Miracle-dz/UniFAR-FTUs huggingface dataset:
Line Drawings from 3D Models

This tutorial describes the geometry and algorithms for generating line drawings from 3D models, focusing on occluding contours. The geometry of occluding contours on meshes and on smooth surfaces is described in detail, together with algorithms for extracting contours, computing their visibility, and creating stylized renderings and animations. Exact methods and hardware-accelerated fast methods are both described, and the trade-offs between different methods are discussed. The tutorial brings together and organizes material that, at present, is scattered throughout the literature. It also includes some novel explanations, and implementation tips. A thorough survey of the field of non-photorealistic 3D rendering is also included, covering other kinds of line drawings and artistic shading.
Fragility and anomalous susceptibility of weakly interacting networks

Percolation is a fundamental concept that has brought new understanding of the robustness properties of complex systems. Here we consider percolation on weakly interacting networks, that is, network layers coupled together by much fewer interlinks than the connections within each layer. For these kinds of structures, both continuous and abrupt phase transitions are observed in the size of the giant component. The continuous (second-order) transition corresponds to the formation of a giant cluster inside one layer and has a well-defined percolation threshold. The abrupt transition instead corresponds to the merger of coexisting giant clusters among different layers and is characterized by a remarkable uncertainty in the percolation threshold, which in turns causes an anomalous behavior of the observed susceptibility. We develop a simple mathematical model able to describe this phenomenon, using a susceptibility measure that defines the range where the abrupt transition is more likely to occur. Finite-size scaling analysis in the abrupt region supports the hypothesis of a genuine first-order phase transition.
Psychometrické ověření české verze Škály vnímaného stresu

Recently, a great attention has been devoted to stress. Thus, it is worth to have a simple and reliable tool measuring the current level of perceived stress. The aim of this paper is to present a validation of the Czech version of the Perceived Stress Scale which was developed by Cohen in 1983 and is a widely used instrument in this field. Verification of the questionnaire containing 10 items was carried out on a group of 358 university students. The statistical analysis included besides other things determination of the internal consistency and confirmatory factor analysis. The Czech version exhibited a high level of internal consistency with Cronbach ́s alpha of 0.871. Confirmatory factor analysis revealed a two-factor structure preferred also in the literature and showed adequate values of both absolute and relative fit indices as well as indices of parsimony. The Czech version of the Perceived Stress Scale is valid and reliable instrument enabling measurement of perceived stress.
Effects of vitamin D3 supplementation on cognition and blood lipids: a 12-month randomised, double-blind, placebo-controlled trial

Alzheimer’s disease (AD) is the leading cause of dementia and loss of autonomy and independency in the elderly.1 Currently, no effective pharmacological interventions change the onset or progression of AD.2 Mild cognitive impairment (MCI) is possibly the earliest stage of detectable dementia and may be the optimal time to intervene.3 4 Exploring factors and biochemical markers that are associated with late-life dementia has attracted much attention. A growing body of epidemiological evidence has suggested that deficiency of nutrition components may be related to the development of cognitive decline.5 Vitamin D can play an important role in brain health and function, and exert various neuroprotective effects in brain areas essential for cognition.6 Being severely deficient in vitamin D is associated with a more than twofold increased risk of developing dementia.7 Protective effects of vitamin D on cognition and pathology are also concluded in animal models.8 9Sonnenberg et al 9 found that 1,25-dihydroxyvitamin D (1,25-D) treatment increases choline acetyltransferase activity in specific rat brain nuclei. Supplementation of vitamin D3 may have contributed to the observed improvements to memory via increasing acetylcholine concentration. Vitamin D research has gained increased attention in recent times due to its roles beyond bone health and calcium homeostasis, such as neurocognitive.10 However, results have been inconclusive.1 11 The mechanisms still remain unclear. The brain is the most lipid-rich organ in the body,12 and almost all major classes of lipids have some correlation with AD pathogenesis.13 Several population-based studies reported that elderly people with AD or with dementia or cognitive impairment have higher plasma total cholesterol (TC) than sex and age-matched non-demented peers.14–16 Brain and peripheral lipid levels were once thought to be entirely isolated from one another by the blood–brain barrier, but recent evidence …
Reverse Engineering the 1-Month Lupron Depot®

