Miracle-dz/UniFAR-FTUs huggingface dataset:
Harmonic optimization in seven level inverter employing hybrid BBO/MADS algorithm

A hybrid optimization algorithm to find the optimum switching angles in a Multilevel Inverter (MLI) is proposed in this paper. The switching angles are optimized to eliminate low frequency harmonics. A seven level Cascaded Multilevel Inverter (CMLI) is considered and optimum switching angles are found by Hybrid Biogeographical based optimization search (BBO) / Mesh Adaptive Direct Search (MADS) algorithm. The low frequency Selective Harmonic Elimination-Pulse Width Modulation (SHE-PWM) technique and Optimal Minimization of Total Harmonic Distortion (OMTHD) technique are applied in order to reduce the switching losses. In SHE-PWM based approach, specific lower order harmonics are eliminated and fundamental component of output voltage is maintained at desired value. In OMTHD based approach the overall THD is minimized rather than eliminating specific low frequency harmonics. The various performance indices like phase THD, line THD, lower order harmonics eliminated in a CMLI are calculated from the optimized switching angles. A seven level CMLI is simulated in MATLAB/Simulink by using switching angles calculated from BBO/MADS algorithm. The FFT analysis of SHE-PWM and OMTHD approaches are compared and verified with the predicted values from BBO/MADS algorithm.
Divide, Conquer, Combine Bayesian Decision Tree Sampling

Decision trees are commonly used predictive models due to their flexibility and interpretability. This paper is directed at quantifying the uncertainty of decision tree predictions by employing a Bayesian inference approach. This is challenging because these approaches need to explore both the tree structure space and the space of decision parameters associated with each tree structure. This has been handled by using Markov Chain Monte Carlo (MCMC) methods, where a Markov Chain is constructed to provide samples from the desired Bayesian estimate. Importantly, the structure and the decision parameters are tightly coupled; small changes in the tree structure can demand vastly different decision parameters to provide accurate predictions. A challenge for existing MCMC approaches is proposing joint changes in both the tree structure and the decision parameters that result in efficient sampling. This paper takes a different approach, where each distinct tree structure is associated with a unique set of decision parameters. The proposed approach, entitled DCC-Tree, is inspired by the work in Zhou et al. [23] for probabilistic programs and Cochrane et al. [4] for Hamiltonian Monte Carlo (HMC) based sampling for decision trees. Results show that DCC-Tree performs comparably to other HMC-based methods and better than existing Bayesian tree methods while improving on consistency and reducing the per-proposal complexity.
Abstract Swiss Cheese Space and the Classicalisation of Swiss Cheeses

Swiss cheese sets are compact subsets of the complex plane obtained by deleting a sequence of open disks from a closed disk. Such sets have provided numerous counterexamples in the theory of uniform algebras. In this paper, we introduce a topological space whose elements are what we call "abstract Swiss cheeses". Working within this topological space, we show how to prove the existence of "classical" Swiss cheese sets (as discussed in a paper of Feinstein and Heath from 2010) with various desired properties. We first give a new proof of the Feinstein-Heath classicalisation theorem. We then consider when it is possible to "classicalise" a Swiss cheese while leaving disks which lie outside a given region unchanged. We also consider sets obtained by deleting a sequence of open disks from a closed annulus, and we obtain an analogue of the Feinstein-Heath theorem for these sets. We then discuss regularity for certain uniform algebras. We conclude with an application of these techniques to obtain a classical Swiss cheese set which has the same properties as a non-classical example of O'Farrell (1979).
Follower Agnostic Methods for Stackelberg Games

In this paper, we present an efficient algorithm to solve online Stackelberg games, featuring multiple followers, in a follower-agnostic manner. Unlike previous works, our approach works even when leader has no knowledge about the followers' utility functions or strategy space. Our algorithm introduces a unique gradient estimator, leveraging specially designed strategies to probe followers. In a departure from traditional assumptions of optimal play, we model followers' responses using a convergent adaptation rule, allowing for realistic and dynamic interactions. The leader constructs the gradient estimator solely based on observations of followers' actions. We provide both non-asymptotic convergence rates to stationary points of the leader's objective and demonstrate asymptotic convergence to a \emph{local Stackelberg equilibrium}. To validate the effectiveness of our algorithm, we use this algorithm to solve the problem of incentive design on a large-scale transportation network, showcasing its robustness even when the leader lacks access to followers' demand.
Fully Conservative Higher Order Finite Difference Schemes for Incompressible Flow

