Miracle-dz/UniFAR-FTUs huggingface dataset:
Love Styles and Cardiovascular Responder Types

We examined the relationship between cardiovascular responder types and love styles. Cardiovascular responder types were measured as reactivity to a social speech task. Measures included heart rate (HR), systolic and diastolic blood pressure (SBP and DBP, respectively), pre-ejection period (PEP), and baroreceptor sensitivity (BRS). Love styles were measured using the 2nd version of Lee's Type Scale; a questionnaire developed based on Lee's color theory. Analyses revealed that Eros (physically attracted love) score was related to HR, SBP, and BRS reactivity, and that Mania (obsessive and jealous love) score was related to HR and BRS reactivity. These results suggest that passionate love style, including Eros and Mania, is associated with cardiovascular responder types.
Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms

The rapid development of light detection and ranging (Lidar) provides a promising way to obtain three-dimensional (3D) phenotype traits with its high ability of recording accurate 3D laser points. Recently, Lidar has been widely used to obtain phenotype data in the greenhouse and field with along other sensors. Individual maize segmentation is the prerequisite for high throughput phenotype data extraction at individual crop or leaf level, which is still a huge challenge. Deep learning, a state-of-the-art machine learning method, has shown high performance in object detection, classification, and segmentation. In this study, we proposed a method to combine deep leaning and regional growth algorithms to segment individual maize from terrestrial Lidar data. The scanned 3D points of the training site were sliced row and row with a fixed 3D window. Points within the window were compressed into deep images, which were used to train the Faster R-CNN (region-based convolutional neural network) model to learn the ability of detecting maize stem. Three sites of different planting densities were used to test the method. Each site was also sliced into many 3D windows, and the testing deep images were generated. The detected stem in the testing images can be mapped into 3D points, which were used as seed points for the regional growth algorithm to grow individual maize from bottom to up. The results showed that the method combing deep leaning and regional growth algorithms was promising in individual maize segmentation, and the values of r, p, and F of the three testing sites with different planting density were all over 0.9. Moreover, the height of the truly segmented maize was highly correlated to the manually measured height (R2> 0.9). This work shows the possibility of using deep leaning to solve the individual maize segmentation problem from Lidar data.
Power and delay analysis of one bit adders

Power and delay parameters are very important factors to analyze performance of any circuit. In this paper different types of one bit adders are analyzed using different methodologies. The Full Swing Gate Diffusion Input (FSGDI) methodology is used here to implement different design styles of one bit full adder at 180 nm CMOS technology and compared with previous basic and standard methods. The FSGDI method is based on Shannon expansion method for low-power design in combinational logic circuits. The FSGDI methodology shows full functionality and robustness under global and local process variations at wide range of supply voltages. Four different design styles of one bit full adder is implemented using FSGDI technique in 180 nm technology in cadence virtuoso EDA tool. The FSGDI technique is analyzed and compared with standard CMOS technique and basic GDI technique that uses buffer with multiple threshold voltage. Simulation result shows that the full adder using FSGDI gates saves 37% power and 30% delay as compare to standard CMOS technique. Also this design saves 24% power and 86% delay compare to multiple Vth GDI technique.
Imputation of missing wind speed data based on low-rank matrix approximation

Wind speed data usually contain missing values due to data observing device failure, incorrect data recording, and so on. Using an effective method to impute the missing and error wind speed data before predicting the future wind speed is necessary. The traditional methods are based on simple statistical models or complex machine learning models. In this paper, the method based on low-rank matrix approximation is introduced for imputing missing wind speed data. Firstly, wind speed data are converted into a low-rank matrix form. Then a low-rank matrix approximation named Grouse algorithm is used on the wind speed matrix which contains missing values. Finally, the missing values in wind speed data are imputed by the approximate matrix. This study is carried on the real wind speed data obtained in Saihanba. Compared with the three traditional methods: mean, multiple imputation, and k nearest neighbors, experiment results show that our method imputes more accurately than other methods in dealing with missing and incorrect wind speed data.
Conditions for Major Transitions in Biological and Cultural Evolution

