Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cartesian Bicategory of Polynomial Functors in Homotopy Type Theory

Polynomial functors are a categorical generalization of the usual notion of polynomial, which has found many applications in higher categories and type theory: those are generated by polynomials consisting a set of monomials built from sets of variables. They can be organized into a cartesian bicategory, which unfortunately fails to be closed for essentially two reasons, which we address here by suitably modifying the model. Firstly, a naive closure is too large to be well-defined, which can be overcome by restricting to polynomials which are finitary. Secondly, the resulting putative closure fails to properly take the 2-categorical structure in account. We advocate here that this can be addressed by considering polynomials in groupoids, instead of sets. For those, the constructions involved into composition have to be performed up to homotopy, which is conveniently handled in the setting of homotopy type theory: we use it here to formally perform the constructions required to build our cartesian bicategory, in Agda. Notably, this requires us introducing an axiomatization in a small universe of the type of finite types, as an appropriate higher inductive type of natural numbers and bijections.
Wet extrusion of Kikuyu grass (Cenchrus clandestinus (Hochst ex Chiov))

Objectives To assess the effect of wet extrusion on the in vitro digestibility (pepsin-cellulase) of dry matter (IVDDM) and fiber in neutral detergent (IVDNDF) of kikuyu grass (Cenchrus clandestinus (Hochst ex Chiov)). Materials and methods Six samples of this grass harvested at 35 days of regrowth (5.0 kg/sample) were collected and cut to 2.0 cm; a subsample was taken from each sample for the formation of a mixture composed of raw grass in which the content of crude protein (CP), Neutral detergent fiber (NDF), Acid Detergent Fiber (ADF), acid detergent lignin (ADL), ashes (Ash), calcium (Ca), phosphorus (P), potassium (K), IVDDM, and IVDNDF was determined. The remaining amount of each of the six samples was wet-extrusioned into a 2.0mm outlet conical single screw extruder rotating at 1300 rpm. The same analyzes were carried out on the bagasse obtained from each sample and in the raw sample. Data were analyzed using a two-tailed Student’s t-test. Results. The bagasse obtained by wet extrusion of kikuyu grass presented a higher content of NDF and ADF with a lower content of CP and Ash than the raw grass, while the IVDDM increased by more than 8% and the IVDNDF by more than 36%. Conclusions. Wet extrusion significantly improves kikuyu grass IVDDM and IVDNDF.
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation

Prior studies on text-to-text generation typically assume that the model could figure out what to attend to in the input and what to include in the output via seq2seq learning, with only the parallel training data and no additional guidance. However, it remains unclear whether current models can preserve important concepts in the source input, as seq2seq learning does not have explicit focus on the concepts and commonly used evaluation metrics also treat them equally important as other tokens. In this paper, we present a systematic analysis that studies whether current seq2seq models, especially pre-trained language models, are good enough for preserving important input concepts and to what extent explicitly guiding generation with the concepts as lexical constraints is beneficial. We answer the above questions by conducting extensive analytical experiments on four representative text-to-text generation tasks. Based on the observations, we then propose a simple yet effective framework to automatically extract, denoise, and enforce important input concepts as lexical constraints. This new method performs comparably or better than its unconstrained counterpart on automatic metrics, demonstrates higher coverage for concept preservation, and receives better ratings in the human evaluation. Our code is available at https://github.com/morningmoni/EDE.
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images

Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show that Bayesian–CNN can overcome these limitations by regularizing automatically and by quantifying the uncertainty. We have developed a novel technique to utilize the uncertainties provided by the Bayesian–CNN that significantly improves the performance on a large fraction of the test data (about 6% improvement in accuracy on 77% of test data). Further, we provide a novel explanation for the uncertainty by projecting the data into a low dimensional space through a nonlinear dimensionality reduction technique. This dimensionality reduction enables interpretation of the test data through visualization and reveals the structure of the data in a low dimensional feature space. We show that the Bayesian-CNN can perform much better than the state-of-the-art transfer learning CNN (TL-CNN) by reducing the false negative and false positive by 11% and 7.7% respectively for the present data set. It achieves this performance with only 1.86 million parameters as compared to 134.33 million for TL-CNN. Besides, we modify the Bayesian–CNN by introducing a stochastic adaptive activation function. The modified Bayesian–CNN performs slightly better than Bayesian–CNN on all performance metrics and significantly reduces the number of false negatives and false positives (3% reduction for both). We also show that these results are statistically significant by performing McNemar’s statistical significance test. This work shows the advantages of Bayesian-CNN against the state-of-the-art, explains and utilizes the uncertainties for histopathological images. It should find applications in various medical image classifications.
Atmospheric Blocking and Weather Extremes over the Euro-Atlantic Sector – A Review