Conservation properties of the mass, momentum, and kinetic energy equations for incompressible flow are specified as analytical requirements for a proper set of discrete equations. Existing finite difference schemes in regular and staggered grid systems are checked for violations of the conservation requirements and a few important discrepancies are pointed out. In particular, it is found that none of the existing higher order schemes for a staggered mesh system simultaneously conserve mass, momentum, and kinetic energy. This deficiency is corrected through the derivation of a general family of fully conservative higher order accurate finite difference schemes for staggered grid systems. Finite difference schemes in a collocated grid system are also analyzed, and a violation of kinetic energy conservation is revealed. The predicted conservation properties are demonstrated numerically in simulations of inviscid white noise, performed in a two-dimensional periodic domain. The proposed fourth order schemes in a staggered grid system are generalized for the case of a non-uniform mesh, and the resulting scheme is used to perform large eddy simulations of turbulent channel flow.
Systematic Evaluation of High-Throughput PBK Modelling Strategies for the Prediction of Intravenous and Oral Pharmacokinetics in Humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for Non-Animal Methods. To address this limitation, High-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000+ publicly available human in vivo concentration-time profiles of 200+ compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic High-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within 10-fold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for High-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of High-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.
Homeotic and non-homeotic patterns in the tetrapod vertebral formula

Vertebrae can be differentiated into five categories along the body axis in tetrapods, with its numerical distribution known as the vertebral formula. The vertebral formula is a principal tool for connecting development and phylogeny [1]. This is largely due to its robust relationship with the conserved clusters of Hox genes [2], which exhibit expression boundaries coincident with vertebral divisions [3–11]. One avenue for variations in the vertebral formula is thus through Hox-mediated homeotic transformations, which manifest as a relatively fixed sum of adjacent vertebral counts. This expectation is borne out in the mammalian thoracolumbar count [12], but to date, no similar vertebral patterns have been found. Here we conduct a systematic search by generating a large dataset of complete vertebral formulae in a diverse range of tetrapod species and probing the variance of linear combinations of vertebrae. We uncover additional mammalian homeotic patterns, but also unexpected balances between distal vertebrae not comprehensible with Hox-mediated regionalization. One distal pattern appears during the progression from theropods to birds, demonstrating its phylogenetic importance. We further show that several vertebral counts correlate with posterior intergenic distances in the HoxB gene cluster. By creating a vertebral formula database and mathematically defining patterns, our work establishes a quantitative approach for comparative genomics in morphology.
A high-throughput zebrafish screen identifies novel candidate treatments for Kaposiform Lymphangiomatosis (KLA)

Kaposiform Lymphangiomatosis (KLA) is a rare, aggressive, and incurable disease caused by a somatic activating NRAS mutation (p.Q61R) in lymphatic endothelial cells (LECs). The development of new therapeutic avenues is hampered by the lack of animal models faithfully replicating the clinical manifestations of KLA. Here, we established a novel zebrafish model of KLA by driving conditional expression of the human NRAS mutation in venous and lymphatic ECs. We find that mutant embryos recapitulated clinical features of KLA, including pericardial edema and a dilated thoracic duct, and that the phenotypes were reverted by Trametinib, a MEK inhibitor used for KLA treatment. We further leverage this model in combination with an AI-based high-throughput drug screening platform to search for small compounds selectively reverting the mutant phenotypes and identify Cabozantinib, an FDA-approved tyrosine kinase inhibitor, and GSK690693, a competitive pan-Akt kinase inhibitor, as leading hits. Finally, we test these drugs in cultured cells derived from KLA patient and demonstrate their ability to normalize LEC sprouting and block NRAS downstream pathways, underscoring the potential of GSK690693 and Cabozantinib as potential KLA treatments. Overall, our novel zebrafish model provides a valuable tool for research into the etiology of KLA and for identifying new therapeutic avenues.
Erratum