The 1-month Lupron Depot® (LD) encapsulating water-soluble leuprolide in poly(lactic-co-glycolic acid) (PLGA) microspheres is a benchmark product upon which modern long-acting release products are often compared. Despite expiration of patent coverage, no generic product for the LD has been approved in the USA, likely due to the complexity of components and manufacturing processes involved in the product. Here, we describe the reverse engineering of the LD composition and important product attributes. Specific attributes analyzed for microspheres were as follows: leuprolide content by three methods; gelatin content, type, and molecular weight distribution; PLGA content, lactic acid/glycolic acid ratio, and molecular weight distribution; mannitol content; in vitro drug release; residual solvent and moisture content; particle size distribution and morphology; and glass transition temperature. For the diluent, composition, viscosity, and specific gravity were analyzed. Analyzed contents of the formulation and the determined PLGA characteristics matched well with the official numbers stated in the package insert and those found in literature, respectively. The gelatin was identified as type B consistent with ~ 300 bloom. The 11-μm volume-median microspheres in the LD slowly released the drug in vitro in a zero-order manner after ~ 23% initial burst release. Very low content of residual moisture (< 0.5%) and methylene chloride (< 1 ppm) in the product indicates in-water drying is capable of removing solvents to extremely low levels during manufacturing. The rigorous approach of reverse engineering described here may be useful for development of generic leuprolide-PLGA microspheres as well as other new and generic PLGA microsphere formulations.
Fusion of Monocular Vision and Radio-based Ranging for Global Scale Estimation and Drift Mitigation

Monocular vision-based Simultaneous Localization and Mapping (SLAM) is used for various purposes due to its advantages in cost, simple setup, as well as availability in the environments where navigation with satellites is not effective. However, camera motion and map points can be estimated only up to a global scale factor with monocular vision. Moreover, estimation error accumulates over time without bound, if the camera cannot detect the previously observed map points for closing a loop. We propose an innovative approach to estimate a global scale factor and reduce drifts in monocular vision-based localization with an additional single ranging link. Our method can be easily integrated with the back-end of monocular visual SLAM methods. We demonstrate our algorithm with real datasets collected on a rover, and show the evaluation results.
Inference Over Programs That Make Predictions

This abstract extends on the previous work (arXiv:1407.2646, arXiv:1606.00075) on program induction using probabilistic programming. It describes possible further steps to extend that work, such that, ultimately, automatic probabilistic program synthesis can generalise over any reasonable set of inputs and outputs, in particular in regard to text, image and video data.
Note on the Estimation of Embedded Hermitian Gaussian Graphical Models for MEEG Source Activity and Connectivity Analysis in the Frequency Domain. Part I: Single Frequency Component and Subject

This technical note presents an inference framework for hierarchically conditioned (embedded) Hermitian Gaussian Graphical Models (GGM). Our methodology, although extendible to several GGMs applications, is mainly centered on the specific neuroscientific context encompassing the estimation of brain sources activity and connectivity from Magnetoencephalography or Electroencephalography (MEEG) signals. This will be conceived in a series of papers with two parts. Part I: Analysis of a single frequency component from a single subject. Part II: Analysis of the Cross-Spectra for multiple subjects in a population. The strategy is based on Maximum Posterior Analysis, via the Expectation Maximization (EM) algorithm, of the Hermitian GGMs Parameters and Hyperparameters. The Hyperparameters set, which comprises MEEG signal noise covariance and source precision (connectivity) matrices of the MEEG generative model, is informed with Priors of special interest in the field and whose implementation in GGM estimation still poses a technical challenge. The theory presented here encapsulates two essential contributions. First: The formulation of a target function for the connectivity estimator that resembles an effective GGM of source activity, which is merged to the maximization step of the iterative EM strategy. Second: A new procedure for the estimation of the source activity effective GGM. These theoretical contributions provide for an explicit and highly optimal connectivity estimation procedure.
Unified IoT Platform Architecture Platforms as Major IoT Building Blocks

Ad-hoc approaches to building Internet of Things (IoT) solutions in the past had mostly failed. The sheer complexity of managing a mesh of distributed interconnected devices was unbearable. The appearance of IoT platforms has enabled the proliferation of successful products in this area. A full-scale, comprehensive IoT platform embraces the following three major capabilities: application enablement, data storage, and connectivity management. This paper presents an overview of technologies and techniques to realize such an IoT platform. The focus is on the platform that unifies edge and cloud computing. The paper refers to Mainflux as such reference example. Mainflux serves as a software infrastructure, that is built around the microservices architectural style, for development of IoT solutions, and deployment of intelligent products. It is conceived as an opensource project, and is already used in production settings. Therefore, the paper describes a real product, whose chosen set of methods, technologies and techniques are feasible and effective in crafting IoT systems. The Mainflux project contains an extensive set of automated tests, that could serve as an additional documentation regarding its usage.
A Nonoutsourceable Puzzle Under GHOST Rule