Abstract. The physical understanding and timely prediction of extreme weather events are of enormous importance to society regarding associated impacts. In this article, we highlight several types of weather extremes occurring in Europe in connection with a particular atmospheric flow pattern, known as atmospheric blocking. This flow pattern effectively blocks the prevailing westerly large-scale atmospheric flow, resulting in changing flow anomalies in the vicinity of the blocking system and persistent conditions in the immediate region of its occurrence. Blockings are long-lasting, quasi-stationary, self-sustaining systems that occur frequently over certain regions. Their presence and characteristics have an impact on the predictability of weather extremes and can thus be used as potential indicators. The phasing between the surface and the upper-level blocking anomalies is of major importance for the development of the extreme event. In summer, heat waves and droughts form below the blocking anticyclone primarily via large-scale subsidence that leads to cloud-free skies and thus, persistent longwave radiative warming of the ground. In winter, cold waves that occur during atmospheric blocking are normally observed downstream or south of these systems. Here, horizontal advection of cold air masses from higher latitudes plays a decisive role. Extreme snowfall can also occur with the lower temperatures, indicating a shift of the storm track due to the blocking system. Such a shift is also crucial in the connection of blocking with wind and precipitation anomalies in general. Due to this multifaceted linkages, compound events are often observed in conjunction with blocking conditions.
Calculation of the Ettingshausen coefficient in quantum wells with parabolic potential in the presence of electromagnetic wave (for electron-confined acoustic phonons scattering)

Recieved: 28/8/2018 Accepted: 10/9/2018 By using the quantum kinetic equation for the distribution function of electrons, the expression for Ettingshausen coefficient (EC) in quantum wells with parabolic potential (QWPP) in the presence of electromagnetic wave (EMW) is obtained for electrons confined acoustic phonons scattering. The analytic results have shown that EC depends on temperature, magnetic field, characteristic quantities of EMW and m quantum number which is specific the confined phonons in a complicated way. The numerical results for GaAs/GaAsAl quantum wells (QW) have displayed these dependence explicitly. In particular, when m is set to zero, we achieve results for magneto – thermoelectric effect in the same QW without the confinement of acoustic phonons.
HOME AUTOMATION USING ML

: Automatic management of home appliances should be unnaturally intelligent systems that require to form itself rely on human being's action and surroundings. These systems square measure totally examine user needs and conditions of the near so as to estimate future actions and cut back user interactions. Here we tend to bring abrand new Home automatic system supported machine learning. During this system we tend to advance VGG16+LSTM design to will increase accuracy and potency of the system. The system contains 3 modes of operations to regulate home appliances, they are: emotion detection mode, automatic mode and manual mode. This method prominence given to energy consumption and prediction performed by provision regression and additionally confirm frequency occurring set of devices by victimization apriori rule . The another element of this method is that we will examine power consumption of individual devices.
Subtle influence of the Atlantic Meridional Overturning Circulation (AMOC) on seasonal sea surface temperature (SST) hindcast skill in the North Atlantic

Abstract. We investigate the impact of the strength of the Atlantic Meridional Overturning Circulation (AMOC) at 26∘ N on the prediction of North Atlantic sea surface temperature anomalies (SSTAs) a season ahead. We test the dependence of sea surface temperate (SST) predictive skill in initialised hindcasts on the phase of the AMOC at 26∘N, invoking a seesaw mechanism driven by AMOC fluctuations, with positive SSTAs north of 26∘ N and negative SSTAs south of 26∘ N after a strong AMOC and vice versa. We use initialised simulations with the MPI-ESM-MR (where MR is mixed resolution) seasonal prediction system. First, we use an assimilation experiment between 1979–2014 to confirm that the AMOC leads a SSTA dipole pattern in the tropical and subtropical North Atlantic, with the strongest AMOC fingerprints after 2–4 months. Going beyond previous studies, we find that the AMOC fingerprint has a seasonal dependence and is sensitive to the length of the observational window used, i.e. stronger over the last decade than for the entire time series back to 1979. We then use a set of ensemble hindcast simulations with 30 members, starting each February, May, August and November between 1982 and 2014. We compare the changes in skill between composites based on the AMOC phase a month prior to each start date to simulations without considering the AMOC phase and find subtle influence of the AMOC mechanism on seasonal SST prediction skill. We find higher subtropical SST hindcast skill at a 2–4-month lead time for June–July–August (JJA) SSTA composites based on the AMOC phase at May start dates than for the full time period. In other regions and seasons, we find a negligible impact of the AMOC seesaw mechanism on seasonal SST predictions due to atmospheric influence, calling for caution when considering such a mechanism. Our method shows that, for May start dates following strong AMOC phases, summer SST hindcast skill over the subtropics increases significantly compared to that of weak AMOC phases. This suggests that in the assessment of SST skill for a season ahead an eye should be kept on the initial AMOC state.
Beyond the exodus of May–June 1940: Internal migration in France during the Second World War