Blended refuge for transgenic plants expressing Bacillus thuringiensis (Bt) toxins has been approved in the northern United States as a resistance management strategy alternative to a structured refuge. A three-year study (2012–2014) was conducted with 54 trials across nine states in the southern United States to evaluate plant injury from lepidopteran pests of corn and yield in a corn hybrid expressing Cry1F Cry1Ab Vip3Aa20 (Pioneer Brand Optimum Leptra) planted as a pure stand and in refuge blends of 5, 10, and 20% in both early and late plantings. Injury by corn earworm, Helicoverpa zea Boddie (Lepidoptera: Noctuidae), and fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), was generally proportional to the percentage of non-Bt corn within each refuge blend. Across locations, ear injury in plots with 100% Cry1F Cry1Ab Vip3Aa20 (Optimum Leptra) corn ranged from no injury to a maximum of 0.42 cm per ear in Mississippi in 2013. Leaf injury ratings in 100% non-Bt plots in early and late planted trials in 2014 were 86and 70-fold greater than in 100% Cry1F Cry1Ab Vip3Aa20 (Optimum Leptra) plots. Plants in plots with blended refuges had significantly greater leaf injury in 2012 (5, 10, and 20% refuge blends), in the earlyplanted corn in 2013 (10 and 20% only), and in both earlyand late-planted corn in 2014 (20% only) as compared with leaf injury in a pure stand of Cry1F Cry1Ab Vip3Aa20 (Optimum Leptra) seen during these years. Corn ears in plots with blended refuges also had significantly greater area of kernels injured in 2012 (5, 10, and 20%), in earlyand late-planted corn in 2013 (5, 10, and 20%), and in early (10 and 20% only)and late-planted corn (5, 10, and 20%) in 2014 as compared with ear injury in a pure stand of Cry1F Cry1Ab Vip3Aa20 (Optimum Leptra) seen during these years. Infestations of southwestern corn borer, Diatraea grandiosella Dyar (Lepidoptera: Crambidae), were also significantly reduced by Cry1F Cry1Ab Vip3Aa20 (Optimum Leptra). Despite these differences in injury, yield averaged across locations varied among refuge blends only in the late-planted trials in 2013, with greater yields in the 0% refuge blend than in the 20% blend; however, when examining yield separately by location, only two of nine locations had higher yields in the 100% Bt plots than in any of the blended refuge plots. As a complement to studying the contribution of blended refuge to delaying resistance, quantifying injury and yield in a range of VC The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com 1859 Journal of Economic Entomology, 109(4), 2016, 1859–1871 doi: 10.1093/jee/tow109 Advance Access Publication Date: 21 June 2016
Expensive Brains: “Brainy” Rodents have Higher Metabolic Rate

Brains are the centers of the nervous system of animals, controlling the organ systems of the body and coordinating responses to changes in the ecological and social environment. The evolution of traits that correlate with cognitive ability, such as relative brain size is thus of broad interest. Brain mass relative to body mass (BM) varies among mammals, and diverse factors have been proposed to explain this variation. A recent study provided evidence that energetics play an important role in brain evolution (Isler and van Schaik, 2006). Using composite phylogenies and data drawn from multiple sources, these authors showed that basal metabolic rate (BMR) correlates with brain mass across mammals. However, no such relationship was found within rodents. Here we re-examined the relationship between BMR and brain mass within Rodentia using a novel species-level phylogeny. Our results are sensitive to parameter evaluation; in particular how species mass is estimated. We detect no pattern when applying an approach used by previous studies, where each species BM is represented by two different numbers, one being the individual that happened to be used for BMR estimates of that species. However, this approach may compromise the analysis. When using a single value of BM for each species, whether representing a single individual, or available species mean, our findings provide evidence that brain mass (independent of BM) and BMR are correlated. These findings are thus consistent with the hypothesis that large brains evolve when the payoff for increased brain mass is greater than the energetic cost they incur.
Design of MEMS capacitive sensor for the identification of cancerous blood cell: A simulation study

In this paper, an array of MEMS capacitive sensors has been designed for the detection of malignancy in white blood cells (WBC) on the basis of change in dielectric permittivity. Here, an electrostatic based capacitive micro sensor is designed with a pair of rectangular shaped identical electrodes and extended for an array to facilitate such identification on an average of 1000 cells at a time. The electroporation process is followed for identifying the malignant cells as polarization of cell membrane depends on frequency range of 20 kHz – 10 MHz. In order to do so, a double shell electrical model of WBC is designed through which the polarization potential of 1V is achieved with a membrane potential of about 0.5-1V. Thus, the same excitation potential of 1V is applied between the electrodes of MEMS capacitive sensors and the induced polarization changes permittivity that finally reflects a distinguishable change in the output of capacitive sensor. Therefore, it can discriminate a normal cell from affected one in terms of change in capacitive value. This work is executed in multiphysics based simulation software.
Acceptance of angulation in the non-operative treatment of paediatric forearm fractures

Forearm fractures are the most common injury in paediatric traumatology. The unique properties of the juvenile skeleton make it possible to cope well with traumatic deformities such as angulation, apposition and displacement. While we make use of these properties, the exact mechanism and degree of healing remains obscure. Different types of forearm fractures require specific treatment options, each with its limitations. A meta-analysis of recent literature was carried out, and together with the opinions of 18 international experts an effort was made to provide insight into the limits of acceptance of angular deformation in the non-operative treatment of paediatric forearm fractures. With this information we constructed graphs (age versus angulation) for each of the eight types of paediatric forearm fractures. In the absence of proper trials, it is our opinion that the presented Isala graphs can provide useful support in the decision-making process of acceptance of angular deformities in paediatric forearm fractures.
Classifier Stacking and Voting for Text Filtering

