Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical models of images and early vision

A fundamental question in visual neuroscience is: Why are the receptive ﬁelds and response properties of visual neurons as they are? A modern approach to this problem emphasizes the importance of adaptation to ecologically valid input. In this paper, we will review work on modelling statistical regularities in ecologically valid visual input (“natural images”) and the obtained functional explanation of the properties of visual neurons. A seminal statistical model for natural images was linear sparse coding which is equivalent to the model called independent component analysis (ICA). Linear features estimated by ICA resemble wavelets or Gabor functions, and provide a very good description of the properties of simple cells in the primary visual cortex. We have introduced extensions of ICA that are based on modelling dependencies of the ”independent” components estimated by basic ICA. The dependencies of the components are used to deﬁne either a grouping or a topographic order between the components. With natural image data, these models lead to emergence of further properties of visual neurons: the to-pographic organization and complex cell receptive ﬁelds. We have also modelled the temporal structure of natural image sequences, which provides an alternative approach to the sparseness used in most models. These models can be combined in a unifying framework that we call bubble coding. Finally, we will discuss a promising new direction of research: predictive visual neuroscience. There, the goal is to try to predict response properties of neurons in areas that are poorly understood, still based on statistical modelling of natural input.
Green Supplier Evaluation and Selection with an Extended MABAC Method Under the Heterogeneous Information Environment

With the increasing awareness of global environmental protection, green production has become a significant part for enterprises to remain in a competitive position. For a manufacturing company, selecting the most suitable green supplier plays an important role in enhancing its green production performance. In this paper, we develop a new green supplier evaluation and selection model through the combination of heterogeneous criteria information and an extended multi-attributive border approximation area comparison (MABAC) method. Considering the complexity of decision context, heterogeneous information, including real numbers, interval numbers, trapezoidal fuzzy numbers, and linguistic hesitant fuzzy sets, is utilized to evaluate alternative suppliers with respect to the selected criteria. A maximizing consensus approach is constructed to determine the weight of each decision-maker based on incomplete weighting information. Then, the classical MABAC method is modified for ranking candidate green suppliers under the heterogeneous information environment. Finally, the developed green supplier selection model is applied in a case study from the automobile industry to illustrate its practicability and efficiency.
A transposon-mediated germline transformation system for the honeybee Apis mellifera

17 Introduction . 18 Experimental Procedures . 21 Cloning of putative promoter sequences . 21 Cell culture . 22 Expression analysis in Sf21 cells . 22 Expression analysis under temperature shift conditions . 22 Electroporation . 23 Results . 24 Honeybee derived promoter sequences direct gene expression in Sf21 cells . 24 The Am hsp83 promoter derived expression is enhanced by temperature shift . 28 The elp2l promoter sequence can direct gene expression in the honeybee brain . 30 Discussion . 33 Acknowledgments . 37 Supplementary information . 38 Author ́s Contribution . 44 Manuscript II . 45 Genetic transformation of honeybees (Apis mellifera) via transposon mediated integration . 45 Abstract . 46.46 Introduction . 47 Experimental Procedures . 49 DNA preparation, PCR and sequencing . 49 ____________________________________________________________________Inhaltsverzeichnis IV Cloning of marker genes into the vector . 49 Microinjection and mRNA synthesis . 50 Fluorescence detection in drone heads . 51 PCR-based identification of integration sites . 51 Results . 52 Germline transformed honeybee queens . 52 Targeted gene expression driven by the honeybee promoter Am-actin5c . 57 Discussion . 59 Acknowledgements . 63 Supplementary Information . 64 Author ́s Contribution . 68 III Zusammenfassung . 69 IV Summary . 70 V References . 71 Danksagung . 79 __________________________________________________________________________Einleitung
Predicting Cosmological Massive Neutrino Simulation with Convolutional Neural Networks