Evolution by natural selection can be seen an algorithm for generating creative solutions to difficult problems. More precisely, evolution by natural selection is a class of algorithms that share a set of properties. The question we address here is, what are the conditions that define this class of algorithms? There is a standard answer to this question: Briefly, the conditions are variation, heredity, and selection. We agree that these three conditions are sufficient for a limited type of evolution, but they are not sufficient for open-ended evolution. By open-ended evolution, we mean evolution that generates a continuous stream of creative solutions, without stagnating. We propose a set of conditions for open-ended evolution. The new conditions build on the standard conditions by adding fission, fusion, and cooperation. We test the proposed conditions by applying them to major transitions in the evolution of life and culture. We find that the proposed conditions are able to account for the major transitions.
STUDYING THERMAL EFFECTS OF LASER ON TISSUE USING IMPLICIT FINITE VOLUME METHOD

In this paper, thermal effects of laser irradiance on biological tissue is investigated using computer simulations. Earlier attempts in this field made use of finite difference and finite element techniques. Here a novel approach is adopted to improve the results. The effect of our implicit approach on the convergence procedure and accuracy of results, with different timing steps, is explored. Monte Carlo method is used in combination with the finite volume algorithm in order to obtain a profile of light distribution and heat transport in tissue. It is shown that implicit finite volume technique has not only acceptable accuracy, but also high stability for different timing steps.
A new classification scheme for periodontal and peri‐implant diseases and conditions – Introduction and key changes from the 1999 classification

A classification scheme for periodontal and peri-implant diseases and conditions is necessary for clinicians to properly diagnose and treat patients as well as for scientists to investigate etiology, pathogenesis, natural history, and treatment of the diseases and conditions. This paper summarizes the proceedings of the World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions. The workshop was co-sponsored by the American Academy of Periodontology (AAP) and the European Federation of Periodontology (EFP) and included expert participants from all over the world. Planning for the conference, which was held in Chicago on November 9 to 11, 2017, began in early 2015. An organizing committee from the AAP and EFP commissioned 19 review papers and four consensus reports covering relevant areas in periodontology and implant dentistry. The authors were charged with updating the 1999 classification of periodontal diseases and conditions and developing a similar scheme for peri-implant diseases and conditions. Reviewers and workgroups were also asked to establish pertinent case definitions and to provide diagnostic criteria to aid clinicians in the use of the new classification. All findings and recommendations of the workshop were agreed to by consensus. This introductory paper presents an overview for the new classification of periodontal and peri-implant diseases and conditions, along with a condensed scheme for each of four workgroup sections, but readers are directed to the pertinent consensus reports and review papers for a thorough discussion of the rationale, criteria, and interpretation of the proposed classification. Changes to the 1999 classification are highlighted and discussed. Although the intent of the workshop was to base classification on the strongest available scientific evidence, lower level evidence and expert opinion were inevitably used whenever sufficient research data were unavailable. The scope of this workshop was to align and update the classification scheme to the current understanding of periodontal and peri-implant diseases and conditions. This introductory overview presents the schematic tables for the new classification of periodontal and peri-implant diseases and conditions and briefly highlights changes made to the 1999 classification. It cannot present the wealth of information included in the reviews, case definition papers, and consensus reports that has guided the development of the new classification, and reference to the consensus and case definition papers is necessary to provide a thorough understanding of its use for either case management or scientific investigation. Therefore, it is strongly recommended that the reader use this overview as an introduction to these subjects. Accessing this publication online will allow the reader to use the links in this overview and the tables to view the source papers (Table 1).
Novel development of fast processing algorithm for the moving object detection in RT videos using histogram orientation gradient method