BACKGROUND Research on annual migration between regions of the same country is scarce because statistical sources are often lacking. Consequently, little is known about migration between French regions during the Second World War. OBJECTIVES Propose a new method to estimate the annual populations of each region and deduce the annual migration flows between regions of the same country. Use this method to present the chronology of inter-regional migration flows in France during the Second World War. METHODS Annual regional populations are estimated using census populations and death-by-cause statistics at the local level. Death-by-cause statistics can be used to dissociate symmetrical mortality, which trends in the same way in the national territory, from asymmetrical mortality, which trends differently. This makes it possible to deduce the trend in the mortality rate and populations at the local level. RESULTS Analysis of French migration reveals that (1) a large proportion of the population (up to 60%) was displaced from the north to the south in 1940 and 1941; (2) the demarcation line did not prevent population movements; (3) the demographic scars of war were still present in 1946 since not all the refugees from the northwest and northeast returned. CONTRIBUTIONS This new method for analyzing annual migration flows between regions could be replicated to study other specific historical events. This paper also proposes a quantitative historical analysis of migration in France during the Second World War and shows that some territories were emptied of more than half their population in a few months. 1 Institut National d'Études Démographiques (INED), Paris, France. Email: florian.bonnet@ined.fr. Bonnet: Beyond the exodus of May-June 1940: Internal migration in France during the Second World War 578 https://www.demographic-research.org
Forecasting the Impact of IXP Outages Using Anycast

—Internet eXchange Points (IXPs) play a major role in Internet connectivity. They provide an infrastructure for trafﬁc exchange, attracting Internet Service Providers (ISPs), Content Delivery Networks (CDNs) and cloud providers, leading to a plethora of options for network operators to connect. Alongside, policymakers are interested in understanding how failures of these infrastructures could affect the cyber economy. In this paper, we propose a new method based on anycast to assess the importance of IXPs in terms of coverage and regional represen-tativeness with the goal of predicting the impact of IXP failures. We deployed an anycast infrastructure to connect to major IXPs, simulate outages, and map the impact of such failures. Our experiments show that our methodology can predict how the trafﬁc ﬂows when one IXP goes down, helping operators get prepared to deal with these events, and add more information to better assess the importance of IXPs as a critical infrastructure.
Mantle Inputs to the Subduction Factory: Assessing Scales of Spatial Variability along and Across the IBM Convergent Margin

Subduction Zones are complex physical and chemical systems where surficial Earth materials are dragged down to interact with asthenospheric mantle and generate melts. This interaction equilibrates Earth’s mantle with its hydrosphere and atmosphere and produces continental crust, ore deposits, and explosive volcanism. Understanding how this interaction leads to melting, how these melts rise through downwelling mantle, and how melts evolve in the crust is a great scientific challenge, requiring multiple perspectives. One important perspective comes from understanding how the composition of magmas changes with depth to the Wadati-Benioff Zone (WBZ). From the earliest days of the plate tectonic revolution, systematic variations in magma chemistry—specifically, higher K contents with greater depth to the WBZ—were noted (Hatherton and Dickinson, 1969). In spite of this systematic behavior, it is still not clear what process is responsible. Hatherton and Dickinson (1969) were undecided as to its cause, suggesting that it could reflect either variations in the stability of K-bearing phases in the subducted slab (i.e., amphibole vs. phlogopite) or manifested differing ascent paths or melting processes.
Automated creation of parallel Bible corpora with cross-lingual semantic concordance

Here we present a novel approach for automated creation of parallel New Testament corpora with cross-lingual semantic concordance based on Strong’s numbers. As scientific editions and translations of Bible texts are often not free to use for scientific purposes and are rarely free to use, and due to the fact that the annotation, curation and quality control of alignments between these texts are quite expensive, there is a lack of available Biblical resources for scholars. We present two approaches to tackle the problem, a dictionary-based approach and a Conditional Random Field (CRF) model and a detailed evaluation on annotated and non-annotated translations. We discuss a proof-of-concept based on English and German New Testament translations. The results presented in this paper are novel and according to our knowledge unique. They present promising performance, although further research is necessary.
The Impact of Fragility on Growth and Poverty in Nigeria: A Disaggregate State-level Analysis

This paper empirically investigated the fragility-growth nexus, as well as the fragilitypoverty nexus, in a sample of 36 states and Federal Capital Territory (FCT) in Nigeria. We further considered the macroeconomic and socio-political relationship in fragile and non-fragile states of Nigeria. Using data covering the period between 2011 and 2015, both the static approach (Ordinary Least Squares, Fixed Effect, and Random Effect) and dynamic approach (Difference and System Generalized Method of Moments) were explored to provide answers to some key questions in the study. The results showed that neoclassical approach and socio-political approach complement each other. We further observed that, economic growth and poverty need urgent attention in both fragile and non-fragile states, but special attention should be directed towards fragile states to move them above fragility trap for them to be more resilient. There is still high-level poverty coupled with declined growth in both regions. The ImpacT of fragIlITy on growTh and poverTy In nIgerIa 1
MORPHOLOGICAL AND MOLECULAR CHARACTERISATION OF Streptomyces spp. WHICH SUPPRESS PATHOGENIC FUNGI N. GOREDEMA, T. NDOWORA, R. SHOKO and E. NGADZE Chinhoyi University of Technology, Private Bag 7724, Chinhoyi, Zimbabwe

