Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of the periodic feeding of linseed oil on the production and iodine value of milk fat

Summary A study was made of the changes in the iodine value of milk fat from cows following the feeding of linseed oil (450g) at intervals of 6 days. Three cows in midlactation and of differing age and breed were used. The experiment consisted of a series of 3 collection periods, each of 6 days' duration, with 12 days between periods. Three doses of oil were given (6 days apart) between periods 1 and 2, the last dose of oil being given immediately before the start of period 2. The mean iodine values for the 3 periods were 35·5, 40·5 and 34·7 respectively. There was a distinct pattern of change in iodine value with time after feeding the oil, a maximum of 45·5 being attained on the second day with a subsequent decline to 38·5 on the sixth day. Analysis of variance showed that these effects were significant and due to oil-feeding, whereas changes in yield and content of fat and production of milk could be accounted for by normal decline in lactation. It was concluded that the periodic feeding of a large dose of linseed oil produces a significant increase in the iodine value of milk fat and avoids the possible disadvantages of continuous fat supplementation.
Percutaneous approach in early-stage ovarian cancer staging

Epithelial ovarian cancer (EOC) is still considered the most lethal oncological disease in gynecology. Surgery alone or surgery plus adjuvant chemotherapy are considered potentially curative for EOC early stage, with a 5-year survival rate close to 90%. In the last decades, the feasibility and the adequacy of laparoscopic (LPS) surgical staging in early-stage EOC were assessed and described in several studies. The endoscopic approach, maintaining oncological adequacy, seems to provide better quality of life, reducing the effects of surgical experience on the patient’s body. In the last few years, minimally invasive surgery (MIS) has shown remarkable progress, leading to the development of increasingly less invasive surgeries such as the Percutaneous Surgical System (PSS). In this surgical video article, we performed a percutaneous-surgical staging for early-stage ovarian cancer patient. Perioperative outcomes were described: the total operative time was 180 min and the intraoperative estimated blood loss was 100 mL. No early or late postoperative complications were recorded. After 24 months of follow-up, no evidence of recurrence was detected. This surgical video aims to show the feasibility and safety of the PSS in early-stage ovarian cancer surgical staging.
High heritability does not imply accurate prediction under the small additive effects hypothesis

Genome-Wide Association Studies (GWAS) explain only a small fraction of heritability for most complex human phenotypes. Genomic heritability estimates the variance explained by the SNPs on the whole genome using mixed models and accounts for the many small contributions of SNPs in the explanation of a phenotype. This paper approaches heritability from a machine learning perspective, and examines the close link between mixed models and ridge regression. Our contribution is twofold. First, we propose estimating genomic heritability using a predictive approach via ridge regression and Generalized Cross Validation (GCV). We show that this is consistent with classical mixed model based estimation. Second, we derive simple formulae that express prediction accuracy as a function of the ratio n/p, where n is the population size and p the total number of SNPs. These formulae clearly show that a high heritability does not imply an accurate prediction when p>n. Both the estimation of heritability via GCV and the prediction accuracy formulae are validated using simulated data and real data from UK Biobank.
Tangent curves to degenerating hypersurfaces

Abstract We study the behaviour of rational curves tangent to a hypersurface under degenerations of the hypersurface. Working within the framework of logarithmic Gromov–Witten theory, we extend the degeneration formula to the logarithmically singular setting, producing a virtual class on the space of maps to the degenerate fibre. We then employ logarithmic deformation theory to express this class as an obstruction bundle integral over the moduli space of ordinary stable maps. This produces new refinements of the logarithmic Gromov–Witten invariants, encoding the degeneration behaviour of tangent curves. In the example of a smooth plane cubic degenerating to the toric boundary we employ localisation and tropical techniques to compute these refinements. Finally, we leverage these calculations to describe how embedded curves tangent to a smooth cubic degenerate as the cubic does; the results obtained are of a classical nature, but the proofs make essential use of logarithmic Gromov–Witten theory.
Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks

