Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures

Formation of a hierarchy within an organization is a natural way of assigning the duties, delegating responsibilities and optimizing the flow of information. Only for the smallest companies the lack of the hierarchy, that is, a flat one, is possible. Yet, if they grow, the introduction of a hierarchy is inevitable. Most often, its existence results in different nature of the tasks and duties of its members located at various organizational levels or in distant parts of it. On the other hand, employees often send dozens of emails each day, and by doing so, and also by being engaged in other activities, they naturally form an informal social network where nodes are individuals and edges are the actions linking them. At first, such a social network seems distinct from the organizational one. However, the analysis of this network may lead to reproducing the organizational hierarchy of companies. This is due to the fact that that people holding a similar position in the hierarchy possibly share also a similar way of behaving and communicating attributed to their role. The key concept of this work is to evaluate how well social network measures when combined with other features gained from the feature engineering align with the classification of the members of organizational social network. As a technique for answering this research question, machine learning apparatus was employed. Here, for the classification task, Decision Trees, Random Forest, Neural Networks and Support Vector Machines have been evaluated, as well as a collective classification algorithm, which is also proposed in this paper. The used approach allowed to compare how traditional methods of machine learning classification, while supported by social network analysis, performed in comparison to a typical graph algorithm. The results demonstrate that the social network built using the metadata on communication highly exposes the organizational structure.
Model recommendation for action recognition and other applications

The typical approach to learning based vision has been that for each individual application, classifiers or detectors are learned anew from annotated training data for each specific task. However, the classifiers trained in this way tend to be brittle and highly specialized to the datasets from which they are derived, making them difficult to transfer between tasks. While multi-task learning and domain adaption techniques address some of these problems on a theoretical level, from a practical standpoint they are just as complicated and labor-intensive as the simpler learning techniques they supplant. However, suppose that these specialized classifiers had simply been collected into a library: while it is unlikely that any specific classifier would generalize well to a new dataset, there may exist some classifier in the library that is tuned to the same conditions as the new task. This thesis addresses the fundamental question of how to efficiently select a good classifier from such a library. Specifically, this thesis demonstrates that collaborative filtering techniques (such as employed by recommender systems like Netflix and Amazon.com) can be used to recommend models appropriate for a specific target task. These recommendations are made by trying, or rating, a small subset of models on the target task, and then using that small set of ratings along with the ratings of the models on other tasks to predict the ratings of the unevaluated models on the target task. This process, which we term "model recommendation", is applied to action recognition and other vision and robotics applications, and the subtle differences between model recommendation and typical recommender systems are used to derive novel algorithms and extensions to the core recommendation concept.
Back-and Forecasting Freight Transport CO 2-emissions : Decomposition from a Growth Cycle Perspective

One of today‟s major sources of greenhouse gas emissions is the transport sector. According to the latest assessment from the IPCC, transportation accounted for 23% of all energyrelated greenhouse gas emissions in 2004 (IPCC 2007) having grown by 26% since 1990 (for EU-25). Growth in transport emissions is caused by several factors. Historically, transport volumes have grown with economic development (Tapio 2005, Eurostat 2007) and some therefore argue transport emissions as something we might have to accept in order to achieve desired economic development. Many studies, however, have shown that a decoupling between freight transport work (ton km) and economic activity, as well as freight traffic work (vehicle km) and economic activity, is possible and also feasible (Tapio 2005, Kveiborg and Fosgerau 2007). Nonetheless, there is still a lack of understanding of the complicated interrelation between economic development and freight transport emissions. This paper seeks to investigate this further and extend previous models to explicitly address long term structural shifts in the economy and its effect on the emissions from all transport modes, as observed thus far, to explain the observed development and predict emissions levels for the next ten years. The issue is addressed through a Divisia Index decomposition method, similar to that of, for example, Lashkmanan and Han (1997). The observed increase in freight transport CO2Backand Forecasting Freight Transport CO2-emissions: Decomposition from a Growth Cycle Perspective ENG LARSSON, Fredrik; OLANDER, Lars-Olof; LUNDQUIST, Karl-Johan; WANDEL, Sten 2 emissions is caused by several factors, and by using a decomposition model we can calculate the relative contributions of each factor over time (Ang and Zhang 2000). Some of these factors are primarily behavior driven whereas others are technology driven. By doing a decomposition analysis, we can extrapolate likely development into a number of scenarios with different levels of behavioral and technological development. The findings are line with the patterns predicted by economic growth cycle theories. The Swedish 31% growth in freight transport CO2-emissions 1990-2008 is primarily driven by the economic development. Structural shifts within the economy causes the tonkms/GDP to decrease over the period and due to a number of other rationalizing efforts among the firms within the economy, this positive effect is even greater for the freight transport CO2 emissions per tonkm. The greatest positive effect is due to organizational improvements reducing the traffic intensity. Also, the increased use of less carbon-intensive fuel sources contributes significantly. Based on the growth cycle theories, the observed development is extrapolated as to predict outcomes for the coming years. Through such an exercise it is shown that in a “business-asusual” scenario, freight transport CO2-emissions are likely to decrease by some 10% over the coming ten year period.
Nutritional and functional properties of popped little millet (Panicum sumatrense)