Neutrinos are elusive subatomic particles that play an important role in the evolution of the Universe. Despite they are the second most abundant particle of the Universe, their properties remain still unknown. Cosmological observations provide a powerful probe of neutrino properties, and in particular of their mass, Mν . In order to maximize the information that can be retrieved from those observations, accurate theoretical predictions are needed. Currently, simulations are the best tool to provide those. In this work, we propose a new method, based on a deep learning network, to quickly generate cosmological simulations with massive neutrinos from standard simulations without neutrinos. We show that our approach can predict accurately cosmological simulations and can generate new massive neutrino simulations 10,000 times faster than traditional methods.
Evaluation of bottle gourd [Lagenaria siceraria (Mol.) Standl.] genotypes during post-rainy season in Red and Laterite Zone of West Bengal

Twenty seven bottle gourd genotypes were assessed with the objective to find out promising genotype(s) for growing in postrainy season under red and laterite zone of West Bengal. Among the genotypes significant variations were noted for all the studied traits. Genotypes APBG-3 and UKBG-1 were early to first fruit harvest. Average fruit number plant-1 and average fruit weight was noted 3.4 and 775.8 g respectively. Genotypes Kundan, Gola Lattoo, UKBG-1, PSPL and Aditi exhibited higher fruit yield plant-1 which varied from 3.6 to 4.0 kg, can be recommended for growing in red and laterite zone of West Bengal.
Securing IoT-based collaborative applications using a new compressed and distributed MIKEY mode

: Multimedia internet keying protocol (MIKEY) aims at establishing secure credentials between two communicating entities. However, existing MIKEY modes fail to meet the requirements of low-power and low-processing devices. To address this issue, we combine two previously proposed approaches to introduce a new compressed and distributed MIKEY mode applied to a collaborative internet of things context. A set of third parties is used to discharge the constrained nodes from heavy computational operations. Doing so, the MIKEY pre-shared mode is used in the constrained part of network, while the public key mode is used in the unconstrained part of the network. Furthermore, to mitigate the communication cost we introduce a new header compression scheme that reduces the size of MIKEY’s header from 12 bytes to 3 bytes in the best compression case. To assess our approach, we performed a detailed security analysis using a formal validation tool (i.e., Avispa). In addition, we performed an energy evaluation of both communicational and computational costs. The obtained results show that our proposed mode is energy preserving whereas its security properties are preserved untouched.
Learning Renormalization with a Convolutional Neural Network

Renormalization is a technique for studying the scale-dependence of correlations in physical systems through iterative coarse-grainings over longer and longer distance scales. The similarity of this procedure to the iterative layer-by-layer abstraction that occurs in deep learning models has motivated applications of machine learning to modeling physical systems, in particular the Ising model, and has also led to speculation that viewing deep learning through the lens of renormalization may be able to explain some of the effectiveness of deep neural networks in other domains. In this work, we demonstrate a method to find an optimal coarse-graining of the 2D Ising model by training a feedforward convolutional neural network (CNN) with a simple classification objective that incentivizes the network to learn long-distance correlations. We comment on the information theory interpretation of this method and its possible applications to a wider variety of machine learning problems.
Technological Drivers of Urban Innovation: A T-DNA Analysis Based on US Patent Data

Fast urbanization leads to several challenges in many cities all over the world. Thus, urban innovation is considered a common approach to deal with such questions. Although technologies are important factors in urban innovation, the development of technologies over time, how they affect urban innovation, in which relationship they stand to each other, and how they can be evaluated in a system approach are still not clear. To answer these questions, in our study, a Technology-DNA (T-DNA) is applied to US patents, which represent the most developed market in the world. Our paper provides some theoretical points in urban innovation and a systematic classification of technologies in this field based on patent classes. In addition, this research shows technological drivers in different system levels in urban innovation, especially in the super-system (representing city infrastructures) in detail. Therefore, it may help researchers, managers, politicians, and planners to focus on important technologies and to integrate technological drivers in urban innovation in their plans.
Physically active lessons in secondary school: an intervention Study

