political Administration took an active part in the grain supplies campaign of 1932-1933, which led to the Holodomor. That activity had negative and repressive nature. Officers of militia also investigated the cases involving cannibalism.

Paradoxes of history helped Istanbul become a focal point of the European humanitarian elite in the 1930s and in particular one of the topoi of literary theory. A key location in a number of individual destinies, it played an essential role in the establishment of distance as a critical method and served as an excellent observatory - due to its borderline status - for tracing literary processes diachronically and synchronically. As a result, Istanbul became not only the homeland of significant literary studies, but also an important stage in conceptualizing literature as a global phenomenon: a place where banishment goes beyond personal fate and becomes part of the biography and philosophy of comparative literature. [ABSTRACT FROM AUTHOR]

The recent discovery of a bundle of documents in the archives of the Royal Society for Asian Affairs has shed more light on the history of the Assyrian Christians of Iraq from the time of the First World War up to the Simele massacre in 1933. The documents are accounts and correspondence written primarily by a number of leading British players including Major-General Dunsterville, Sir Henry Dobbs, Colonel J.J. McCarthy, and also the Assyrian Patriarch Mar Shimun. They also shed further light on the role played by Leo Amery, Secretary of State for the Colonies. The documents appear to have been compiled by Sir Percy Sykes, the then Secretary of the Royal Central Asian Society (as the Royal Society for Asian Affairs then was) as part of an investigation into the situation of the Assyrians. This article introduces the newly-discovered collection of documents and discusses how they advance our understanding of this period. [ABSTRACT FROM AUTHOR]

The Opening of the grammar school in Kosovska Mitrovica has been in the plan since the war time in 1915. In September of the same year, after two requests to the Ministry of Education and to the ecclesiastical authorities of the Kingdom of Serbia, the long-standing desire of the residents of Kosovska Mitrovica was finally achieved, by an official command of the crown prince, Alexander Karađorđević I, which authorized the opening of incomplete quadrennial grammar school. However, the joy of people from Mitrovica was short lived. Not nearly a month after bringing the command of opening the grammar school, it was followed by a German, Austro-Hungarian and a Bulgarian offensive on the Kingdom of Serbia. The winds of war had dissolved the high hopes of people of Mitrovica about getting their first high school institution. Four years later, after the ending of the First World War and other misfortunes, the grammar school in Kosovska Mitrovica was finally opened, for its first director was appointed Avram N. Popovic, a renowned cultural and national worker. The grammar school was slowly, but certainly, developing and became one of the most prominent city institutions. A new school building was bought, the number of pupils was increasing, and the teachers and the pupils ran cultural manifestations and humanitarian actions, promoting the sport and a healthy lifestyle and helping poor pupils and fellow citizens. Just in time when the grammar school of Kosovska Mitrovica was having its huge momentum, out of the blue, there was a decision of the Ministerial Council of the Kingdom of Yugoslavia on its abolition. This happened in June of 1932. Violent reactions of teachers, students, parents and prominent citizens, and demonstrations were held on this occasion. They forced the government authorities to somewhat mitigate its decision by opening the Self-governing Grammar School. This, however, had not completely satisfied residents of Mitrovica, which lodged different petitions, letters, delegations and informal pressure at the end, and brought about restoration of the State Grammar School in his town in September next year. So, the grammar school in Kosovska Mitrovica stepped into his new epoch, which would last until the beginning of World War II.

A summary of distributional records from 19 California counties and a single Nevada county are presented for the poorly known stonefly Oroperla barbara Needham, 1933. The adult stages, egg, early larval instars, and habitat are described or redescribed, larval growth is documented for populations at four elevations in the Cosumnes River and a preliminary analysis of larval diet is presented. [ABSTRACT FROM AUTHOR]

El presente artículo aborda el estudio de la distribución de la tierra en los años treinta del siglo XX en Cantabria a partir del Registro de la Propiedad Expropiable (1933), centrándose en el análisis de la estructura de la propiedad, parcelación, residencia de los propietarios, formas de adquisición y usos del suelo. [fr] Le présent article aborde l'étude de la distribution de la terre dans Cantabna en les années trente du XXe siecle, a partir du Registre de la Propiété Foncière Expropiable (1933), étude axée sur l'analyse de la structure de la propiété, le parcellement, la résidence des propiétaires, les formes d'acquisition et les usages deu sol.