Engineering the Frequency Spectrum of Bright Squeezed Vacuum via Group Velocity Dispersion in an SU(1,1) Interferometer

Bright squeezed vacuum, a promising tool for quantum information, can be generated by high-gain parametric down-conversion. However, its frequency and angular spectra are typically quite broad, which is undesirable for applications requiring single-mode radiation. We tailor the frequency spectrum of high-gain parametric down-conversion using an SU(1,1) interferometer consisting of two nonlinear crystals with a dispersive medium separating them. The dispersive medium allows us to select a narrow band of the frequency spectrum to be exponentially amplified by high-gain parametric amplification. The frequency spectrum is thereby narrowed from (56.5 +- 0.1) to (1.22 +- 0.02) THz and, in doing so, the number of frequency modes is reduced from approximately 50 to 1.82 +- 0.02. Moreover, this method provides control and flexibility over the spectrum of the generated light through the timing of the pump.Comment: 6 pages, 5 figure

THE IMAGE OF THE PARENT FAMILY OF ADULTS WITH MENTAL DISABILITIES

Тhe article is devoted to the study of the image of the parent family of adults with mental disabilities, as this problem is little studied in modern psychology. Sample: 39 people with mental disorders aged 18 to 47 years. Research method: clinical semi-structured interview and projective methods. The results indicate that the majority of adults with mental disorders were brought up in families: 44% - in a full family, 12% - only with the mother, 3% - in the families of grandparents. Many of them continue to live with their parents and now. Communication in the family is often evaluated positively, memories are associated with parents or other family members. The respondents of this group experienced the death of loved ones. 41% of adults with mental disorders were sent by parents to special boarding schools, while half of them never saw their parents and did not communicate with them, and 14% were familiar with their parents, but had a negative experience with them. They noted generally positive plots, but they extremely seldom were connected with a family. At the same time, negative memories are often associated with the family. Respondents of both groups distinguish not only the positive characteristics of parents, but also note the importance of joint activities. The obtained data can be successfully used as a basis for further study of the relationship of the image of the parent family with the subjective well - being of adults with intellectual disabilities, as well as the development of the main targets of the psychotherapeutic process

mGPT: Few-Shot Learners Go Multilingual

Recent studies report that autoregressive language models can successfully solve many NLP tasks via zero- and few-shot learning paradigms, which opens up new possibilities for using the pre-trained language models. This paper introduces two autoregressive GPT-like models with 1.3 billion and 13 billion parameters trained on 60 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus. We reproduce the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism; Deepspeed and Megatron frameworks allow us to parallelize the training and inference steps effectively. The resulting models show performance on par with the recently released XGLM models by Facebook, covering more languages and enhancing NLP possibilities for low resource languages of CIS countries and Russian small nations. We detail the motivation for the choices of the architecture design, thoroughly describe the data preparation pipeline, and train five small versions of the model to choose the most optimal multilingual tokenization strategy. We measure the model perplexity in all covered languages and evaluate it on the wide spectre of multilingual tasks, including classification, generative, sequence labeling and knowledge probing. The models were evaluated with the zero-shot and few-shot methods. Furthermore, we compared the classification tasks with the state-of-the-art multilingual model XGLM. source code and the mGPT XL model are publicly released

Evaluation of the effect of different doses of biochar application on the yield of soybean cultivar sculptor in the conditions of аgroecological station of K.A. Timiryazev

In the conditions of the academic fields of the Timiryazev Academy (Moscow, Russia), an experiment was conducted to evaluate the impact of biochar on soybean cultivar Sculptor. Plots of 0.25 ha were laid on arable sod-podzolic soils, the doses of biochar application were 3 kg/m2, 1 kg/ m2 and reference variant without application. The plots were established in triplicate. The results of the studies showed an improvement in a number of soil properties at 1 kg per m2, with more plant biomass, more stem pods per plant. With application of 3 kg per m2 - development of soybean variety Sculptor did not have the necessary effect, at the first stages soybean developed rapidly, but later only slow development of weed plants was noted. On the reference plots plants had a smaller biomass compared to the variant with the application of 1 kg per m2. Application of biochar to soybean crops shows a positive effect on yield and biomass of plants at the application dose of 1 kg per m2

Spatially-temporal distribution of moisture content and dynamics of greenhouse gas emissions from upper soil horizons in floodplain fallow lands of Bashmakovsky district of Penza oblast

There are more than 1’300 thousand hectares of fallow lands potentially suitable for agricultural producers in the Penza region of Russia, which is 31% of the region total area. More than 300000 hectares of land have been abandoned for more than 20 years, and territory already became forest. Additionally soils under young forests – actively sequester carbon dioxide in soil. How much it makes sense to raise fallow land in terms of climate change and which areas produce the most carbon dioxide from the soil is a research question of great interest. In addition to vegetation, factors such as soil moisture and temperature influence the carbon dioxide emission from soil. As a result, the greenhouse gas fluxes monitoring, we can conclude that forest areas and natural ecosystems mostly deposit carbon dioxide, as the amount of available carbon increases due to a greater increase in biomass. Areas that are used in agriculture produce more nitrous oxide and methane, but less carbon dioxide, which is associated with the specifics of fertilization

A Family of Pretrained Transformer Language Models for Russian

Nowadays, Transformer language models (LMs) represent a fundamental component of the NLP research methodologies and applications. However, the development of such models specifically for the Russian language has received little attention. This paper presents a collection of 13 Russian Transformer LMs based on the encoder (ruBERT, ruRoBERTa, ruELECTRA), decoder (ruGPT-3), and encoder-decoder (ruT5, FRED-T5) models in multiple sizes. Access to these models is readily available via the HuggingFace platform. We provide a report of the model architecture design and pretraining, and the results of evaluating their generalization abilities on Russian natural language understanding and generation datasets and benchmarks. By pretraining and releasing these specialized Transformer LMs, we hope to broaden the scope of the NLP research directions and enable the development of industrial solutions for the Russian language

The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

International audienceForest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (aGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. aGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. all plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities

EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats

Aim: The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation‐plot records to the habitats of the EUNIS system, use it to classify a European vegetation‐plot database, and compile statistically‐derived characteristic species combinations and distribution maps for these habitats. Location: Europe. Methods: We developed the classification expert system EUNIS‐ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set‐theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species‐to‐habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results: Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man‐made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions: EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS‐ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment