Miracle-dz/UniFAR-FTUs huggingface dataset:
The General Aspects of Heterotic Superstring/F-Theory Duality Correspondence of Moduli Superspaces

In the present article we aim to broaden the consideration of background geometry of manifolds/bundles arising in heterotic compactifications with an aim towards extending the validity and understanding of heterotic/F-theory duality. In particular, we will focus on elliptically fibered Calabi-Yau geometries arising in heterotic theories in the context of the so-called Fourier Mukai transforms of vector bundles on elliptically fibered manifolds. The duality between the Heterotic and F-theory is a powerful tool in gaining more insights into F-theory description of low-energy chiral multiplets. We propose a generalization of heterotic/F-theory duality and in order to complete the translation, the dictionary of the heterotic/F-theory duality has to be refined in some aspects. The precise map of spectral surface and complex structure moduli is obtained, and with the map, we find that divisors specifying the line bundles correspond precisely to codimension singularities in F-theory.
Effects of Body Weight and Injection Rate of Contrast Medium on 16-Row Multidetector Computed Tomography of Canine Aorta

In this research, the imaging characteristics of non-ionic iodinated contrast medium in the caudal aorta of 19 dogs were evaluated under 16-row MDCT. Study 1: contrast was injected with different speeds (3 mL/sec and 4 mL/sec) to two groups of dogs with similar body weights (20.58±6.32 kg and 23.13±6.03 kg). Study 2: two groups of dogs with significant different body weights (23.13±6.03 kg and 62.28±9.63 kg) were administered with contrast at the some injection rate (4 mL/sec). Similar to the observation in humans, the time-enhancement curve in caudal aorta of all 19 dogs achieved the maximum enhancement rapidly, during which an optimal enhancement period was generated, and the enhancement decreased afterwards following metabolism of the contrast. However in dogs, the time-enhancement curves are shifted more leftward. The result of study 1 shows a faster injection rate achieves the optimal enhancement period sooner, and with higher maximum enhancement attenuation. Study 2 shows dogs with heavier body weight will spend longer time to achieve the optimal enhancement period, and this period is more extended comparing to dogs of lighter body weight. In addition, a saline chaser following the completion of contrast injection further decreases the required volume of contrast without impacting the optimal aortic enhancement. This also produced an even higher and more prolonged optimal enhancement period.
Transformer-Based Acoustic Modeling for Streaming Speech Synthesis

Transformer models have shown promising results in neural speech synthesis due to their superior ability to model long-term dependencies compared to recurrent networks. The computation complexity of transformers increases quadratically with sequence length, making it impractical for many real-time applications. To address the complexity issue in speech synthesis do-main, this paper proposes an efﬁcient transformer-based acoustic model that is constant-speed regardless of input sequence length, making it ideal for streaming speech synthesis applications. The proposed model uses a transformer network that predicts the prosody features at phone rate and then an Emformer network to predict the frame-rate spectral features in a streaming manner. Both the transformer and Emformer in the proposed architecture use a self-attention mechanism that involves explicit long-term information, thus providing improved speech naturalness for long utterances. In our experiments, we use a WaveRNN neural vocoder that takes in the predicted spectral features and generates the ﬁnal audio. The overall architecture achieves human-like speech quality both on short and long utterances while maintaining a low latency and low real-time factor. Our mean opinion score (MOS) evaluation shows that for short utterances, the proposed model achieves a MOS of 4.213 compared to ground-truth with MOS of 4.307; and for long utterances, it also produces high-quality speech with a MOS of 4.201 compared to ground-truth with MOS of 4.360.
v2e: From Video Frames to Realistic DVS Events

To help meet the increasing need for dynamic vision sensor (DVS) event camera data, this paper proposes the v2e toolbox that generates realistic synthetic DVS events from intensity frames. It also clarifies incorrect claims about DVS motion blur and latency characteristics in recent literature. Unlike other toolboxes, v2e includes pixel-level Gaussian event threshold mismatch, finite intensity-dependent bandwidth, and intensity-dependent noise. Realistic DVS events are useful in training networks for uncontrolled lighting conditions. The use of v2e synthetic events is demonstrated in two experiments. The first experiment is object recognition with N-Caltech 101 dataset. Results show that pretraining on various v2e lighting conditions improves generalization when transferred on real DVS data for a ResNet model. The second experiment shows that for night driving, a car detector trained with v2e events shows an average accuracy improvement of 40% compared to the YOLOv3 trained on intensity frames.
Preparation and physicochemical characterizations of solid lipid nanoparticles containing DOTAP$^®$ for DNA delivery

