Miracle-dz/UniFAR-FTUs huggingface dataset:
EVENT2MIND FOR RUSSIAN: UNDERSTANDING EMOTIONS AND INTENTS IN TEXTS. CORPUS AND MODEL FOR EVALUATION

The paper provides a comprehensive overview of the corpus for the Russian language for the commonsense inference task. Namely, we construct event phrases, which cover a wide range of everyday situations with labelled intents and reactions of the event main participant and emotions of other people involved. The dataset consists of two parts: a crowdsourced corpus of 6,756 examples from Russian sources and a translated into Russian part of the original corpus of 23,409 examples. Apart from this, we use the collected data in order to train the event2mind model for the Russian language. The paper presents careful description of the best Russian model and the results of the conducted experiments.
Compliance with Labour Legislation in Informal Labour Markets∗

I study how strengthening labour law enforcement affects compliance with the legislation and the changes generated to the equilibrium in the labour market. Identification relies on discontinuities in the allocation of labour inspectors across Colombian departments. I find that stricter enforcement increases compliance with the legislation and the provision of mandated benefits, but this effect takes place only in the formal sector of the economy. There is also an increase in the probability of being formally employed. This partially comes from the formalization of workers who were already employed in the formal sector but were kept off-the-books by their employers (i.e. intensive margin), but it is largely explained by a shift in labour supply towards the formal sector of the economy to take advantage of the higher provision of mandated benefits (i.e. extensive margin). Despite the increase in labour costs associated with these changes, overall employment levels do not decrease and wages adjust in the formal sector only in labour markets characterised by low formality rates.
Doping challenges and pathways to industrial scalability of III–V nanowire arrays

Semiconductor nanowires (NWs) have been investigated for decades, but their application into commercial products is still difficult to achieve, with triggering causes related to the fabrication cost and structure complexity. Dopant control at the nanoscale greatly narrows their exploitation as components for device integration. In this context, doping appears the truly last missing piece of the puzzle for III–V NWs, for them to become commercially exploitable. In this paper, we review the doping of bottom up III–V NW arrays grown by molecular beam epitaxy and metal-organic vapor phase epitaxy, aiming to link materials science challenges with the critical aspect of device design. First, the doping methods and mechanisms are described, highlighting the differences between self-assembled and ordered NW arrays. Then, a brief overview of the available tools for investigating the doping is offered to understand the common strategies used for doping characterization. Both aspects are crucial to discuss the recent advancements in reproducibility and up-scalability, which are discussed in view of large area fabrication for industrial production. Finally, the most common doping-related challenges are presented together with the latest solutions to achieve high performing NW-based devices. On this basis, we believe that new insights and innovative findings discussed herein will open the low dimensional materials era, on the premise of multidisciplinary collaborative works of all the sectors involved in the design and optimization of commercial products.
Design, Construction and Production of Small Scale Energy Generation Plant using Indigenous Resources

Developing countries like Pakistan are in serious energy crisis. Renewable energy resources are the best alternative for conventional energy sources. The use of indigenous resources to produce bioenergy is an excellent solution to meet the energy needs of developing countries. The aim of the study was to design, construct and production of bioenergy generation from indigenous resources to fulfil bioenergy requirement for electricity, cooking and heating. This research introduces the Best Available Technology (BAT) and bioenergy plant was constructed with local materials at minimum cost to avoid economic burden on bioenergy production cost. An underground bio-digester unit with a volume of 10 cubic meter (7 m3 bioenergy digester tank plus 3 m3 bioenergy gas cap/holder) has been installed. The daily feed was approximately 160 kilogram of cow slurry (80 kg cow dung plus 80 litres/kg water). The retention period was approximately 44 days and the reported seasonal temperature was approximately 24˚C - 32˚C. The unit was thermally insulated, so the fluctuation in temperature was slightly about ±2˚C. In experimental setup, indigenous biomass resources were mixed with water in a mixing chamber. Whole mixture enters into digester through the inlet pipe and regularly feed up to selected retention time. Anaerobic bacteria decompose the biomass in the digester and produce bioenergy. A simulation was performed to estimate relevant model parameters from experimental data. The proposed model can predict methane production behaviour from some key indicators (such as organic matter and VFAs) in the anaerobic digestion process. Results obtained from the experiment showed that the plant could generate average volume of 3.18 m3 of bioenergy biogas at average pressure of 170 mbar in a day. Results also revealed that the rate of bioenergy generation increase with respect to time from 33 to 44 days of retention time, the pressure of bioenergy generated increase from 35 mbar to 175 mbar. From the results, it was observable that the more the pressure in the chamber, the more the volume of bioenergy generated; thus, at 175 mbars, it produced maximum volume of 3.2 m3 of bioenergy.
Utilizing anatomical information for signal detection in functional magnetic resonance imaging