Food industries are focusing energies towards the development of functional foods and food ingredients. Several ancient grains are being used as a source of functional nutrients. Millets are minor cereals which have high nutritional value, are non-glutinous and are easily digestible. In spite of this, their consumption is limited. This could be attributed to their non-availability in ready-to-eat and ready-to-use foods. Processing of millets to incorporate them in ready-to-eat foods can increase their nutritional value, availability and economic value. Thermal processing can improve the bioavailability of certain vitamins and minerals and can also help in lowering the water activity thus, preventing the growth of microorganisms. Thermally processed foods also have better organoleptic properties. One interesting method of thermal processing is popping. Popping enhances the carbohydrate and protein digestibility by inactivating some of the enzymes and enzyme inhibitors. Popping also improves the color, appearance, aroma and taste of the processed food commodity. In the present study, the popping quality of little millet (Panicum sumatrense) and the effects of popping on the nutrient composition and the functional properties of the millet were studied. The popping quality of little millet was optimized with respect to the temperature of the particulate medium and the moisture content of the millet, both of which were found to determine the yield of popping. The optimized conditions for popping little millet were obtained at 16% grain moisture and particulate medium temperature of 260°C. The total protein, crude fat and total ash content of the popped millet was almost equal to that of the native millet. Popping increased the non-resistant starch content of little millet. The availability of total phenolics increased from 225 mg GAE/100g sample (db) in native millet to 661.462 mg GAE/100g sample (db) in popped millet. Popped millet flour (PMF) had a higher oil absorption capacity at room temperature as well as at 140°C and also exhibited higher swelling power and solubility. While the cold paste viscosity of the native millet flour (NMF) was 5.359 X 10 -3 Pa s, that of PMF varied from 1.5 to 7.5 Pa s. NMF had a hot paste viscosity (HPV) of 0.1908 Pa s whereas the HPV of PMF varied from 1.9 to 7.5 Pa s. From the results obtained in the present
PHAGOCYTES, GRANULOCYTES, AND MYELOPOIESIS Discoidin domain receptor 2 regulates neutrophil chemotaxis in 3D collagen matrices

Key Points • DDR2 regulates the directional migration of neutrophils in 3D collagen matrices, but not on 2D surfaces. • DDR2 regulates directionality through increased metalloproteinase secretion and generation of collagen-derived chemotactic peptide gradients. Neutrophils express a variety of collagen receptors at their surface, yet their functional significance remains unclear. Although integrins are essential for neutrophil adhesion and migration on 2-dimensional (2D) surfaces, neutrophils can compensate for the absence of integrins in 3-dimensional (3D) lattices. In contrast, we demonstrate that the inhibition of the tyrosine-kinase collagen receptor discoidin domain receptor 2 (DDR2) has no impact on human primary neutrophil migration on 2D surfaces but is an important regulator of neutrophil chemotaxis in 3D collagen matrices. In this context, we show that DDR2 activation specifically regulates the directional migration of neutrophils in chemoattractant gradients. We further demonstrate that DDR2 regulates directionality through its ability to increase secretion of metalloproteinases and local generation of collagen-derived chemotactic peptide gradients. Our findings highlight the importance of collagen-derived extracellular signaling during neutrophil chemotaxis in 3D matrices. (Blood. 2013;121(9):1644-1650)
Colocalization of cortactin and PH domain of BCR in HEK293T cells and its potential role in cell signaling.