Blockchain technology has attracted a lot of attention in recent years. Applications of blockchain are not only restricted to cybercurrencies, but have also been extended to other areas such as finance, e-health, music, and other business. One of the key components of blockchain is the design for miners who are responsible for adding new transactions (blocks) by solving a puzzle and receive some rewards in return. As a result, miners tend to join centralized mining pools to outsource their computing resources in order to gain more steady rewards, which may affect the security and fairness of the system. This motivates the researchers to propose nonoutsourceable puzzles. However, existing nonoutsourceable puzzles do not work well under the high-rate transaction processing protocol (GHOST). In this paper, we propose the first nonoutsourceable puzzle that can satisfy all security requirements of GHOST. Our experimental results show that our puzzle is practical.
w‐Type ions formed by electron transfer dissociation of Cys‐containing peptides investigated by infrared ion spectroscopy

Abstract In mass spectrometry‐based peptide sequencing, electron transfer dissociation (ETD) and electron capture dissociation (ECD) have become well‐established fragmentation methods complementary to collision‐induced dissociation. The dominant fragmentation pathways during ETD and ECD primarily involve the formation of c‐ and z •‐type ions by cleavage of the peptide backbone at the N─Cα bond, although neutral losses from amino acid side chains have also been observed. Residue‐specific neutral side chain losses provide useful information when conducting database searching and de novo sequencing. Here, we use a combination of infrared ion spectroscopy and quantum‐chemical calculations to assign the structures of two ETD‐generated w‐type fragment ions. These ions are spontaneously formed from ETD‐generated z •‐type fragments by neutral loss of 33 Da in peptides containing a cysteine residue. Analysis of the infrared ion spectra confirms that these z •‐ions expel a thiol radical (SH•) and that a vinyl C═C group is formed at the cleavage site. z •‐type fragments containing a Cys residue but not at the cleavage site do not spontaneously expel a thiol radical, but only upon additional collisional activation after ETD.
Engineered Small-Molecule Control of Influenza A Virus Replication

Influenza A virus (IAV) causes significant morbidity and mortality annually worldwide, despite the availability of new formulations of the vaccine each season. There is a critical need to develop more-efficacious vaccines. However, testing novel vaccines in the human population in controlled studies is difficult due to the limited availability and efficacy of intervention strategies should the vaccine fail. There are also significant safety concerns for work with highly pathogenic IAV strains in the laboratory. Therefore, novel strategies are needed to improve the safety of vaccine studies and of research on highly pathogenic IAV. In this study, we developed an IAV strain engineered to contain a small-molecule-mediated safety switch. This tag, when attached to an essential viral protein, allows for the regulation of IAV replication in vitro and in vivo. This strategy provides a platform for the regulation of virus replication without targeting viral proteins directly. ABSTRACT Influenza A virus (IAV) remains a global health concern despite the availability of a seasonal vaccine. It is difficult to predict which strains will circulate during influenza season, and therefore, it is extremely challenging to test novel vaccines in the human population. To overcome this obstacle, new vaccines must be tested in challenge studies. This approach poses significant safety problems, since current pharmacological interventions for IAV are poorly efficacious. New methods are needed to enhance the safety of these challenge studies. In this study, we have generated a virus expressing a small-molecule-assisted shutoff (SMASh) tag as a safety switch for IAV replication. The addition of the SMASh tag to an essential IAV protein allows for small-molecule-mediated inhibition of replication. Treatment with this drug controls the replication of a SMASh-tagged virus in vitro and in vivo. This model for restriction of viral replication has potential for broad applications in vaccine studies, virotherapy, and basic virus research. IMPORTANCE Influenza A virus (IAV) causes significant morbidity and mortality annually worldwide, despite the availability of new formulations of the vaccine each season. There is a critical need to develop more-efficacious vaccines. However, testing novel vaccines in the human population in controlled studies is difficult due to the limited availability and efficacy of intervention strategies should the vaccine fail. There are also significant safety concerns for work with highly pathogenic IAV strains in the laboratory. Therefore, novel strategies are needed to improve the safety of vaccine studies and of research on highly pathogenic IAV. In this study, we developed an IAV strain engineered to contain a small-molecule-mediated safety switch. This tag, when attached to an essential viral protein, allows for the regulation of IAV replication in vitro and in vivo. This strategy provides a platform for the regulation of virus replication without targeting viral proteins directly.
Unbiased Analysis of Item-Specific Multi-Voxel Activation Patterns Across Learning