Very-high-resolution (VHR) images can provide abundant ground details and spatial geometric information. Change detection in multi-temporal VHR images plays a significant role in urban expansion and area internal change analysis. Nevertheless, traditional change detection methods can neither take full advantage of spatial context information nor cope with the complex internal heterogeneity of VHR images. In this paper, a powerful feature extraction model entitled multi-scale feature convolution unit (MFCU) is adopted for change detection in multi-temporal VHR images. MFCU can extract multi-scale spatial-spectral features in the same layer. Based on the unit two novel deep siamese convolutional neural networks, called as deep siamese multi-scale convolutional network (DSMS-CN) and deep siamese multi-scale fully convolutional network (DSMS-FCN), are designed for unsupervised and supervised change detection, respectively. For unsupervised change detection, an automatic pre-classification is implemented to obtain reliable training samples, then DSMS-CN fits the statistical distribution of changed and unchanged areas from selected training samples through MFCU modules and deep siamese architecture. For supervised change detection, the end-to-end deep fully convolutional network DSMS-FCN is trained in any size of multi-temporal VHR images, and directly outputs the binary change map. In addition, for the purpose of solving the inaccurate localization problem, the fully connected conditional random field (FC-CRF) is combined with DSMS-FCN to refine the results. The experimental results with challenging data sets confirm that the two proposed architectures perform better than the state-of-the-art methods.
"It's Not Something We Have Talked to Our Team About": Results From a Preliminary Investigation of Cybersecurity Challenges in Denmark

Although Denmark is reportedly one of the most digitised countries in Europe, IT security in Danish companies has not followed along. To shed light into the challenges that companies experience with implementing IT security, we conducted a preliminary study running semi-structured interviews with four employees from four different companies, asking about their IT security and what they need to reduce risks of cyber threats. Our results show that companies are lacking fundamental security protection and are in need of guidance and tools to help them implementing basic security practices, while raising awareness of cyber threats. Based on our findings and with the inspiration of the latest reports and international security standards, we discuss steps towards further investigation towards developing a framework targeting SMEs that want to adopt straightforward and actionable IT security guidance.
Learning to Play Sequential Games versus Unknown Opponents

We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action. We seek to design strategies for the learner to successfully interact with the opponent. While most previous approaches consider known opponent models, we focus on the setting in which the opponent's model is unknown. To this end, we use kernel-based regularity assumptions to capture and exploit the structure in the opponent's response. We propose a novel algorithm for the learner when playing against an adversarial sequence of opponents. The algorithm combines ideas from bilevel optimization and online learning to effectively balance between exploration (learning about the opponent's model) and exploitation (selecting highly rewarding actions for the learner). Our results include algorithm's regret guarantees that depend on the regularity of the opponent's response and scale sublinearly with the number of game rounds. Moreover, we specialize our approach to repeated Stackelberg games, and empirically demonstrate its effectiveness in a traffic routing and wildlife conservation task
Towards Tracking Data Flows in Cloud Architectures

As cloud services become central in an increasing number of applications, they process and store more personal and business-critical data. At the same time, privacy and compliance regulations such as the General Data Protection Regulation (GDPR), the EU ePrivacy regulation, and the upcoming EU Cybersecurity Act raise the bar for secure processing and traceability of critical data. Especially the demand to provide information about existing data records of an individual and the ability to delete them on demand is central in privacy regulations. Common to these requirements is that cloud providers must be able to track data as it flows across the different services to ensure that it never moves outside of the legitimate realm, and it is known at all times where a specific copy of a record that belongs to a specific individual or business process is located. However, current cloud architectures do neither provide the means to holistically track data flows across different services nor to enforce policies on data flows. In this paper, we point out the deficits in the data flow tracking functionalities of major cloud providers by means of a set of practical experiments. We then generalize from these experiments introducing a generic architecture that aims at solving the problem of cloud-wide data flow tracking and show how it can be built in a Kubernetes-based prototype implementation.
A Posteriori Error Estimates for Adaptive QM/MM Coupling Methods

Hybrid quantum/molecular mechanics models (QM/MM methods) are widely used in material and molecular simulations when MM models do not provide sufficient accuracy but pure QM models are computationally prohibitive. Adaptive QM/MM coupling methods feature on-the-fly classification of atoms during the simulation, allowing the QM and MM subsystems to be updated as needed. In this work, we propose such an adaptive QM/MM method for material defect simulations based on a new residual based it a posteriori error estimator, which provides both lower and upper bounds for the true error. We validate the analysis and illustrate the effectiveness of the new scheme on numerical simulations for material defects.
A Novel Approach to Generate Correctly Rounded Math Libraries for New Floating Point Representations