Moving object detection in RT videos tracking is an important research area in the field of surveillance mission especially in the high security zones & defense areas. Despite a lot of progress in the field, visual tracking remains a difficult problem due to many challenges as this involves lot of memory, computations & fast processing algorithms. In this research paper, we present a novel methodology of fast processing algorithm for the moving object detection in RT videos using histogram orientation gradient method and using support vector machines. Two cases of videos are being considered for the simulation purposes. The simulation results shows the effectiveness of the methodology adopted by us in the field of video processing of dynamic objects.
Chaos Theory meets deep learning : On Lyapunov exponents and adversarial perturbations

In this paper, we would like to disseminate a serendipitous discovery involving Lyapunov exponents of a 1-D time series and their use in serving as a filtering defense tool against a specific kind of deep adversarial perturbation. To this end, we use the state-of-the-art CleverHans library to generate adversarial perturbations against a standard Convolutional Neural Network (CNN) architecture trained on the MNIST dataset. We empirically demonstrate how the Lyapunov exponents computed on the flattened 1-D vector representations of the images served as highly discriminative features that could be to pre-classify images as adversarial or legitimate before feeding the image into the CNN for classification. 1. Background on defenses against adversarial attacks In the recent past, a plethora of defenses against adversarial attacks have been proposed. These include SafetyNet [12], adversarial training [19], label smoothing [20], defensive distillation [17] and feature-squeezing [21, 22] to name a few. There is also an ongoing Kaggle contest[2] underway for exploring novel defenses against adversarial attacks. As evinced by the recent spurt in the papers written on this topic, most defenses proposed are quelled by a novel attack that exploits some weakness in the defense. In [10], the authors queried if one could concoct a strong defense by combining multiple defenses and showed that an ensemble of weak defenses was not sufficient in providing strong defense against adversarial examples that they were able to craft. With this background, we shall now look more closely at a specific type of defense and motivate the relevance of our method within this framework. 1.1. The pre-detector based defenses One prominent approach that emerges in the literature of adversarial defenses is that of crafting pre-detection and filtering systems that flag inputs that might be potentially adversarial. In [9], the authors posit that adversarial examples are not drawn from the same distribution as the legitimate samples and can thus be detected using statistical tests. In [14, 7], the authors train a separate binary classifier to first classify any input image as legitimate or adversarial and then perform inference on the passed images. In approaches such as [6], the authors assume that DNNs classify accurately only near the small manifold of training data and that the synthetic adversarial samples do not lie on the data manifold. They apply dropout at test time to ascertain the confidence of adversariality of the input image. In this paper, we would like to disseminate a modelagnostic approach towards adversarial defense that is dependent purely on the quasi-time-series statistics of the input images that was discovered in a rather serendipitous fashion. The goal is to not present the method we propose as a fool-proof adversarial filter, but to instead draw the attention of the DNN and CV communities towards this chance discovery that we feel is worthy of further inquiry.
Analysis of protein landscapes around N-glycosylation sites from the PDB repository for understanding the structural basis of N-glycoprotein processing and maturation

Asparagine-linked glycans (N-glycans) are attached onto nascent glycoproteins in the endoplasmic reticulum (ER) and subsequently processed by a set of processing enzymes in the ER and Golgi apparatus. Accumulating evidence has shown that not all N-glycans on glycoproteins are uniformly processed into mature forms (hybrid and complex types in mammals) through the ER and Golgi apparatus, and a certain set of glycans remains unprocessed as an "immature" form (high-mannose type in mammals). Much attention has been paid to environmental factors regulating N-glycoprotein maturation, such as the expression levels of glycosyltransferases/glycosidases. On the other hand, the influence of the 3D structure of the carrier glycoprotein on N-glycan maturation has been investigated mostly using individual model glycoproteins. To obtain more insights into N-glycoprotein maturation, we herein analyze glycoprotein structures extracted from the Protein Data Bank. We confirm that site-specific N-glycan processing is largely explained by the solvent accessibility of the glycosylated Asn residue and of the conjugated N-glycan. Potential bias of protein structural features toward immature or mature forms was explored within a range of concentric circles of fully folded glycoproteins. There does appear to be bias in amino acid composition and secondary structure. Most notably, γ-branched amino acid residues (Asn+Asp+Leu) occur more frequently on unstructured loop regions of immature glycoproteins. Structural features of the protein surface around the N-glycosylated site do seem to affect N-glycan processing and maturation.
A 16 Year Experience with Modified Lateral Supraorbital Approach for RupturedCerebral Aneurysms

