Miracle-dz/UniFAR-FTUs huggingface dataset:
Pattern Decomposition of Inorganic Materials: Optimizing Computational Algorithm

Phase pattern decomposition of inorganic materials’ crystalline structure is extremely important for the unearthing of new properties such as superconductivity. Previously, this process had meticulously been done by hand, so computer algorithms have been developed to try and uncover these phases. They, however, have yet to combine eﬃciency and accuracy together. The goal of this project is to do just that by extending the Graph-based endmember extraction and labeling algorithm (GREN-DEL). Phase one will be to incorporate physical constraints and prior knowledge to increase the accuracy of our phase composition results, and phase two will be to utilize active learning to minimize the number of sample points needed to analyze a given material to increase eﬃciency.
Comparison of Proacademy and EntreComp

Tampereen ammattikorkeakoulu Tampere University of Applied Sciences Degree Programme in Educational Leadership TENEVA, IRENA: Entrepreneurial competences Comparison of Proacademy and EntreComp Master’s thesis 53 pages, appendices 5 pages December 2018 The purpose of this study was to perform a high-level benchmarking of learning outcomes achieved at Proacademy against The Entrepreneurship Competence Framework (EntreComp). Data was collected through semi-structured interviews with six final year bachelor students and two Proacademy coaches. Interviews focused on entrepreneurial competences, as articulated by EntreComp and discussed their application in Proacademy education. It was found that competences included in EntreComp closely match the ones embedded in Proacademy program, thus demonstrating the holistic and multi-faceted nature of both Proacademy program and EntreComp. Findings also indicate that, while the theme of both EntreComp and Proacademy is entrepreneurship, competences analysed are attributes of success in what is expected to be the future of working life and therefore aspirational in any area of education. More work will need to be done to perform a comprehensive mapping against EntreComp and study individual and team competencies of Proacademy learners in greater details.
DELIVERABLE D8.4 Real-time Ghost-in-the-Machine Study

We describe the design of the newly developed Ghost-in-theMachine paradigm and present initial results of an experiment addressing the initiation of service interactions at a bar. For developing policies for a robotic bartender, we investigated which sensor modalities were most informative to humans, and which actions they selected as a socially appropriate response. The results showed that participants used two nonverbal cues for their initial response to a new customer. Those were the distance to the bar and whether the customers’ torso was directed to the bar. For acknowledging a new customer, the participants typically responded nonverbally by looking and smiling at the customers. All results can be directly transferred into robotic decision policies.
Facebook-Induced Friend Shift and Identity Shift: A Longitudinal Study of Facebook Posting and Collegiate Drinking

While research has indicated that perceiving alcohol-related posts on Facebook can be predictive of future alcohol consumption for college students, little research has considered the consequences of posting about alcohol on future behavior. The purpose of this study was to consider the pathways through which alcohol-related posts on Facebook may lead to increased drinking among the college students who make these posts. Incoming college freshmen (310 participants) from 2 universities were interviewed before their freshman year of college (T1) and again 1 year later (T2), and their Facebook profiles were evaluated for alcohol-related posts during the academic year. A positive attitude toward alcohol before freshman year was positively related to drinking activity before sophomore year. This relationship was mediated by alcohol-related Facebook posts during the academic year and the percentage of friends reported to be drinkers before sophomore year. Theoretically, this is evidence of identity shift online, as well as a Facebook-induced friend shift. These findings highlight the role that Facebook may play in both identity development and friendship cultivation for college freshmen, and offer insight into future collegiate drinking prevention and intervention initiatives.
Directed Assembly of Homopentameric Cholera Toxin B-Subunit Proteins into Higher-Order Structures Using Coiled-Coil Appendages