Given the importance of floating-point~(FP) performance in numerous domains, several new variants of FP and its alternatives have been proposed (e.g., Bfloat16, TensorFloat32, and Posits). These representations do not have correctly rounded math libraries. Further, the use of existing FP libraries for these new representations can produce incorrect results. This paper proposes a novel methodology for generating polynomial approximations that can be used to implement correctly rounded math libraries. Existing methods produce polynomials that approximate the real value of an elementary function $f(x)$ and experience wrong results due to errors in the approximation and due to rounding errors in the implementation. In contrast, our approach generates polynomials that approximate the correctly rounded value of $f(x)$ (i.e., the value of $f(x)$ rounded to the target representation). This methodology provides more margin to identify efficient polynomials that produce correctly rounded results for all inputs. We frame the problem of generating efficient polynomials that produce correctly rounded results as a linear programming problem. Our approach guarantees that we produce the correct result even with range reduction techniques. Using our approach, we have developed correctly rounded, yet faster, implementations of elementary functions for multiple target representations. Our Bfloat16 library is 2.3$\times$ faster than the corresponding state-of-the-art while producing correct results for all inputs.
Positive and Negative Plant-Plant Interactions in Two Contrasting Arctic-Alpine Plant Communities

Abstract Positive interactions in alpine plant communities have been reported to increase in importance with increasing altitude and exposure. Positive and negative interactions between plants might occur simultaneously, so the net plant-plant interaction is determined by the balance between positive and negative effects. I investigated the relative effect of facilitation and resource competition by surrounding dwarf shrubs on Carex bigelowii in two contrasting arctic-alpine tundra heathlands. Carex bigelowii was positively associated with dwarf shrubs on an exposed mountain ridge but negatively associated with dwarf shrubs on a protected heath. A removal experiment indicated that positive associations at the exposed site are the result of facilitation of C. bigelowii by the dwarf shrub canopy. Our understanding of arctic and alpine plant communities can be enhanced by regarding plant interactions as combinations of positive and negative components.
Value of combining PET/CT and clinicopathological features in predicting EGFR mutation in Lung Adenocarcinoma with Bone Metastasis

Purpose: Epidermal growth factor receptor (EGFR) mutation is the most common target for precision treatment in metastatic lung adenocarcinoma. We investigated the predictive role of 18F-FDG PET/CT and clinicopathological features for EGFR mutations in lung adenocarcinoma with bone metastasis. Methods: Seventy-five lung adenocarcinoma patients with histologically confirmed bone metastasis were included. They all received EGFR status test and PET/CT before systemic treatment. The differences of maximum standardized uptake value (SUVmax) in primary tumor (pSUVmax), regional lymph node (nSUVmax) and bone metastasis (bmSUVmax) between different EGFR status groups were compared, alongside with common clinicopathological features. Multivariate logistic regression analysis was performed to evaluate predictors of EGFR mutations. Results: EGFR mutations were found in 37 patients (49.3%). EGFR mutations were more common in females, non-smokers, expression of Thyroid Transcription Factor-1 (TTF-1) and NaspinA. Low bmSUVmax was significantly associated with EGFR mutations, while no significant difference was observed in pSUVmax and nSUVmax. Multivariate analysis showed that bmSUVmax ≤7, non-smoking, expression of TTF-1 were predictors of EGFR mutations. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was 0.84 for the combination of the three factors. Conclusion: Low bmSUVmax is more frequently in EGFR mutations, and bmSUVmax is an independent predictor of EGFR mutations. Combining bmSUVmax with other clinicopathological features could forecast the EGFR status in lung adenocarcinoma with unavailable EGFR gene testing.
Novel three Phase Quasi Z source Multilevel Inverter Topology for Wind Turbine Driven Permanent Magnet Generator

This paper presents the utilization of multilevel inverter that has been designed to reduce the circuit complexity and optimizing the use of bidirectional switches. A new three phase Quasi Z source multilevel inverter (QZS-MLI) for direct drive wind turbine generator applications has been proposed. The proposed inverter consists of an auxiliary circuit, made up of inductor and capacitor network and 5 bidirectional switches along with the full bridge inverter configuration. The proposed QZSMLI is able to produce 7 levels of staircase waveforms with only 2 shared power switches. The output voltage is similar to the conventional multilevel inverters but with less power switches and THD. A modified carrier based level shifted pulse width modulation control strategy has also been developed. The performance of the proposed inverter is analyzed through MATLAB/SIMULINK and the results are verified with experimental setup.
Differential expression of four Cav3.1 splice variants in the repeat iii‐iv loop