This paper summarizes the approach and the results of the TextCat system participating in the Filtering track in the Text Retrieval Conference 2002. The system relies primarily on statistical methods, and was designed with the main purpose of having a backbone system in which we can further integrate semantic components, and evaluate their relative performance as compared to traditional statistical approaches. The system is therefore simple, and is based on techniques for keywords extraction, and various classiﬁer combinations including stacking and voting. TextCat participated in the Batch and Routing tasks. In the Batch task, it achieved a score of 39.02% normalized utility, and 26.37% F-measure respectively, averaged over all topics. The averaged uninterpolated precision for our best routing submission was 14.16%.
On cancellations of ultraviolet divergences in supergravity amplitudes

Concrete calculations have pointed out that amplitudes in perturbative gravity exhibit unanticipated cancellations taming their ultraviolet behaviour independently of supersymmetry. Similar ultraviolet behaviour of 𝒩 = 4 super‐Yang‐Mills and 𝒩 = 8 maximal supergravity has explicitly been observed until three loops. The cancellations can be connected to two manifest features of gravitational theories: firstly gauge invariance from diffeomorphism symmetries and secondly that amplitudes are colourless and exhibits crossing symmetry. We will discuss these two properties in turn as well as the rôle of supersymmetry and string theory dualities in the structure of multiloop amplitudes in supergravity.
Dynamic View Planning by Effective Particles for Three-Dimensional Tracking

In this paper, we propose a new approach to dynamically manage the viewpoint of a vision system for optimal 3-D tracking using particle techniques. We adopt the effective sample size in the proposed particle filter as a criterion for evaluating tracking performance and employ it to guide the view-planning process for finding the best viewpoint configuration. In our approach, the vision system is designed and configured to achieve the largest number of effective particles, which minimizes tracking error by revealing the system to a better swarm of importance samples and interpreting posterior states in a better way. Superiorities of our method are shown by comparison with the resampling particle filter and other view-planning methods.
Integrative Bioinformatic Analysis of Transcriptomic Data Identifies Conserved Molecular Pathways Underlying Ionizing Radiation-Induced Bystander Effects (RIBE)

Ionizing radiation-induced bystander effects (RIBE) encompass a number of effects with potential for a plethora of damages in adjacent non-irradiated tissue. The cascade of molecular events is initiated in response to the exposure to ionizing radiation (IR), something that may occur during diagnostic or therapeutic medical applications. In order to better investigate these complex response mechanisms, we employed a unified framework integrating statistical microarray analysis, signal normalization, and translational bioinformatics functional analysis techniques. This approach was applied to several microarray datasets from Gene Expression Omnibus (GEO) related to RIBE. The analysis produced lists of differentially expressed genes, contrasting bystander and irradiated samples versus sham-irradiated controls. Furthermore, comparative molecular analysis through BioInfoMiner, which integrates advanced statistical enrichment and prioritization methodologies, revealed discrete biological processes, at the cellular level. For example, the negative regulation of growth, cellular response to Zn2+-Cd2+, and Wnt and NIK/NF-kappaB signaling, thus refining the description of the phenotypic landscape of RIBE. Our results provide a more solid understanding of RIBE cell-specific response patterns, especially in the case of high-LET radiations, like α-particles and carbon-ions.
Maximizing Determinism in Stream Processing Under Latency Constraints

The problem of coping with the demands of determinism and meeting latency constraints is challenging in distributed data stream processing systems that have to process high volume data streams that arrive from different unsynchronized input sources. In order to deterministically process the streaming data, they need mechanisms that synchronize the order in which tuples are processed by the operators. On the other hand, achieving real-time response in such a system requires careful tradeoff between determinism and low latency performance. We build on a recently proposed approach to handle data exchange and synchronization in stream processing, namely ScaleGate, which comes with guarantees for determinism and an efficient lock-free implementation, enabling high scalability. Considering the challenge and trade-offs implied by real-time constraints, we propose a system which comprises (a) a novel data structure called Slack-ScaleGate (SSG), along with its algorithmic implementation; SSG enables us to guarantee the deterministic processing of tuples as long as they are able to meet their latency constraints, and (b) a method to dynamically tune the maximum amount of time that a tuple can wait in the SSG data-structure, relaxing the determinism guarantees when needed, in order to satisfy the latency constraints. Our detailed experimental evaluation using a traffic monitoring application deployed in the city of Dublin, illustrates the working and benefits of our approach.