The aim of this study was to prepare and evaluate stable cationic solid lipid nanoparticles (SLNs) as colloidal carriers for gene therapy. SLNs were mainly composed of three different biocompatible and biodegradable matrix lipids called tripalmitin, glyceryl dibehenate, and triglyceride, all containing the cationic lipid $N$-(1-(2,3-dioleoyloxy)propyl)-$N,N,N$-trimethylammonium (DOTAP$^®$). Each of these SLNs were divided into three parts and characterized by applying different processes: Part I was autoclaved (121 $^{\circ}$C, 15 min), Part II was lyophilized (5 mbar, --50 $^{\circ}$C), and Part III was kept in its intact form. These parts were stored at 4 $^{\circ}$C, 25 $^{\circ}$C (room temp.), and 40 $^{\circ}$C for short-term stability tests. The formulations were tested physically regarding the particle size and zeta potential. pUC18 plasmid DNA was used as the genetic material. Zeta potentials of all SLNs and pDNA-SLN complexes were determined to be highly positive (between +28.90 and +59.39). Crystallization processes of lipid matrices were characterized by X-ray diffractometry and differential scanning calorimetry. pDNA binding ability of SLNs and the stability of pDNA-SLN complexes with DNase I enzyme were also determined by gel electrophoresis. It was determined that all formulations became positively charged with DOTAP$^®$. They were able to bind DNA and were partially protective against enzyme degradation. Although additional studies are necessary, this study reveals the promising potential of this gene delivery system for gene therapy.
Towards Explainable and Trustworthy AI for Decision Support in Medicine: An Overview of Methods and Good Practices

Artificial Intelligence (AI) is defined as intelligence exhibited by machines, such as electronic computers. It can involve reasoning, problem solving, learning and knowledge representation, which are mostly in focus in the medical domain. Other forms of intelligence, including autonomous behavior, are also parts of AI. Data driven methods for decision support have been employed in the medical domain for some time. Machine learning (ML) is used for a wide range of complex tasks across many sectors of the industry. However, a broader spectrum of AI, including deep learning (DL) as well as autonomous agents, have been recently gaining more focus and have risen expectation for solving numerous problems in the medical domain. A barrier towards AI adoption, or rather a concern, is trust in AI, which is often hindered by issues like lack of understanding of a black-box model function, or lack of credibility related to reporting of results. Explainability and interpretability are prerequisites for the development of AI-based systems that are lawful, ethical and robust. In this respect, this paper presents an overview of concepts, best practices, and success stories, and opens the discussion for multidisciplinary work towards establishing trustworthy AI.
GRASP Combined with ILS for the Vehicle Routing Problem with Time Windows, Precedence, Synchronization and Lunch Break Constraints

In this era of pandemic especially with COVID-19, many hospitals and care structures are at full capacity regarding availability of beds. This problem leads to ensure giving specific cares to people in need either in illness or disability in their own homes. Home Health Care (HHC) proposes this kind of services for patients demanding it. These services have to be done at the request of the patient which appears to be the client in a way that gives satisfaction to the requester of the service. Often, these demands are bound by a specific time that the workforce (caregivers) are obligated to respect in addition to the precedence (priority) constraint. The main purpose of the HHC structures is to provide a service that is good in term of quality, minimize the overall costs and shorten the losses. To reduce the costs of these HHC structures, it is mandatory to find comprehensible and logical ways to do it, for it is not permissible to touch the caregiver salary, HHC structures find themselves in the obligation to optimize by other means such as reducing the travel cost. Note that these structures give cares in one's home, which means that the travel aspect is important and is considered the core spending charges of the institution. Another fact is the satisfaction of the patients toward caregivers;this is an essential element to optimize in order to obtain a good quality service, to give a realistic aspect for the problem the lunch break of caregivers is introduced as a parameter. For those arguments, a conception of an efficient planning of caregivers involves using decision tools and optimization methods. A caregiver (vehicle) is attributed to a patient (customer) to do a number of cares with several options in accordance to the customer wishes like time windows requirement often specified by the client, the priority or precedence constraints are usually performed if a care have to be performed before another and could need the intervention of more than one caregiver and must have at least one lunch break a day and it is not always taken at a set time of the day and must be versatile to optimize customer demand satisfaction. To resolve this issue which is called VRPTW-SPLB, a mathematical model of the problem is proposed and explained as a Mixed Integer Linear Programming (MILP) and a greedy heuristic based on a Greedy Randomized Adaptive Procedure (GRASP) is proposed, two strategies based on local search and two metaheuristics, and a metaheuristic resultant of an hybridization of the two metaheuristics. At the end of the paper, results are shown on a benchmark extracted from the literature. © 2021. All Rights Reserved.
A-Polynomials of Fillings of the Whitehead Sister