We are considering the statistical analysis of functional magnetic resonance imaging (fMRI) data. As demonstrated in previous work, grouping voxels into regions (of interest) and carrying out a multiple test for signal detection on the basis of these regions typically leads to a higher sensitivity when compared with voxel-wise multiple testing approaches. In the case of a multi-subject study, we propose to deﬁne the regions for each subject separately based on their individual brain anatomy, represented, e.g., by so-called Aparc labels. The aggregation of the subject-speciﬁc evidence for the presence of signals in the different regions is then performed by means of a combination function for p -values. We apply the proposed methodology to real fMRI data and demonstrate that our approach can perform comparably to a two-stage approach for which two independent experiments are needed, one for deﬁning the regions and one for actual signal detection.
Material deprivation in Spain and the role of European Structural Funds

Material deprivation of households has invested European countries in the past decade with worrying intensity. In order to evaluate the degree of this affliction and suggesting eventual solutions, relying on EU-SILC data for Spanish regions, we identify seven disadvantage/deprivation categories of households, with an increasing intensity of deprivation. Then, we identify i) the main household characteristics that determine the (relative) probability to fall into one material deprivation category and ii) which axis of the Cohesion Funds (social inclusion or sustainable and quality of employment) matters to reduce the probability of falling into a certain category of material deprivation. The main results show that living in rural areas, having persons with a disability and a female householder increase the probability of being materially deprived, with increasing intensity. On the contrary, having a householder older than 65, a higher education and income reduces the chances of material deprivation. Regarding European Structural Funds, the regional resources with sustainable and quality employment weakly reduce or have no effect on the probability to fall into material deprivation. Those with social inclusion purpose, on the other hand, are strongly associated to a lower relative risk ratio of material deprivation of any kind. However, there is some regional misalignment regarding this result. Therefore, we suggest to look at the predicted relative risks for households of falling into some level of material deprivation as a parallel criterion to allocate European regional funds, beyond the level of development and the unemployment rates. We call for a better use of Structural and Cohesion Funds based on a “placed based” strategy.
Layout Embedding via Combinatorial Optimization

We consider the problem of injectively embedding a given graph connectivity (a layout) into a target surface. Starting from prescribed positions of layout vertices, the task is to embed all layout edges as intersection‐free paths on the surface. Besides merely geometric choices (the shape of paths) this problem is especially challenging due to its topological degrees of freedom (how to route paths around layout vertices). The problem is typically addressed through a sequence of shortest path insertions, ordered by a greedy heuristic. Such insertion sequences are not guaranteed to be optimal: Early path insertions can potentially force later paths into unexpected homotopy classes. We show how common greedy methods can easily produce embeddings of dramatically bad quality, rendering such methods unsuitable for automatic processing pipelines. Instead, we strive to find the optimal order of insertions, i.e. the one that minimizes the total path length of the embedding. We demonstrate that, despite the vast combinatorial solution space, this problem can be effectively solved on simply‐connected domains via a custom‐tailored branch‐and‐bound strategy. This enables directly using the resulting embeddings in downstream applications which cannot recover from initializations in a wrong homotopy class. We demonstrate the robustness of our method on a shape dataset by embedding a common template layout per category, and show applications in quad meshing and inter‐surface mapping.
Long non-coding RNA FAM83H-AS1 induced by adipose-derived stem cells promotes breast and pancreatic cancer cell proliferation and migration

Background Stromal cells recruited to the tumor microenvironment and long non-coding RNAs (lncRNAs) in the tumor cells regulate cancer progression. However, their relationship is largely unknown. Methods In the current study, we identified the effects of lncRNA FAM83H-AS1, induced by adipose-derived stem cells (ADSCs) during tumor development, and explored the underlying mechanisms using a coculture cell model. Adipose tissues were obtained from healthy female donors, the expression of stromal markers on cell surface of expanded ADSCs were confirmed using immunofluorescence analysis. The breast and pancreatic cancer cells were cultured with or without ADSCs using 24-well transwell chamber systems with 8.0 µm pore size. Results Our results showed that FAM83H-AS1 was upregulated in breast and pancreatic cancers and associated with poor prognosis. ADSCs further induced FAM83H-AS1 and increased tumor cell proliferation via promoting G1/S transition through cyclin D1, CDK4 and CDK6. Wound healing, modified Boyden chamber and immunoblotting assays demonstrated that ADSCs induced epithelial-mesenchymal transition and migration of breast and pancreatic cancer cells in a FAM83H-AS1-dependent manner. And ADSC-induced FAM83H-AS1 increased unfolded protein response through AKT/XBP1 pathway. Conclusion In conclusion, our results indicated that ADSCs promoted breast and pancreatic cancer development via inducing cell proliferation and migration, as well as unfolded protein response through FAM83H-AS1.
Effect of Sodium Silicate on Flocculation of Hematite with Starch in the Presence of Calcium