The self-assembly of proteins into higher order structures is ubiquitous in living systems. It is also an essential process for the bottom-up creation of novel molecular architectures and devices for synthetic biology. However, the complexity of protein–protein interaction surfaces makes it challenging to mimic natural assembly processes in artificial systems. Indeed, many successful computationally designed protein assemblies are prescreened for “designability”, limiting the choice of components. Here, we report a simple and pragmatic strategy to assemble chosen multisubunit proteins into more complex structures. A coiled-coil domain appended to one face of the pentameric cholera toxin B-subunit (CTB) enabled the ordered assembly of tubular supra-molecular complexes. Analysis of a tubular structure determined by X-ray crystallography has revealed a hierarchical assembly process that displays features reminiscent of the polymorphic assembly of polyomavirus proteins. The approach provides a simple and straightforward method to direct the assembly of protein building blocks which present either termini on a single face of an oligomer. This scaffolding approach can be used to generate bespoke supramolecular assemblies of functional proteins. Additionally, structural resolution of the scaffolded assemblies highlight “native-state” forced protein–protein interfaces, which may prove useful as starting conformations for future computational design.
Salty sensors, fresh ideas: The use of molecular and imaging sensors in understanding plankton dynamics across marine and freshwater ecosystems

Understanding plankton dynamics in marine ecosystems has been advanced using in situ molecular and imaging instrumentation. A range of research objectives have been addressed through high‐resolution autonomous sampling, from food web characterization to harmful algal bloom dynamics. When used together, molecular and imaging sensors can cover the full‐size range, genetic identity, and life stages of plankton. Here, we briefly review a selection of in situ instrumentation developed for the collection of molecular and imaging information on plankton communities in marine ecosystems. In addition, we interviewed a selection of instrumentation developers to determine if the transfer of sensor technology from marine to freshwater ecosystems is feasible and to describe the process of creating in situ sensors. Finally, we discuss the status of in situ molecular and imaging sensors in freshwater ecosystems and how some of the reviewed sensors could be used to address basic and applied research questions.
List and Retail Prices: A Theory of Price Dispersion

We study two-stage versions of clearinghouse models. In the first stage, supplying firms advertise list prices; in a second stage they set final retail prices. A firm’s retail price is constrained to be no higher than its list price. In contrast to the mixed-strategy equilibria of single-stage games, a unique profile of distinct prices is supported by the play of pure strategies along the equilibrium path. Therefore, we predict stable price dispersion across firms. Unlike previous work, the equilibrium strategies we derive are robust to ex-post deviations and are in line with the findings of empirical studies of price dispersion. We also find novel results in applications of our model to a variety of settings including sales, prominence, advertising and search.
The All-or-Nothing Phenomenon in Sparse Linear Regression

We study the problem of recovering a hidden binary $k$-sparse $p$-dimensional vector $\beta$ from $n$ noisy linear observations $Y=X\beta+W$ where $X_{ij}$ are i.i. d. $\mathcal{N}(0,1)$ and $W_i$ are i.i. d. $\mathcal{N}(0,\sigma^2)$. A closely related hypothesis testing problem is to distinguish the pair $(X,Y)$ generated from this structured model from a corresponding null model where $(X,Y)$ consist of purely independent Gaussian entries. In the low sparsity $k=o(p)$ and high signal to noise ratio $k/\sigma^2=\Omega\left(1\right)$ regime, we establish an `All-or-Nothing' information-theoretic phase transition at a critical sample size $n^*=2 k\log \left(p/k\right) /\log \left(1+k/\sigma^2\right)$, resolving a conjecture of \cite{gamarnikzadik}. Specifically, we show that if $\liminf_{p\to \infty} n/n^*>1$, then the maximum likelihood estimator almost perfectly recovers the hidden vector with high probability and moreover the true hypothesis can be detected with a vanishing error probability. Conversely, if $\limsup_{p\to \infty} n/n^*<1$, then it becomes information-theoretically impossible even to recover an arbitrarily small but fixed fraction of the hidden vector support, or to test hypotheses strictly better than random guess. Our proof of the impossibility result builds upon two key techniques, which could be of independent interest. First, we use a conditional second moment method to upper bound the Kullback-Leibler (KL) divergence between the structured and the null model. Second, inspired by the celebrated area theorem, we establish a lower bound to the minimum mean squared estimation error of the hidden vector in terms of the KL divergence between the two models.
Generating superpixels using deep image representations