Knots obtained by Dehn filling the Whitehead sister link include some of the smallest volume twisted torus knots. Here, using results on A-polynomials of Dehn fillings, we give formulas to compute the A-polynomials of these knots. Our methods also apply to more general Dehn fillings of the Whitehead sister.
Wave packets in QFT: Leading order width corrections to decay rates and clock behavior under Lorentz boosts

Decay rates in quantum field theory (QFT) are typically calculated assuming the particles are represented by momentum eigenstates (i.e. plane waves). However, strictly speaking, localized free particles should be represented by wave packets. This yields width corrections to the decay rate and to the clock behaviour under Lorentz boosts. We calculate the decay rate of a particle of mass M modeled as a Gaussian wavepacket of width a and centered at zero momentum. We find the decay rate to be Γ0 [ 1− 3a 4M +O (
Atomic Layer Deposition of Osmium

Growth of osmium thin films and nanoparticles by atomic layer deposition is described. The Os thin films were successfully grown between 325 and 375 °C using osmocene and molecular oxygen as precursors. The films consisted of only Os metal as osmium oxides were not detected in X-ray diffraction measurements. Also the impurity contents of oxygen, carbon, and hydrogen were less than 1 at % each at all deposition temperatures. The long nucleation delay of the Os process facilitates either Os nanoparticle or thin film deposition. However, after the nucleation delay of about 350 cycles the film growth proceeded linearly with increasing number of deposition cycles. Also conformal growth of Os thin films on three-dimensional (3-D) structures was confirmed.
How Cross-Link Numbers Shape the Large-Scale Physics of Cytoskeletal Materials

Cytoskeletal networks are the main actuators of cellular mechanics, and a foundational example for active matter physics. In cytoskeletal networks, motion is generated on small scales by filaments that push and pull on each other via molecular-scale motors. These local actuations give rise to large-scale stresses and motion. To understand how microscopic processes can give rise to self-organized behavior on larger scales it is important to consider what mechanisms mediate long-ranged mechanical interactions in the systems. Two scenarios have been considered in the recent literature. The first scenario is systems that are relatively sparse, in which most of the large-scale momentum transfer is mediated by the solvent in which cytoskeletal filaments are suspended. The second scenario is systems in which filaments are coupled via cross-link molecules throughout. Here, we review the differences and commonalities between the physics of these two regimes. We also survey the literature for the numbers that allow us to place a material within either of these two classes.
Hall instability: origin, properties and asymptotic theory for its tearing mode

Hall instability in electron magnetohydrodynamics is interpreted as the shear-Hall instability driven jointly by helicoidal oscillations and shear in the electron current velocity. This explanation suggests an antiparallel orientation of the background magnetic field and vorticity of the current velocity as the necessary condition for Hall instability. The condition is tested and generally confirmed by numerical computations in plane slab geometry. Unstable eigenmodes are localized in the spatial regions of the antiparallel field and vorticity. Computations of the tearing-type mode of the instability are complemented by (and generally agree with) asymptotic analytical estimations for large Hall numbers. The stabilizing effect of perfect conductor boundary conditions is found and explained. For large Hall numbers, the growth rates approach the power-law dependence $\sigma \propto B^\alpha \eta ^{1-\alpha }$ on the magnetic field ($B$) and diffusivity ($\eta$). Almost all computations give the power index $\alpha = 3/4$ with one exception of the tearing-type mode with vacuum boundary conditions for which case $\alpha = 2/3$.
Prediction of geophysical properties of rocks on rare well data and attributes of seismic waves by machine learning methods on the example of the Achimov formation

Purpose of this research is to forecast the development of sand bodies in productive sediments based on well log data and seismic attributes. The object of the study is the productive intervals of Achimov sedimentary complex in the part of oil field located in Western Siberia. The research shows a technological stack of machine learning algorithms, methods for enriching the source data with synthetic ones and algorithms for creating new features. The result was the model of regression relationship between the values of natural radioactivity of rocks and seismic wave field attributes with an acceptable prediction quality. Acceptable quality of the forecast is confirmed both by model cross validation, and by the data obtained following the results of new well.
The Role of Facultative Anaerobic Micro-Organisms in Anaerobic Biodegradation of Chlorophenols