Abstract Molecular cloning revealed the three isoforms (Cav3.1, Cav3.2, and Cav3.3) of the T‐type calcium channel subfamily. Expression studies exhibited their distinctive electrophysiological and pharmacological properties, accounting for diverse properties of T‐type calcium channel currents previously characterized from isolated cells. However, electrophysiological properties of ion channels have shown to be more diversified by their splice variants. We here searched splice variants of rat Cav3.1 T‐type channel by reverse‐transcription‐polymerase chain reaction (RT‐PCR) to further explore diversity of Cav3.1. Interestingly, analyses of cloned RT‐PCR products displayed that there were at least four splicing variants of rat Cav3.1 in the loop connecting repeats III and IV. Southern blot analyses indicated that the predominantly detected variant in brain was Cav3.1a (492 bp), which were rarely detected in most of peripheral tissues. Other two variants (Cav3.1c, 546 bp; Cav3.1d, 525 bp) were detected in most of the tissues examined. The smallest isoform (Cav3.1b, 471 bp) was rarely detected all the tissues. Electrophysiological characterization of the splicing variants indicated that the splice variants differ in inactivation kinetics and the voltage dependence of activation and inactivation as well.
Genetic Diversity of a Tropical Tree Species, Shorea leprosula Miq. (Dipterocarpaceae), in Malaysia: Implications for Conservation of Genetic Resources and Tree Improvement 1

Genetic diversity and population genetic structure of Shorea leprosula was investigated using seven natural populations distributed throughout Peninsular Malaysia and one natural population from Borneo. The mean population and species level genetic diversity were exceptionally high (He= 0.369 ± 0.025 and 0.406 ± 0.070, respectively). Heterozygosity varied among populations, ranging from 0.326 to 0.400, with the highest values found in the populations from central Peninsular Malaysia. Correlations among ecological factors (longitude, latitude, and annual rainfall) were not significant (P > 0.05), indicating that these ecological variables were not responsible for the observed genetic differences among populations. The Bangi adult population exhibited a higher level of observed heterozygosity but lower fixation indices in comparison to its seedling population. All other seedling populations also showed positive fixation indices (f), indicating a general excess of homozygotes. This also may suggest selection against homozygotes between the seedling and adult stages. A low level of population differentiation was detected (GST= 0.117 with the Lambir population and GST= 0.085 without the Lambir population). Furthermore, gene flow (Nm) between populations was not significantly correlated with geographical distances for the populations within Peninsular Malaysia. Cluster analysis also did not reflect geographical proximity and gave little insight into the genetic relatedness of the populations. This may indicate that the populations sampled are part of a continuous population with fragmentation having occurred in the recent past.
On the p-adic uniformization of unitary Shimura curves

We prove $p$-adic uniformization for Shimura curves attached to the group of unitary similitudes of certain binary skew hermitian spaces $V$ with respect to an arbitrary CM field $K$ with maximal totally real subfield $F$. For a place $v|p$ of $F$ that is not split in $K$ and for which $V_v$ is anisotropic, let $\nu$ be an extension of $v$ to the reflex field $E$. We define an integral model of the corresponding Shimura curve over ${\rm Spec}\, O_{E, (\nu)}$ by means of a moduli problem for abelian schemes with suitable polarization and level structure prime to $p$. The formulation of the moduli problem involves a Kottwitz condition, an Eisenstein condition, and an adjusted invariant. The first two conditions are conditions on the Lie algebra of the abelian varieties; the last condition is a condition on the Riemann form of the polarization. The uniformization of the formal completion of this model along its special fiber is given in terms of the formal Drinfeld upper half plane for $F_v$. The proof relies on the construction of the contracting functor which relates a relative Rapoport-Zink space for strict formal $O_{F_v}$-modules with a Rapoport-Zink space of $p$-divisible groups which arise from the moduli problem, where the $O_{F_v}$-action is usually not strict when $F_v\ne \mathbb {Q}_p$. Our main tool is the theory of displays, in particular the Ahsendorf functor.
Self-Healing Dilemmas in Distributed Systems: Fault Correction vs. Fault Tolerance

Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: fault detection inherits network uncertainties making a remote faulty process indistinguishable from a slow process. In the case of a slow process without fault, fault correction is undesirable as it can trigger new faults that could be prevented with fault tolerance that is a more proactive system maintenance. But in the case of an actual faulty process, fault tolerance alone without eventually correcting persistent faults can make systems underperforming. Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several energy, transport and health applications. This paper contributes a novel and general-purpose modeling of fault scenarios during system runtime. They are used to accurately measure and predict inconsistencies generated by the undesirable outcomes of fault correction and fault tolerance as the means to improve self-healing of large-scale decentralized systems at the design phase. A rigorous experimental methodology is designed that evaluates 696 experimental settings of different fault scales, fault profiles and fault detection thresholds in a prototyped decentralized network of 3000 nodes. Almost 9 million measurements of inconsistencies were collected in a network, where each node monitors the health status of another node, while both can defect. The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network data aggregation using real-world data from a Smart Grid pilot project. Findings confirm the origin of inconsistencies at design phase and provide new insights how to tune self-healing at an early stage. Strikingly, the aggregation accuracy is well predicted as shown by high correlations and low root mean square errors.