Sodium silicate is used as a dispersant in the selective flocculation of iron ores with starch as the polymeric floccullant. The mechanism of its dispersive effect on quartz has been reported to be related to its ability to remove coagulants, such as calcium and magnesium ions, from the process water. The interaction between iron oxides and calcium and sodium silicate, which has not appeared in literature, is also an important factor that would contribute to the success of the selective flocculation process. To elucidate the nature of this interaction at pH 11, where selective flocculation of iron oxides is commonly carried out, the effect of calcium and sodium silicate on the flocculation–selective flocculation behavior of hematite was investigated. Abstraction of calcium and adsorption of starch by hematite at different concentrations of sodium silicate and calcium were determined. It was found that calcium silicate precipitates as well as sodium silicate prevented starch adsorption and, therefore, sodium silicate could only be used effectively below 10 mg/L calcium and 1000 mg/L SiO2 concentrations while dispersing a hematite–quartz mixture.
M&A, Uncertainty, and Bargaining Power: Evidence from the German Retail Sector

Market concentration has been suggested as an enhancer of bargaining power imbalances for vertical commercial relationships. However, the empirical literature has not yet explored in which way this market concentration, as a result of -for instancea M&A operations, could a ect the negotiations with agents in vertical related markets; in particular, in frictional multiproduct commercial relationships, in which uncertainty may play a role of such negotiations. The present work proposes an explanation to this matter, by analyzing the strategic incentives and uncertainties that arise in this kind of commercial relationships from the announcement of an horizontal M&A operation, and the way these expectations could in uence the bargaining power redistribution among players after the operation; opening the discussion on a dynamic analysis of bargaining outcomes.
Unsupervised zero-shot classification of Finnish documents using pre-trained language models

In modern Natural Language Processing, document categorisation tasks can achieve success rates of over 95% using fine-tuned neural network models. However, so-called “zero-shot” situations, where specific training data is not available, are researched much less frequently. The objective of this thesis is to investigate how pre-trained Finnish language models fare when classifying documents in a completely unsupervised way: by relying only on their general “knowledge of the world” obtained during training, without using any additional data. Two datasets are created expressly for this study, since labelled and openly available datasets in Finnish are very uncommon: one is built using around 5k news articles from Yle, the Finnish Broacasting Company, and the other, 100 pieces of Finnish legislation obtained from the Semantic Finlex data service. Several language representation models are built, based on the vector space model, by combining modular elements: different kinds of textual representations for documents and category labels, different algorithms that transform these representations into vectors (TF-IDF, Annif, fastText, LASER, FinBERT, S-BERT), different similarity measures and post-processing techniques (such as SVD and ensemble models). This approach allows for a variety of models to be tested. The combination of Annif for extracting keywords and fastText for producing word embeddings out of them achieves F1 scores of 0.64 on the Finlex dataset and 0.73-0.74 on the Yle datasets. Model ensembles are able to raise these figures by up to three percentage points. SVD can bring these numbers to 0.7 and 0.74-0.75 respectively, but these gains are not necessarily reproducible on unseen data. These results are distant from the ones obtained from state-of-the-art supervised models, but this is a method that is flexible, can be quickly deployed and, most importantly, do not depend on labelled data, which can be slow and expensive to make. A reliable way to set the input parameter for SVD would be an important next step for the work done in this thesis. Acknowledgements This project was made possible by funding from the Finnish Ministry of Justice within the Anoppi project1 in partnership with the Semantic Computing Research Group (SeCo)2 at the University of Helsinki (HELDIG -Helsinki Centre for Digital Humanities) andAalto University. I would like to offer my special thanks to Yves Scherer from the University of Helsinki for his invaluable comments; Aki Hietanen and Tiina Husso, from the Ministry of Justice, and Eero Hyvönen, Jouni Tuominen, and the rest of the SeCo crew for their support. 1Automatic anonymisation and content description of documents containing personal data. https://oikeusmi nisterio.fi/en/project? tunnus=OM042:00/2018 2https://seco.cs. aalto.fi
Modification of the Roothaan equations to exclude BSSE from molecular interaction calculations

The Roothaan equations have been modified to compute molecular interactions between weakly bonded systems at the SCF level of theory without the basis set superposition error (BSSE). The increase in complication with respect to the usual SCF algorithm is negligible. Calculation of the SCF energy on large systems, such as nucleic acid pairs, does not pose any computational problem. At the same time, it is shown that a modest change in basis-set quality from 3-21G to 6-31G changes the binding energy by about 50% when computed according to standard SCF “supermolecule” techniques, while remaining practically constant when computed without introducing BSSE. Bader analysis shows that the amount of charge transferred between the interacting units is of the same order of magnitude when performed on standard SCF wave functions and those computed using the new method. The large difference between the corresponding computed energies is thus ascribed to the BSSE. © 1996 John Wiley & Sons, Inc.
The Library of Distributed Protocols