Streptomyces species are aerobes and chemoorganotrophic bacteria. These microorganisms produce a wide range of industrially significant compounds, specifically antibiotics and anti fungal substances. The objective of this study was to characterise soil-borne Streptomyces isolates using morphological and molecular traits in order to identify them to species level, and leverage from their potential to suppress the growth of Aspergillus flavus, Fusarium oxysporum and Penicillium italicum. Twentyseven soil-borne putative Streptomyces, which elicited comprehensive antimicrobial activity against Aspergillus flavus, Fusarium oxysporum and Penicillium italicum, in a previous study, were evaluated. On the basis of morphology, the bacteria resembled the genus Streptomyces. Initially, colonies phenotypically appeared to have a relatively smooth surface but as growth progressed the bacteria developed a weft of aerial mycelium granular, powdery or velvety in appearance. Bacteria produced a wide variety of pigments which in turn were responsible for the colour of the vegetative and aerial mycelia, colour ranged from white to cream or buff shades and yellow to orange or brown. Microscopic analyses and morphological characteristics generated sub-groups of the isolates and clustered them according to their similarities. One bacterial strain was randomly selected from each cluster and investigated using molecular characteristics. Partial 16S rDNAs from the selected representative isolates from each subgroup, were sequenced and phylogenetic analysis performed. The 16S rDNA sequences of the isolates indicated that they were related to Streptomyces species: S. bungoensis, S. thermocarboxydus, S. corchorusii and S. lasaliensis, that are known secondary metabolite producers possessing antimicrobial activity against plant pathogens.
Bank Non-Performing Loans in the Fintech Era

This study investigates the behavior of bank non-performing loans in the Fintech era. Using data from 35 developed countries from 1998 to 2016, the findings show that non-performing loans are fewer in the second wave Fintech era. Also, bank non-performing loans are positively related to the state of the business cycle in the second wave Fintech era. Countries that have high supply of credit to the private sector experience high non-performing loans in the second wave Fintech era. The two-way interaction analysis show that non-performing loans are lower during times of economic boom and when there is higher credit supply in the second-wave Fintech era.
PRBM_A_317641 675..685

1Department of Cardiac Sciences, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 2Department of Medical Education, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 3Department of Prosthetic Dental Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia; 4Department of Rehabilitation Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia Purpose: To estimate dental students’ self-perception of mental well-being (MWB) and its effects on their clinical psychomotor skills (CPS) once they (or their family members) get infected with COVID-19. Materials and Methods: This is a cross-sectional (n =268) study from a public dental college in Riyadh. We collected data on MWB and CPS. An 18-item online survey was used to collect the responses from the participants. The inter-rater reliability for the finalised survey came out to be 0.86. Results: The participants (54.3%) who were infected with COVID-19 recorded that their CPS were significantly affected (almost 4 times higher) as compared to others (OR= 4.02; P=0.0004). However, 42.2% reported infection control measures at clinics resulted in bringing significant (OR=2.22, P=0.04) psychological upsets, for those who were infected with COVID-19. Participants (45.1%) also reported that they have difficulty in recalling old memories or information due to the COVID-19 pandemic; among them, 46.7% were the ones who (and/or any family member) were exposed to COVID-19. Conclusion: The study spotlighted the extent of dental students’ MWB and its significant effect on their CPS once (themselves or a family member) infected with COVID-19. Moreover, levels of infection control measures at clinics resulted in psychological upsets for dental students. Clinical Significance: The issue is fundamental as participants enter the clinical workforce and face the ever-increasing demands of dental practice.
A Systematic Review of Android Malware Detection Techniques

Malware detection is a significant key to Android application security. Malwares threat to Android users is increasing day by day. End users need security because they use mobile device to communicate information. Therefore, developing malware detection and control technology should be a priority. This research has extensively explored various state of the art techniques and mechanisms to detect malwares in Android applications by systematic literature review. It categorized the current researches into static, dynamic and hybrid approaches. This research work identifies the limitation and strength current research work. According to the restrictions of current malware detection technologies, it can conclude that detection technologies that use statistical analysis consume more time, energy and resources as compare to machine learning techniques. The results obtained from this research work reinforce the assertion that detection approaches designed for Android malware do not produce 100% efficient detection accuracy.