D. S. Gurianov, S. V. Antonenko, G. D. Telegeev © 2016 D. S. Gurianov et al.; Published by the Institute of Molecular Biology and Genetics, NAS of Ukraine on behalf of Biopolymers and Cell. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited UDC 577.25, 591.87, 611.44
ARCHES-Template for deliverables

for dissemination) This is a report about the current stage of development of the first Test Setup and the tools developed to reach this goal: The tactile relief generation software, the context-sensitive tactile audio guide, and the produced artworks. We show the progress we made towards reaching our milestones within WP 5, especially subtasks 5.1. 1 “Integration of prototypes”, 5.1. 2 “Extension of design tools”, 5.3. 1 “Improvement of touch and gesture recognition”, 5.3. 2. “Improvement, extension of user interaction methods & content authoring tool”, 5.4. 1 “Design and production of reliefs for evaluation” and 5.4. 3 “Creation of tactile audio guide for produced reliefs”. We also present the promising results of the first user studies within the participative research group and sum up the conclusions we drew from it.
Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection

Efficient model inference is an important and practical issue in the deployment of deep neural network on resource constraint platforms. Network quantization addresses this problem effectively by leveraging low-bit representation and arithmetic that could be conducted on dedicated embedded systems. In the previous works, the parameter bitwidth is set homogeneously and there is a trade-off between superior performance and aggressive compression. Actually the stacked network layers, which are generally regarded as hierarchical feature extractors, contribute diversely to the overall performance. For a well-trained neural network, the feature distributions of different categories differentiate gradually as the network propagates forward. Hence the capability requirement on the subsequent feature extractors is reduced. It indicates that the neurons in posterior layers could be assigned with lower bitwidth for quantized neural networks. Based on this observation, a simple but effective mixed-precision quantized neural network with progressively ecreasing bitwidth is proposed to improve the trade-off between accuracy and compression. Extensive experiments on typical network architectures and benchmark datasets demonstrate that the proposed method could achieve better or comparable results while reducing the memory space for quantized parameters by more than 30\% in comparison with the homogeneous counterparts. In addition, the results also demonstrate that the higher-precision bottom layers could boost the 1-bit network performance appreciably due to a better preservation of the original image information while the lower-precision posterior layers contribute to the regularization of $k-$bit networks.
A decennial review of psychotraumatology: what did we learn and where are we going?

ABSTRACT On 6 December 2019 we start the 10th year of the European Journal of Psychotraumatogy (EJPT), a full Open Access journal on psychotrauma. This editorial is part of a special issue celebrating the 10 years anniversary of the journal and acknowledging some of our most impactful articles of the past decade. In this editorial the editors present a decennial review of the field addressing a range of topics that are core to both the journal and to psychotraumatology as a discipline. These include neurobiological developments (genomics, neuroimaging and neuroendocrine research), forms of trauma exposure and impact across the lifespan, mass trauma and early interventions, work-related trauma, trauma in refugee populations, and the potential consequences of trauma such as PTSD or Complex PTSD, but also resilience. We address innovations in psychological, medication (enhanced) and technology-assisted treatments, mediators and moderators like social support and finally how new research methods help us to gain insights in symptom structures or to better predict symptom development or treatment success. We aimed to answer three questions 1. Where did we stand in 2010? 2. What did we learn in the past 10 years? 3. What are our knowledge gaps? We conclude with a number of recommendations concerning top priorities for the future direction of the field of psychotraumatology and correspondingly the journal. HIGHLIGHTS • Celebrating 10 years of the European Journal of Psychotraumatology the editors present a decennial review of core topics in the field and conclude with recommendations concerning top priorities for future research.
Memory of decisions: Relationship between decline of autobiographical memory and temporal discounting in Alzheimer’s disease

ABSTRACT Temporal discounting refers to the tendency to attribute higher value to a reward received early than to one received later. We evaluated this tendency in patients with Alzheimer’s Disease. We also evaluated whether temporal discounting is associated with decline in autobiographical memory (i.e., the ability to remember past personal experiences), with executive dysfunction, and/or with general cognitive decline. We invited patients with AD and control participants to answer binary questions involving the choice between receiving a smaller amount of money earlier or a larger one later (e.g., “Which do you prefer, 10 euros in cash right now or 50 euros in a month? ”). Results demonstrated higher temporal discounting in patients with AD than in control participants. Temporal discounting was significantly correlated with decline in AM and general cognitive decline but not with executive dysfunction in patients with AD. The tendency to decide based on immediate rewards (i.e., temporal discounting) in AD is related with difficulty in remembering information about experiences of previous decisions, and/or their consequences (i.e., decline in autobiographical memory).
Proteome and Transcriptional Analysis of Arabidopsis thaliana Sperm Cells