Background: This article describes an intervention study examining the effect of physically active lessons on both educational outcomes and the teachers’ perspective on integrating PA into academic lessons in secondary school. Methods: A 12-week physically active lessons program using the MIT-approach, Movement Integrated into Teaching. The study outcome was 1) change in math and reading performance 2) the teachers motivation for implementing PA in academic lessons focusing on barriers and opportunities, collected through group interviews with teachers. Results: The study showed no effect on reading and math scores. However, reading improvements in the intervention group was higher than in the control group, but due to differences in baseline scores, a linear regression showed a ceiling effect. The physically active lessons engage and motivate more students, and it increases the students’ on-task behavior. Integrating PA in the lessons is a challenge, because of the time pressure and strong curriculum demands in secondary school. The teachers see the MIT-approach as a more systematic way of using physically active lessons making the teaching more active and varied. Discussion: The results of the study are expected to provide schools and policy-makers with new insights into the potential of physically active lessons in secondary school.
Rapid CO2 Release From Eroding Permafrost in Seawater

Permafrost is thawing extensively due to climate warming. When permafrost thaws, previously frozen organic carbon (OC) is converted into carbon dioxide (CO2) or methane, leading to further warming. This process is included in models as gradual deepening of the seasonal non‐frozen layer. Yet, models neglect abrupt OC mobilization along rapidly eroding Arctic coastlines. We mimicked erosion in an experiment by incubating permafrost with seawater for an average Arctic open‐water season. We found that CO2 production from permafrost OC is as efficient in seawater as without. For each gram (dry weight) of eroding permafrost, up to 4.3 ± 1.0 mg CO2 will be released and 6.2 ± 1.2% of initial OC mineralized at 4 °C. Our results indicate that potentially large amounts of CO2 are produced along eroding permafrost coastlines, onshore and within nearshore waters. We conclude that coastal erosion could play an important role in carbon cycling and the climate system.
Structural, Functional and Kinetic Studies of the Recombinant YjeA Endonuclease of Bacillus subtilis

The low transformation efficiency of chimeric plasmid DNA in Bacillus subtilis has been hindering the successful application of this organism as an alternative cloning and secretory system to E. coli. Our laboratory has previously cloned a novel DNase gene, designated yjeA, from B. subtilis. The recombinant YjeA was shown to be an endonuclease, which cleaved pBR322 and hampered the transformation of the plasmid into E. coli cells. The current study focuses on the characterization of the functional and structural domains of the YjeA protein. To identify the functionally important region(s) of the protein, deletion mutagenesis was employed to generate YjeA mutants, of which regions located in the N-terminus, the C-terminus, or the middle part of the protein had been deleted. The C-terminal region of YjeA appeared to be crucial for both of activity and stability. Moreover, mutants with deletions of 49a.a. (184-232) and 30a.a. (283-312) in the mid-region of YjeA were also shown to be DNase-negative, suggesting that these regions were also functionally important. Further study showed that over-expression of YjeA induces host cell stress responses. The harmful effects observed included slow growth rate, small colony morphology and reduced plasmid stability. Despite the susceptibility of YjeA to proteolysis in E. coli JM101, expression of it under the control of the lacUV5 promoter carried on a multi-copy plasmid, pWKW2, was 2-fold better than that expressed employing the pACYC184-pR promoter system. Kinetic studies have showed similarities between YjeA and DNase I. The hydrolytic activity of YjeA was found to be activated by Mg, and reinforced by the presence of Ca; moreover, divalent transition metal ions such as Co and Mn were also shown to be potent activators of YjeA. One unit of the recombinant YjeA-H enzyme was defined as the amount of enzyme that hydrolyzes 0. 1μg/μl of pBR322 CCC DNA in a 30ul reaction per minute at 37°C. The Km and Vmax were 10. 7μg/μl and 2.18U/min, respectively, and the specific activity of YjeA-H is 9.95U/μg protein.
Economic Analysis of Green Mung Bean in Selected Area, Myanmar