Objectives: Upto now, most authors reported the supraorbital approach for unruptured aneurysm and small and less complex aneurysm. The authors devised the Modified Lateral Supraorbital (MLSO) approach. We present a 16 year experience with MLSO approach for ruptured anterior circulation aneurysms and some parts of posterior circulation aneurysms. Materials and Methods: Aneurysm clipping surgery through the MLSO approach was performed on a total 323 patients. Baseline demographics, operative time, procedural complications, and clinical outcomes at 6 months and 1 year were retrospectively analyzed. Results: For patients treated MLSO approach, 293 had single aneurysms and 30 had multiple aneurysms. The proportion of small, medium, large, and giant aneurysms were 64% (206/323), 31% (101/323), 4.7% (15/323), and 0.3% (1/323). Mean operation time was 219.6 minutes. Procedural complications occurred in 21 cases (6.5%). Five patients died from severe brain swelling and refusal of additional treatment, while 1 patient died from tonsilar herniation due to lumbar catheter drainage. Favorable clinical outcomes, Glasgow Outcome Scale IV or V, were observed in 82.6% (267/293) and 83.6% (270/293) at 6 months and one year follow-up. The MLSO approach has the following advantages: less brain retraction, good surgical field for high positioned A-com or basilar tip aneurysm surgery, short operation time, small skin incision, minimizing temporal muscle manipulation and good cosmetic appearance. Conclusions: The MLSO approach can be considered as a useful approach for neurosurgeons for cases of general ruptured aneurysm, except those with a large amount of intracerebral hemorrhage and severe brain swelling.
Increases in placental nitric oxide, but not nitric oxide‐mediated relaxation, underlie the improvement in placental efficiency and antihypertensive effects of hydrogen sulphide donor in hypertensive pregnancy

Dysregulation of hydrogen sulphide (H2S) producing enzymes has been related to hypertensive pregnancy, and H2S donor, sodium hydrosulphide (NaHS) exerts antihypertensive effects, modulates angiogenic factors production and acts as an antioxidant. Moreover, reduction in nitric oxide (NO) bioavailability is related to hypertensive pregnancy and H2S may interact with NO, modulating its production. We aimed to investigate the NaHS effects in hypertension‐in‐pregnancy and also in feto‐placental parameters. Female Wistar rats (200‐250 g) were mated and desoxycorticosterone acetate injections followed by replacement of water by 0.9% saline solution were used to induce hypertensive pregnancy. Rats were divided into four groups: normal pregnant (Norm‐Preg), pregnant + NaHS (Preg+NaHS), hypertensive pregnant (HTN‐Preg) and HTN‐Preg+NaHS. Systolic blood pressure was increased in HTN‐Preg and this increase was blunted in HTN‐Preg+NaHS. Fetal and placental weights were decreased in HTN‐Preg animals, while fetal growth restriction was improved in HTN‐Preg+NaHS. Placental weight was lower in HTN‐Preg+NaHS than in HTN‐Preg; however, placental efficiency was re‐established in HTN‐Preg+NaHS rats. We observed that a partial contribution of placental NO, but not changes in anti‐angiogenic factors may mediate the increases in placental efficiency in HTN‐Preg+NaHS. HTN‐Preg presented thoracic aorta hyperreactivity to phenylephrine while NaHS treatment blunted this hyperreactivity, which seems not to be related to NO‐mediated relaxation induced by acetylcholine. Therefore, changes in vascular responsiveness promoted by NaHS treatment may underlie the beneficial effects in systolic blood pressure and feto‐placental parameters in our study.
Change in Inflammatory Biomarkers and Adipose Tissue in BRCA1/2+ Breast Cancer Survivors Following a Yearlong Lifestyle Modification Program