Superpixel algorithms are a common pre-processing step for computer vision algorithms such as segmentation, object tracking and localization. Many superpixel methods only rely on colors features for segmentation, limiting performance in low-contrast regions and applicability to infrared or medical images where object boundaries have wide appearance variability. We study the inclusion of deep image features in the SLIC superpixel algorithm to exploit higher-level image representations. In addition, we devise a trainable superpixel algorithm, yielding an intermediate domain-specific image representation that can be applied to different tasks. A clustering-based superpixel algorithm is transformed into a pixel-wise classification task and superpixel training data is derived from semantic segmentation datasets. Our results demonstrate that this approach is able to improve superpixel quality consistently.
National Planning Strategies for Agro-based Industrial Development in Secondary Cities of Sindh Province, Pakistan

The aim of this study was to critically investigate the planning policy loopholes in five-year (7th and 8th) plans of Pakistan to develop agricultural-based industrial sectors in secondary cities of the Sindh Province, Pakistan. The study had conducted key informant interviews from agro-based industrial sectors to diagnose the strengths, weaknesses, opportunities, and threats (SWOT) of the 7th and 8th five-year plans. For that purpose, the study had conducted 30 key informant interviews by using the purposive sampling technique. The NVIVO software was used with content analysis method to get results of SWOT of 7th five-year (1988–1993) and 8th five-year (1993–1998) plans. The results revealed that high priority is given to the textile and sugar industries. However, there is an absence of planning policies for small and medium agricultural industries in secondary cities, fiscal reliance on central government, and reliance on foreign aid as weaknesses of the plans. Whereas the centralized planning system, political influence of federal government, weak agricultural infrastructure services in secondary cities, and cross-border war were the threats hampered in the execution of development plans. Considering these results, the study suggests development of planning policies in the five-year development plans to stimulate the agriculture industrial development in secondary cities and devolution of powers can help to achieve sustainable agricultural development.
Context-Aware Learning for Neural Machine Translation

Interest in larger-context neural machine translation, including document-level and multi-modal translation, has been growing. Multiple works have proposed new network architectures or evaluation schemes, but potentially helpful context is still sometimes ignored by larger-context translation models. In this paper, we propose a novel learning algorithm that explicitly encourages a neural translation model to take into account additional context using a multilevel pair-wise ranking loss. We evaluate the proposed learning algorithm with a transformer-based larger-context translation system on document-level translation. By comparing performance using actual and random contexts, we show that a model trained with the proposed algorithm is more sensitive to the additional context.
Resistance to Antimicrobials and Biofilm Formation in Staphylococcus aureus Isolated from Bovine Mastitis in Beni-Suef Governorate

TAPHYLOCOCCUS (S.) aureus is one of the most prevalent causes most prevalent causes of clinical and subclinical bovine mastitis. A bovine mastitis. A total of 400 lactating cows housed in 5 farms in BeniSuef governorate, Egypt, were examined for presence of either clinical or subclinical mastitis. The examination revealed that 20 (5%) and 78 (21.8%) of animals showed clinical and subclinical mastitis criteria, respectively. Twenty three S. aureus isolates were recovered from 98 milk samples in a prevalence of 23.5%. Antimicrobial susceptibility testing of them against 8 compounds revealed that only one isolate was susceptible to all the tested antibiotics while high percentage (n=14, 60.9%) were resistant to more than one antibiotic. The highest percentage of resistance (82.6%) was documented against penicillin. Multiple drug resistance was observed in 26.1% of the tested isolates. Additionally 11(47.8%) isolates were resistant to cefoxitin so they were categorized phenotypically as methicillin-resistant S.aureus. All the recovered isolates were seeded on congo red agar to evaluate their biofilm forming ability and 18 (78.3%) of them were recorded as biofilm producers. Investigation of icaA, icaD and bap genes among the recovered isolates revealed that icaA and icaD were coexisted in 21 isolates (91.3%) while bap gene was existed in only one isolate (4.3 %).
An End-To-End Network for Panoptic Segmentation