As mung bean is one of the income remunerative crops in Myanmar, this study was focus on economic analysis and market performance of green mung bean producers and market intermediaries along the supply chain in selected areas of Nay Pyi Taw and related market regions. The objectives are to understand the existing participants, marketing activities, and distribution channel in the marketing system of mung bean and to analyze the cost and profitability of mungbean producing farmers and market participants along the channel in the study area. Main marketing actors are farmers, village collector, local wholesalers, and wholesalers (exporter) in Mandalay and Yangon markets. Marketing activities analysis showed farmers still have lower standard of grading and standardizing and also limited source of market information as compared to other market participants been indication to upgrade an efficient market information system to all. In the marketing channel, farmers preferred to sell local wholesalers and only very few farmers sold to village collector and most of the farmers in the study areas have directly contact with the local (townships) wholesalers. Local wholesalers traded their goods to the main agents such as Mandalay and Yangon wholesalers/exporters. In the results of profitability analysis, Farmers got the benefit cost ratio of 1.97 in mung bean production in the study area implied that for every 1.00 MMK invested in mung bean production in the area, 0.97 MMK was returned, thus further confirming the profitability of the enterprise. Among the market intermediaries the calculated absolute marketing margins for exporters to some Asia and EU markets obtained the higher margin than the other market participants.
HIGAN: Cosmic Neutral Hydrogen with GANs

In this work we use a Wasserstein Generative Adversarial Network (WGAN) to generate new high-resolution 3D realizations of cosmic neutral Hydrogen (HI). The generator produces samples that match closely the fully non-linear abundance and clustering properties of cosmic HI from the state-of-the-art simulation IllustrisTNG. We show that different statistical properties of the generated samples – 1D PDF, power spectrum, bispectrum, and void size function – match very well to those of IllustrisTNG, and outperform state-of-the-art models such as Halo Occupation Distributions (HODs). Our WGAN samples reproduce the abundance of HI across 9 orders of magnitude, from the Lyα forest to Damped Lyman Absorbers.
Efficacy Study of Anti-Endomysium Antibodies for Celiac Disease Diagnosis: A Retrospective Study in a Spanish Pediatric Population

The aim of this study was to assess the efficacy of anti-endomysium antibodies (EMA) as a serological marker for celiac disease (CD) diagnosis in a pediatric population. A retrospective study of pediatric patients who underwent a CD serological markers study: EMA and anti-tissue transglutaminase antibodies (anti-TG2). Clinical symptomatology, degree of histological lesion, human leukocyte antigen (HLA) haplotype compatible with CD (HLA DQ2 and/or DQ8), and final diagnosis were taken into account. We included 445 patients who were classified in two groups according to the final diagnosis. Group 1: 232 children with CD, 91.4% of whom exhibited small intestinal villous atrophy, 228 being EMA-positive and four EMA-negative. Group 2: 213 children with a non-CD diagnosis, 212 EMA negative and one EMA positive. Both antibodies, EMA and anti-TG2, reached similar sensitivities, 98% and 99% respectively, while EMA had a higher specificity (99%) than anti-TG2 (93%). By using both markers combined, compared to using anti-TG2 alone, 5.7% of patients are better diagnosed. However, when we compare the efficacy of EMA and anti-TG2 in asymptomatic and symptomatic patients, the sensitivity of EMA is 98% irrespective of symptoms, thus higher than for anti-TG2 ≥10 × upper limit of normal (ULN) (respectively 77% and 84%). Our results support the use of EMA to increase CD diagnostic accuracy in a non-biopsy approach, especially in asymptomatic children.
Identifying chemically identical stars using adverserial disentanglement

As the chemistry of stars is interlinked with their birthsites, identifying stars with identical chemical abundances is a promising avenue for unravelling the history of our galaxy. We present a method for precisely identifying chemically identical stars from spectroscopic observations. In our approach, a neural network is trained to learn a latent which is disentangled from non-chemical factors of variation. This allows for identifying chemically identical stars without any reliance on chemical models.
Rates of High-Temperature Evaporation of Promising Fire-Extinguishing Liquid Droplets