Distributed computing aims to solve computational problems using multiple hardware and software components connected over a network and potentially geographically distant. The management of distributed systems is really challenging because processes must achieve some form of cooperation despite the fact that some of them may fail or be disconnected. In this work, we also look at Byzantine processes that can behave in an arbitrary manner, either intentionally or because of a bug, thus interfering with the proper execution of an algorithm. In order to deal with such processes, some cryptographic abstractions are integrated in the design of the algorithms. We implemented a library of distributed algorithms in a modular way, focusing on the creation of programming abstractions interacting together. The development starts from the ground up with the abstraction of processes and communication links, and then gradually progresses to various broadcast versions or the well-known consensus problem. The technology used is DistAlgo, a high-level programming language very close to pseudo-code. Its ease of use as well as its complex synchronization conditions make it a serious contender for the implementation of distributed algorithms.
Stereocontrolled syntheses of α-C-mannosyltryptophan and its analogues

The total synthesis of α-C-mannosyltryptophan (C-Man-Trp), a naturally occurring C-glycosylamino acid, was achieved from a commercially available α-methyl-D-mannoside in 10 steps including the following key steps: the C-glycosidation of a mannose derivative with a stannylacetylene, Castro indole synthesis, and Sc(ClO4)3-promoted coupling with L-serine-derived aziridine carboxylate. The glucose- and galactose-analogues of C-Man-Trp were also synthesized in a similar manner. Conformational analyses of the synthesized C-glycosyltryptophan and its synthetic intermediate are briefly discussed.
A Clustering-Based Approach for Features Extraction in Spectro-Temporal Domain Using Artificial Neural Network

In this paper, a new feature extraction method is presented based on spectro-temporal representation of speech signal for phoneme classification. In the proposed method, an artificial neural network approach is used to cluster spectro-temporal domain. Self-organizing map artificial neural network (SOM) was applied to clustering of features space. Scale, rate and frequency were used as spatial information of each point and the magnitude component was used as similarity attribute in clustering algorithm. Three mechanisms were considered to select attributes in spectro-temporal features space. Spatial information of clusters, the magnitude component of samples in spectro-temporal domain and the average of the amplitude components of each cluster points were considered as secondary features. The proposed features vectors were used for phonemes classification. The results demonstrate that a significant improvement is obtained in classification rate of different sets of phonemes in comparison to previous clustering-based methods. The obtained results of new features indicate the system error is compensated in all vowels and consonants subsets in compare to weighted K-means clustering.
Derived Substantives in Word-Formation Nests of Auditory Perception Verbs: Ethnolinguistic Aspect

This paper aims to identify and describe the semantic and ethnocultural content of substantive derivatives extracted from the corresponding word-formation nests of the Russian and Tatar languages. The characteristics of the ways of lexicalization of related processes and features are essential for both lexical typology and ethnolinguistics. In the derivation-semantic space of auditory perception verbs, lexical units that have a peculiar semantic content are identified. These are first of all names of persons, specific objects, and abstract concepts, which their internal form and lexical meaning determine ethnocultural specificity. Comparison of derived substances that are directly or indirectly related to the process of auditory perception reveals certain conceptual areas and national-cultural meanings that form the language picture of the world and the mentality of each of the ethnic groups. It is proved that the ethnocultural component in the semantic structure of the studied words is also revealed within the same language: when comparing the diachronically marked and modern vocabulary. © 2021 IJSCL. All rights reserved. 1 PhD Candidate, Email: mahguzel652@mail.ru (Corresponding Author) Tel: +7-891-72933526 2 Professor, Email: favenera@mail.ru 3 Professor, Email: admin@riash.ru a Kazan Federal University, Russia b Moscow State Pedagogical University, Russia ARTICLE HISTORY: Received September 2020 Received in Revised form December 2020 Accepted December 2020 Available online January 2021
PacketMill: toward per-Core 100-Gbps networking

We present PacketMill, a system for optimizing software packet processing, which (i) introduces a new model to efficiently manage packet metadata and (ii) employs code-optimization techniques to better utilize commodity hardware. PacketMill grinds the whole packet processing stack, from the high-level network function configuration file to the low-level userspace network (specifically DPDK) drivers, to mitigate inefficiencies and produce a customized binary for a given network function. Our evaluation results show that PacketMill increases throughput (up to 36.4 Gbps -- 70%) & reduces latency (up to 101 us -- 28%) and enables nontrivial packet processing (e.g., router) at ~100 Gbps, when new packets arrive >10× faster than main memory access times, while using only one processing core.