Panoptic segmentation, which needs to assign a category label to each pixel and segment each object instance simultaneously, is a challenging topic. Traditionally, the existing approaches utilize two independent models without sharing features, which makes the pipeline inefficient to implement. In addition, a heuristic method is usually employed to merge the results. However, the overlapping relationship between object instances is difficult to determine without sufficient context information during the merging process. To address the problems, we propose a novel end-to-end Occlusion Aware Network (OANet) for panoptic segmentation, which can efficiently and effectively predict both the instance and stuff segmentation in a single network. Moreover, we introduce a novel spatial ranking module to deal with the occlusion problem between the predicted instances. Extensive experiments have been done to validate the performance of our proposed method and promising results have been achieved on the COCO Panoptic benchmark.
SZENT ISTVÁN UNIVERSITY Animal Husbandry Science PhD School GENERATION OF MOUSE INDUCED PLURIPOTENT STEM (iPS) CELLS BY SLEEPING BEAUTY (SB) TRANSPOSON

in Peer-reviewed Journals:  Muenthaisong, S. , Ujhelly, O. , Varga, E. , Carstea A.C. , Ivics, Z. , Pirity, M. , and Dinnyés, A. 2011. Generation of mouse induced pluripotent stem (iPS) cells from various genetic background by Sleeping Beauty (SB) Transposon mediated gene transfer. Reprod.Fert. Dev. 23 (1): 292.  Muenthaisong, S. , Ujhelly, O. , Varga, E. , Ivics, Z. , Pirity, M. and Dinnyes, A. 2010. Generation of induced pluripotent stem cells from mouse embryonic fibroblasts by Sleeping Beauty transposon. Transgenic Research 19 (2): 87.  Rungarunlert, S. , Tar, K. , Muenthaisong, S. , Techakumphu, M. , Pirity, M.K. , and Dinnyés, A. 2010. Differentiation of mouse embryonic stem cells into cardiomyocytes by using slow tunning lateral vessel (STLV/Bioreactor). Reprod.Fert. Dev. 22 (1): 398.
Deep One-Class Classification Using Intra-Class Splitting

This paper introduces a generic method which enables to use conventional deep neural networks as end-to-end one-class classifiers. The method is based on splitting given data from one class into two subsets. In one-class classification, only samples of one normal class are available for training. During inference, a closed and tight decision boundary around the training samples is sought which conventional binary or multi-class neural networks are not able to provide. By splitting data into typical and atypical normal subsets, the proposed method can use a binary loss and defines an auxiliary subnetwork for distance constraints in the latent space. Various experiments on three well-known image datasets showed the effectiveness of the proposed method which outperformed seven baselines and had a better or comparable performance to the state-of-the-art.
Markers of Immune Activation and Inflammation, and Non-Hodgkin Lymphoma: A Meta-Analysis of Prospective Studies

Abstract Background Chronic inflammation and immune activation are reported to play a key role in the etiology of non-Hodgkin lymphoma (NHL). We conducted a meta-analysis on the associations between prediagnosis circulating levels of immune stimulatory markers, interleukin 6 (IL-6), IL-10, tumor necrosis factor α (TNF-α), CXCL13, soluble CD23 (sCD23), sCD27, sCD30, and the risk of NHL. Methods Relevant studies were identified from PubMed, EMBASE, and Web of Science up to January 1, 2017. We calculated summary odds ratio (OR) estimates for the association between one natural log increase in concentration of each biomarker and NHL using random-effects models for NHL as a composite outcome and for several histological subtypes of NHL. Results Seventeen nested case control studies were included. Elevated levels of several biomarkers were more strongly associated with increased odds of NHL: TNF-α, OR = 1.18 (95% confidence interval [CI] = 1.04 to 1.34); CXCL13, OR = 1.47 (95% CI = 1.03 to 2.08); sCD23, OR = 1.57 (95% CI = 1.21 to 2.05); sCD27, OR = 2.18 (95% CI = 1.20 to 3.98); sCD30, OR = 1.65 (95% CI = 1.22 to 2.22). In stratified analyses, IL-6, TNF-α, sCD27, and sCD30 were more strongly associated with NHL in HIV-infected individuals compared to HIV-uninfected individuals. Between-study heterogeneity was observed across multiple biomarkers for overall NHL and by subtypes. Conclusion This meta-analysis provides evidence that elevated circulating levels of TNF-α, CXCL13, sCD23, sCD27, and sCD30 are consistently associated with an increased risk of NHL, suggesting the potential utility of these biomarkers in population risk stratification and prediction.
Analysis of magnetization transfer (MT) influence on quantitative mapping of T2 relaxation time