Breast cancer survivors who carry a genetic mutation for one of the BRCA genes often undergo surgically induced menopause a decade or more before the usual age of natural menopause. These women are at elevated risk for multiple negative health outcomes, including metabolic diseases, heart disease, and cancer recurrence. Effects of a 12-month commercially available web-based lifestyle program (Precision Nutrition) were tested on body composition and markers of inflammation in a randomized controlled trial. Participants (N = 35) were BRCA1/2+, breast cancer survivors, and had completed surgically induced menopause at age <45 years. Dual-energy X-ray absorptiometry was used to quantify body composition. Fasting blood samples were used to assay insulin, IL1β, IL6, IL8, and TNFα. At baseline, we observed relationships between insulin, TNFα, and IL6, and between biomarkers and adiposity. Insulin and subcutaneous adipose tissue levels significantly decreased following the intervention compared with the change in the control group. Compared with baseline, TNFα and total adipose tissue levels decreased significantly in the intervention group. The percent change in insulin levels was moderately correlated with the percent change in subcutaneous adipose tissue (r = 0.33). Change in adiposity was not related to change in TNFα or IL6. Women in the intervention group decreased levels of subcutaneous, but not visceral, adipose tissue. The change in subcutaneous adipose tissue was the main driver of change in insulin levels for the women in the intervention group. However, the change in body composition achieved by the Precision Nutrition program was not sufficient to alter biomarker levels of inflammation. Cancer Prev Res; 11(9); 545–50. ©2018 AACR.
On the Theoretical and Practical Limits of Digital QSOs

A usual digital communication system consists of hardware and software. In order to reach maximum sensitivity, every hardware component must carefully be tuned to optimal power or lowest noise. This paper deals with the part of the system which is implemented in software on the PC. The theoretical lower bound of digital information transfer is analysed under the special constraints of radio amateur QSOs.
Efekt nízkého rizika: Aplikace na německý akciový trh

Text presentuje parametry deseti portfolii složených dle historickeho rizika v obdobi 1999-2012 na německem akciovem trhu. Portfolia s nizkou volatilitou, resp. nizkým faktorem β, jsou schopny dosahnout stejne/vyssi výkonnosti v porovnani s akciovým trhem při nižsi volatilitě. Nejrizikovějsi akcie naopak zcela propadly ve srovnani s ostatnimi investicemi. Podobne výsledky je možne pozorovat i na rizikově ocistěne bazi.
A novel approach: Reliable and secure data storage and retrieval in a cloud

Cloud Computing, a latest and emerging technology in the current IT field. It is very useful technology which it can store the numerous of data in servers and it can be accessed from anywhere in the world through Internet. Simply, on demand we can access the resources from Internet at anytime and anywhere. It is the latest architecture of IT industry. It works on the software and large databases where the services are not fully trusted. This poses many security challenges which have not been well defined and worked. Here, we develop a system with strong benefits and strong securities. So whenever a customer storing his data on server he is not worry about his data. The system is developed with a mechanism which is highly trustworthy in placing the user data in to cloud server and retrieving by the cloud user.
Investigation of grid supply harmonic effects in wound rotor induction machines

This paper presents an in-depth investigation of the effects of several grid supply harmonic voltages on the stator currents of an example wound rotor induction machine. The observed effects of higher order grid supply harmonics are identified using a finite element time stepping transient model, as well as a time-stepping electromagnetic model. In addition, a number of analytical equations to calculate the spectral content of the stator currents are presented in the paper. The presented equations are validated through comparison with the obtained spectra predicted using the finite element and electromagnetic models. The presented study provides a better understanding of the origin of supply harmonic effects identified in the stator currents of the example wound rotor induction machine. Furthermore, the study helps to understand the effects of higher order supply harmonics on the harmonic emissions of an example wound rotor induction machine.