Differences in the rates of heating and evaporation of droplets with the component composition are important parameters of heat transfer processes and phase transformations. This paper presents the values of high-temperature (up to 600 ◦C) evaporation rates of droplets of promising fire-extinguishing compositions (water, bentonite suspension, bischofite solution, EA-5 solution, and foaming agent emulsion) at convective (in the air stream), conductive (on a heated surface), and radiation (in a muffle furnace) heating. A high-speed video recording system and tracking software algorithms are used. At identical initial sizes of droplets of fire-extinguishing suspensions, known as emulsions and solutions, the times of their complete evaporation are shown to differ 3.7 times when heating on the substrate, 1.25 times in the air flow, and 1.9 times in the muffle furnace. A general approximation expression is formulated, and the empirical constants are calculated to predict the evaporation rate of the droplets of extinguishing agents in a wide range of temperatures (up to 600 ◦C) and heat fluxes (up to 100 kW/m2), which are characteristic of forest fires. With the use of the experimental data obtained, it is possible to predict the completeness of evaporation of promising extinguishing liquids at different schemes of heat supply.
Selective or random panel dropout? An investigation of personality and relationship parameters

In social sciences, longitudinal studies represent a common form of study design. However, this form of data collection may face the special problem of panel attrition over subsequent waves – a circumstance that can lead to a biased sample. As a consequence, longitudinal analyses may become flawed if the bias is unknown or not corrected. Previous research on panel attrition revealed that dropout can be related to several specific characteristics, such as sex, personality, or relationship status of the participants. As twins are a core feature of twin-studies – twin similarity a key tool in behavioral genetics – relational characteristics of this special siblinghood may play a crucial role in re-participating, too. Therefore, the aim of the present study was to investigate panel attrition with respect to certain personality traits (Big Five) and relational characteristics (sibling relationship and satisfaction with sibling relationship) to estimate potential biases as a basis for corrections in future studies. Analyses were not only performed on an individual level but also regarding twin pair resemblance as this is the main source of information in behavior genetic twin studies. The sample consisted of 4,097 twin pairs and 8,281 of their family members from wave one of the German TwinLife study. 60.9 % of the initial sample re-participated in wave 2. Results indicate that there are no consistent patterns in personality or relational characteristics across family members, age cohorts of the twins, and sexes regarding panel attrition. There also seem to be no consistent patterns regarding differences in twin similarity across reand non-re-participators. Subsequently, selective panel dropout with respect to personal characteristics seems to be of minor importance in the TwinLife study and corrections may only be necessary in selective cases. ■
Analyzing age-sex structure patterns in Nepal using factor analysis

This study aimed to cluster the districts in Nepal based on the patterns of age-sex structures by applying factor analysis. The population data, which is grouped by 5-year age gap, by sex and by districts, was used. The factor analysis was applied to spline smooth single-year age population by sex and district. A three factor model was best fitted to the data. These three common factors were interpreted as three different patterns based on common characteristics of age and sex distribution. The study found that 23, 17 and 5 districts correlated purely to factor 1, 2 and 3, respectively. Thirty districts were found correlated with two or more factors. In conclusion, the age-sex structure varied substantially between the different districts of Nepal in 2011. The variations were explained well by a three-factor model. The method used in this study is straightforward and applicable to the further demographic study.
Model Parameter Optimization: ML-guided trans-resolution tuning of physical models

This paper proposes a novel method to automate and enhance the process of tuning physically-based numerical models. The Model Parameter Optimization (MPO) method learns from previous executions of numerical models to inform future executions. Specifically, MPO utilizes machine learning to construct a mapping between the free-parameter spaces of low-resolution models and high-resolution models, enabling users to tune their high-resolution models by executing much cheaper low-resolution models. We validate the MPO method by applying it to Modular Ocean Model 6 (MOM6)[7], and show that it achieves competitive tuning results.