Multi‐echo spin‐echo (MESE) protocol is the most effective tool for mapping T2 relaxation in vivo. Still, MESE extensive use of radiofrequency pulses causes magnetization transfer (MT)‐related bias of the water signal, instigated by the presence of macromolecules (MMP). Here, we analyze the effects of MT on MESE signal, alongside their impact on quantitative T2 measurements.
Symbol Detection Aided Algorithm for Interference Alignment of Maximum Signal to Interference and Noise Ratio

In the K x K multiple-input and multiple-output (MIMO) interference alignment (IA) communication system, for the problem of the traditional maximum signal to noise ratio (MAX-SINR) algorithm's poorly performed, a symbol detection aided maximum signal to interference and noise ratio (SDA-MAX-SINR) algorithm for IA is proposed. The algorithm improves the system's performance of the traditional MAX-SINR algorithm. First, the traditional MAX-SINR algorithm for IA is used to obtain the received signal. Next, based on the maximum signal to interference and noise ratio(SINR) criterion, the receiver interference suppression matrix and the transmitter precoding matrix are calculated by using the received signal directly. The simulation result shows that compared with the traditional MAX-SINR algorithm, the SDA-MAX-SINR algorithm has decreases the mean square error (MSE) and the bit error rate (BER) of the system effectively, improved the dependability of MIMO system obviously.
SANTA-SIM: simulating viral sequence evolution dynamics under selection and recombination

Simulations are widely used to provide expectations and predictive distributions under known conditions against which to compare empirical data. Such simulations are also invaluable for testing and comparing the behavior and power of inference methods. We describe SANTA-SIM, a software package to simulate the evolution of a population of gene sequences forwards through time. It models the underlying biological processes as discrete components: replication, recombination, point mutations, insertion-deletions, and selection under various fitness models and population size dynamics. The software is designed to be intuitive to work with for a wide range of users and executable in a cross-platform manner.
A game-based neurofeedback training system to enhance cognitive performance in healthy elderly subjects and in patients with amnestic mild cognitive impairment

Introduction This study examines the clinical efficacy of a game-based neurofeedback training (NFT) system to enhance cognitive performance in patients with amnestic mild cognitive impairment (aMCI) and healthy elderly subjects. The NFT system includes five games designed to improve attention span and cognitive performance. The system estimates attention levels by investigating the power spectrum of Beta and Alpha bands. Methods We recruited 65 women with aMCI and 54 healthy elderly women. All participants were treated with care as usual (CAU); 58 were treated with CAU + NFT (20 sessions of 30 minutes each, 2–3 sessions per week), 36 with CAU + exergame-based training, while 25 patients had only CAU. Cognitive functions were assessed using the Cambridge Neuropsychological Test Automated Battery both before and after treatment. Results NFT significantly improved rapid visual processing and spatial working memory (SWM), including strategy, when compared with exergame training and no active treatment. aMCI was characterized by impairments in SWM (including strategy), pattern recognition memory, and delayed matching to samples. Conclusion In conclusion, treatment with NFT improves sustained attention and SWM. Nevertheless, NFT had no significant effect on pattern recognition memory and short-term visual memory, which are the other hallmarks of aMCI. The NFT system used here may selectively improve sustained attention, strategy, and executive functions, but not other cognitive impairments, which characterize aMCI in women.