Recent work has highlighted that multi-voxel pattern analysis (MVPA) can be severely biased when BOLD response estimation involves systematic imbalance in model regressor correlations. This problem occurs in situations where trial types of interest are temporally dependent and the associated BOLD activity overlaps. For example, in learning paradigms early and late learning stage trials are inherently ordered. It has been shown empirically that MVPAs assessing consecutive learning stages can be substantially biased especially when stages are closely spaced. Here, we propose a simple technique that ensures zero bias in item-specific multi-voxel activation patterns for consecutive learning stages with stage being defined by the incremental number of individual item occurrences. For the simpler problem, when MVPA is computed irrespective of learning stage over all item occurrences within a trial sequence, our results confirm that a sufficiently large, randomly selected subset of all possible trial sequence permutations ensures convergence to zero bias – but only when different trial sequences are generated for different subjects. However, this does not help to solve the harder problem to obtain bias-free results for learning-related activation patterns regarding consecutive learning stages. Randomization over all item occurrences fails to ensure zero bias when the full trial sequence is retrospectively divided into item occurrences confined to early and late learning stages. To ensure bias-free MVPA of consecutive learning stages, trial-sequence randomization needs to be done separately for each consecutive learning stage.
Prediction of Heart Disease Using Machine Learning

with the rampant increase in the heart stroke rates at juvenile ages, we need to put a system in place to be able to detect the symptoms of a heart stroke at an early stage and thus prevent it. It is impractical for a common man to frequently undergo costly tests like the ECG and thus there needs to be a system in place which is handy and at the same time reliable, in predicting the chances of a heart disease. Thus we propose to develop an application which can predict the vulnerability of a heart disease given basic symptoms like age, sex, pulse rate etc. The machine learning algorithm neural networks has proven to be the most accurate and reliable algorithm and hence used in the proposed system.
Additive Scrambling PAPR Reduction Scheme in BPSK-OFDM Systems

OFDM is a multicarrier transmission technique with high data rate, but one of its important shortcomings is the large PAPR of OFDM signals. Among the existing PAPR reduction methods, SLM is very attractive because of its favorable performance in PAPR reduction. This paper proposes an additive scrambling PAPR reduction scheme for OFDM systems employing BPSK. In proposed additive scrambling scheme, a set of additive scrambling sequences are generated in the first place. Then, by adding each additive scrambling sequence to the original OFDM signal, a set of OFDM candidate signals are gained. Finally, the candidate sequence with the minimum PAPR is chosen. This proposed additive scrambling scheme earns considerable complexity reduction and produce comparable PAPR reduction performance compared with SLM scheme.
Learning local features for medical image classification

In this paper, we propose a novel patch based approach for medical image classification. Spherical symmetric 3D local ternary pattern is used to extract the multi-scale multidirectional local features from the sample patches extracted from MRI scans of brain. Artificial neural networks follows this robust feature extraction to learn the features of region of interest (ROI) and labels it into normal or abnormal ROI. Two benchmark medical databases namely: OASIS, BRATS 2012 have been used to analyse the robustness of proposed approach for medical image classification. Performance evaluation of the system has been carried out using recall, precision and F-score. Experimental analysis shows that integration of local features and ANN outperforms the other existing methods for medical image classification.
Inconsistent reproductive isolation revealed by interactions between Catostomus fish species

Interactions between species are central to evolution and ecology, but we do not know enough about how outcomes of interactions between species vary across geographic locations, in heterogeneous environments, or over time. Ecological dimensions of interactions between species are known to vary, but evolutionary interactions such as the establishment and maintenance of reproductive isolation are often assumed to be consistent across instances of an interaction between species. Hybridization among Catostomus fish species occurs over a large and heterogeneous geographic area and across taxa with distinct evolutionary histories, which allows us to assess consistency in species interactions. We analyzed hybridization among six Catostomus species across the Upper Colorado River basin (US mountain west) and found extreme variation in hybridization across locations. Different hybrid crosses were present in different locations, despite similar species assemblages. Within hybrid crosses, hybridization varied from only first generation hybrids to extensive hybridization with backcrossing. Variation in hybridization outcomes might result from uneven fitness of hybrids across locations, polymorphism in genetic incompatibilities, chance, unidentified historical contingencies, or some combination thereof. Our results suggest caution in assuming that one or a few instances of hybridization represent all interactions between the focal species, as species interactions vary substantially across locations.
Learning sparse optimal rule fit by safe screening

In this paper, we consider linear prediction models in the form of a sparse linear combination of rules, where a rule is an indicator function defined over a hyperrectangle in the input space. Since the number of all possible rules generated from the training dataset becomes extremely large, it has been difficult to consider all of them when fitting a sparse model. In this paper, we propose Safe Optimal Rule Fit (SORF) as an approach to resolve this problem, which is formulated as a convex optimization problem with sparse regularization. The proposed SORF method utilizes the fact that the set of all possible rules can be represented as a tree. By extending a recently popularized convex optimization technique called safe screening, we develop a novel method for pruning the tree such that pruned nodes are guaranteed to be irrelevant to the prediction model. This approach allows us to efficiently learn a prediction model constructed from an exponentially large number of all possible rules. We demonstrate the usefulness of the proposed method by numerical experiments using several benchmark datasets.