Angiosperm sexual reproduction is unique and remarkable. Unlike for other living organisms, fertilisation involves the fusion of two sperm cells to two cell types of female gametophyte, the egg and central cell. This fertilization process is called double fertilisation. Fusion of the egg and the sperm yields a zygote whereas the interaction of the central cell and the sperm gives the nutritive endosperm. This fertilization process has been studied extensively for many years. However, despite these studies, relatively little is known at the molecular level about either of the plant gametes. This is primarily due to the difficulties in plant gamete isolation. Both plant gametes reside within enclosed tissues, pollen grains and embryo sacs. In this study, sperm isolation techniques were successfully developed for Brassica oleracea and Arabidopsis thaliana which ultimately utilised fluorescence-activated cell sorting (FACS) to obtain pure cell samples. Proteomic studies utilising two-dimensional protein electrophoresis and mass spectrometry (MS) were carried out on both semi-purified and FACS-purified sperm cells. In parallel with the proteomic studies a bioinformatics approach was taken which used sperm transcriptome data of maize, Plumbago zeylanica, rice and tobacco to identify homologues in Arabidopsis. Transcriptional analyses, RT-PCR and GFP translational fusion experiments were used to investigate these Arabidopsis sperm cell-expressed gene candidates. As a result, two sperm cell-expressed genes (At1g10090 and At5g39650) were identified and these are being analysed to confirm their functions in the reproductive process. Moreover, the sperm cell-expressed gene candidates derived from the bioinformatics were also screened for roles in plant reproduction by a reverse genetics approach (Arabidopsis T-DNA insertion mutagenesis plant screening) and eight genes were identified. In addition, for the first time, sperm cell size dimorphism was identified for Arabidopsis in this study utilising a GFP-labelled sperm line and confocal microscopy. Overall the techniques for sperm cell-expressed gene candidate selection were proven to be effective and will certainly facilitate further sperm cell-specific gene identification studies. Further the Arabidopsis sperm purification technique successfully developed in this project will surely be useful for any plant sperm cell studies in the future.
Engineering of the extremely high Q factor in two subwavelength dielectric resonators

The high-Q {\it subwavelength} resonances in an isolated dielectric disk modes can be achieved by avoided crossing (anticrossing) of the nonorthogonal TE resonances under variation of the aspect ratio as it was reported by Rybin {\it et al} [Phys. Rev. Lett. 119, 243901 (2017)]. Traversing over two parameters, the aspect ratio and the distance between two disks, enhances the Q factor by several times compared to the case of the former case of one-parametric avoided crossing in the isolated disk. Therefore successive two-parametric avoided crossing gives multiplicative gain in the Q-factor as it was expected. However if for the single disk its orthogonal resonant modes do not undergo avoided crossing, a presence of the second disk gives removes this restriction and gives rise to the avoided crossing of these modes that enhances the $Q$ factor by two orders in magnitude compared to the case of single disk. Respectively the multipolar decomposition of the anti-bonding resonant mode demonstrates conversion from lower to higher orders of the multipole modes similar to that as shown by Chen {\it et al} [Laser&PhotonicsReviews 13, 1900067 (2019)]. These results are interpreted by that the field configuration at the maximal conversion becomes close to the Mie resonant mode with high orbital momentum in equivalent sphere. For m=1 the resonant modes leakage into both type continua that substantially lowers the Q-factor of the subwavelength resonant modes.
A Novel Method of Moving Object Detection

---------------------------------- Abstract: Visual background extractor (Vibe), a universal background subtraction algorithm, which has the characteristics of fast operation, high accuracy and wide application range. But there are also difficulties in eliminating the "ghost" and adapting to the light mutation, in this paper, an improved Vibe algorithm is proposed. It draws on the W 4 theory and the mean value method in establishing the background model to suppress ghosts and some shadows, the new background model of Vibe was created by adding light mutation criterion to the motion detection algorithm, so that it could adapt to the light mutation rapidly, the experimental results show that the proposed method have good performance in removing interfaces as ghost shadow, when any sudden light change of circumstance occurs, the algorithm can also detect and update pixels statistical parameters adaptively.