Pure cultures of facultative anaerobic bacteria and yeasts were isolated from anaerobic reactors fed synthetic wastewater which contained eight different chlorophenolic compounds. The concentration of facultative anaerobic micro-organisms was followed in the reactor throughout the experimental period, during which the chlorophenol concentrations were increased and many of the chloronated phenols were removed nearly completely. The most common species were Klebsiella spp. and K. Pneumoniae. Some of the common colonies were tentatively identified as Pseudomonas-like organisms and Aeromonas hydrofila. Methanogenic bacteria in the reactor were mainly acetate users. When the total chlorophenol concentration in the feed was increased from zero to 2.4 or 5.3 mg/1, the concentration of Pseudomonas-like organisms increased, as did the concentration of yeasts. Pure and mixed cultures were tested for use of 2,4,6-trichlorophenol (246-TCP) as a sole carbon and energy source under anaerobic conditions. None of the isolates were able to degrade 246-TCP under anaerobic conditions in pure culture. However, mixed cultures containing only P. aeruginosa and K. oxytoca were able to degrade 246-TCP.
Multi-Goal Reinforcement Learning environments for simulated Franka Emika Panda robot

This technical report presents panda-gym , a set Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym. Five tasks are included: reach, push, slide, pick & place and stack. They all follow a Multi-Goal RL framework, allowing to use goal-oriented RL algorithms. To foster open-research, we chose to use the open-source physics engine PyBullet. The implementation chosen for this package allows to deﬁne very easily new tasks or new robots. This report also presents a baseline of results obtained with state-of-the-art model-free off-policy algorithms. panda-gym is open-source at https://github.com/qgallouedec/panda-gym .
Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations

Recent advances in whole‐slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence‐based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilise information embedded in pathology WSIs beyond what can be obtained through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue, and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms that are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well‐defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large‐scale annotation exercise involving a multidisciplinary team of pathologists, ML experts, and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real‐world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary, and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
A mechanistic-based data-driven approach to accelerate structural topology optimization through finite element convolutional neural network (FE-CNN)

In this paper, a mechanistic data-driven approach is proposed to accelerate structural topology optimization, employing an in-house developed finite element convolutional neural network (FE-CNN). Our approach can be divided into two stages: offline training, and online optimization. During offline training, a mapping function is built between high and low resolution representations of a given design domain. The mapping is expressed by a FE-CNN, which targets a common objective function value (e.g., structural compliance) across design domains of differing resolutions. During online optimization, an arbitrary design domain of high resolution is reduced to low resolution through the trained mapping function. The original high-resolution domain is thus designed by computations performed on only the low-resolution version, followed by an inverse mapping back to the high-resolution domain. Numerical examples demonstrate that this approach can accelerate optimization by up to an order of magnitude in computational time. Our proposed approach therefore shows great potential to overcome the curse-of-dimensionality incurred by density-based structural topology optimization. The limitation of our present approach is also discussed.
Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse, which mainly arise from a lack of diversity in their adversarial interactions. Co-evolutionary GAN (CoE-GAN) training algorithms have shown to be resilient to these pathologies. This article introduces Mustangs, a spatially distributed CoE-GAN, which fosters diversity by using different loss functions during the training. Experimental analysis on MNIST and CelebA demonstrated that Mustangs trains statistically more accurate generators.
SnakeLines: integrated set of computational pipelines for sequencing reads

Abstract With the rapid growth of massively parallel sequencing technologies, still more laboratories are utilising sequenced DNA fragments for genomic analyses. Interpretation of sequencing data is, however, strongly dependent on bioinformatics processing, which is often too demanding for clinicians and researchers without a computational background. Another problem represents the reproducibility of computational analyses across separated computational centres with inconsistent versions of installed libraries and bioinformatics tools. We propose an easily extensible set of computational pipelines, called SnakeLines, for processing sequencing reads; including mapping, assembly, variant calling, viral identification, transcriptomics, and metagenomics analysis. Individual steps of an analysis, along with methods and their parameters can be readily modified in a single configuration file. Provided pipelines are embedded in virtual environments that ensure isolation of required resources from the host operating system, rapid deployment, and reproducibility of analysis across different Unix-based platforms. SnakeLines is a powerful framework for the automation of bioinformatics analyses, with emphasis on a simple set-up, modifications, extensibility, and reproducibility. The framework is already routinely used in various research projects and their applications, especially in the Slovak national surveillance of SARS-CoV-2.