Miracle-dz/UniFAR-FTUs huggingface dataset:
Cumulative evidence for associations between genetic variants in interleukin 17 family gene and risk of human diseases

Background Genetic association studies have elucidated the link of variants in the interleukin 17 (IL-17) family genes with susceptibility to human diseases, yet have obtained controversial outcomes. Therefore, we sought to update comprehensive synopsis of variants in the IL-17 family genes with susceptibility to human diseases. Methods Our study screened the Pubmed and Web of Science to enroll eligible articles and performed a meta-analysis, then graded the cumulative evidence of significant association using Venice criteria and false-positive report probability test, and finally assessed the function of variants with strong evidence. Results Seven variants in IL-17 family genes had significant relationships with susceptibility to 18 human diseases identified by meta-analyses. Strong evidence was assigned to 4 variants (IL-17A rs2275913, IL-17A rs8193037, IL-17F rs1889570, IL-17F rs763780) with susceptibility to 6 human diseases (lung and cervical cancer, spondyloarthritis, asthma, multiple sclerosis, rheumatoid arthritis), moderate to 2 variants with risk of 5 diseases, weak to 5 variants with risk of 10 diseases. Bioinformatics analysis suggested that the variants with strong evidence might fall in putative functional regions. Additionally, positive relationships for 5 variants with risk of 4 diseases (based on two datasets) and 14 variants with risk of 21 diseases (based on one dataset) were considered noteworthy. Conclusions This study offers updated and comprehensive clues that variants in the IL-17 family genes are significantly linked with susceptibility to cervical, lung cancer, asthma, multiple sclerosis, rheumatoid arthritis and spondyloarthritis, and elucidates the crucial role of the IL-17 regions in the genetic predisposition to cancer or noncancerous diseases.
Real-Time Scene Reconstruction

This report discusses the implementation of a real-time system for scene reconstruction. Algorithms for feature extraction, tracking, structure from motion and mesh generation are introduced. We present experimental results demonstrating the performance of the KLT feature tracker running on GPU. The report touches on the eight point algorithm for structure from motion and a solution to mesh generation, analyzing their fitness for a real-time system.
A two-stage visual tracking algorithm using dual-template

Template matching and updates are crucial steps in visual object tracking. In this article, we propose a two-stage object tracking algorithm using a dual-template. By design, the initial state of a target can be estimated using a prior fixed template at the first stage with a particle-filter-based tracking framework. The use of prior templates maintains the stability of an object tracking algorithm, because it consists of invariant and important features. In the second step, a mean shift is used to gain the optimal location of the object with the stage update template. The stage template improves the ability of target recognition using a classified update method. The complementary of dual-template improves the quality of template matching and the performance of object tracking. Experimental results demonstrate that the proposed algorithm improves the tracking performance in terms of accuracy and robustness, and it exhibits good results in the presence of deformation, noise and occlusion.
Car-Following and Lane-Changing Behavior Involving Heavy Vehicles:

This paper presents empirical findings on car-following and lane-changing behavior involving heavy vehicles; trajectory data from the next generation simulation program were used in the study. It was found that when following passenger cars, heavy vehicles tended to reduce speed variations caused by traffic disturbances and thereby dampened traffic oscillations. In contrast, passenger cars following heavy vehicles tended to amplify traffic disturbances, although with lower probability and magnitude compared with the dampening effect. Moreover, heavy vehicles tended to discourage lane changes, especially behind them. This finding has convoluted implications: although reduced lane changes can improve traffic stability by preventing or reducing disturbances, large gaps can persist behind heavy vehicles and contribute to underutilization of road capacity.
The design of multi temperature and humidity monitoring system for incubator

Currently, there is only one monitor of the temperature and humidity in an incubator, which may cause inaccurate or unreliable data, and even endanger the life safety of the baby. In order to solve this problem,we designed a multi-point temperature and humidity monitoring system for incubators. The system uses the STC12C5A60S2 microcontrollers as the sender core chip which is connected to four AM2321 temperature and humidity sensors. We select STM32F103ZET6 core development board as the receiving end,cooperating with Zigbee wireless transmitting and receiving module to realize data acquisition and transmission. This design can realize remote real-time observation data on the computer by communicating with PC via Ethernet. Prototype tests show that the system can effectively collect and display the information of temperature and humidity of multiple incubators at the same time and there are four monitors in each incubator.
Involvement of ceramide biosynthesis in increased extracellular vesicle release in Pkd1 knock out cells

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is an inherited disorder characterized by the development of renal cysts, which frequently leads to renal failure. Hypertension and other cardiovascular symptoms contribute to the high morbidity and mortality of the disease. ADPKD is caused by mutations in the PKD1 gene or, less frequently, in the PKD2 gene. The disease onset and progression are highly variable between patients, whereby the underlying mechanisms are not fully elucidated. Recently, a role of extracellular vesicles (EVs) in the progression of ADPKD has been postulated. However, the mechanisms stimulating EV release in ADPKD have not been addressed and the participation of the distal nephron segments is still uninvestigated. Here, we studied the effect of Pkd1 deficiency on EV release in wild type and Pkd1-/- mDCT15 and mIMCD3 cells as models of the distal convoluted tubule (DCT) and inner medullary collecting duct (IMCD), respectively. By using nanoparticle tracking analysis, we observed a significant increase in EV release in Pkd1-/- mDCT15 and mIMCD3 cells, with respect to the wild type cells. The molecular mechanisms leading to the changes in EV release were further investigated in mDCT15 cells through RNA sequencing and qPCR studies. Specifically, we assessed the relevance of purinergic signaling and ceramide biosynthesis enzymes. Pkd1-/- mDCT15 cells showed a clear upregulation of P2rx7 expression compared to wild type cells. Depletion of extracellular ATP by apyrase (ecto-nucleotidase) inhibited EV release only in wild type cells, suggesting an exacerbated signaling of the extracellular ATP/P2X7 pathway in Pkd1-/- cells. In addition, we identified a significant up-regulation of the ceramide biosynthesis enzymes CerS6 and Smpd3 in Pkd1-/- cells. Altogether, our findings suggest the involvement of the DCT in the EV-mediated ADPKD progression and points to the induction of ceramide biosynthesis as an underlying molecular mechanism. Further studies should be performed to investigate whether CerS6 and Smpd3 can be used as biomarkers of ADPKD onset, progression or severity.
Up-to-Date Feature Selection Methods for Scalable and Efficient Machine Learning

With the advent of high dimensionality, machine learning researchers are now interested not only in accuracy, but also in scalability of algorithms. When dealing with large databases, pre-processing techniques are required to reduce input dimensionality and machine learning can take advantage of feature selection, which consists of selecting the relevant features and discarding irrelevant ones with a minimum degradation in performance. In this chapter, we will review the most up-to-date feature selection methods, focusing on their scalability properties. Moreover, we will show how these learning methods are enhanced when applied to large scale datasets and, finally, some examples of the application of feature selection in real world databases will be shown. Noelia Sánchez-Maroño University of A Coruña, Spain
Two Factor Biometric Key for Secure Wireless Networks

The applications of wireless networks is steadily increasing through out the world. Wireless transactions are now happening in highly secure banking networks. To have more reliable networks, security of wireless networks is to be strengthened. Various Encryption algorithms are employed for this purpose. With standard key size and format, they are vulnerable to attacks. A key generated from biometric feature is unique and it is hard to break it. If the biometric key is based on key generated from two biometric features, it is highly reliable. In this paper, one such attempt is made to generate key for standard encryption algorithm from keys generated from face and fingerprint with the application of a unique algorithm.
PENDIDIKAN SENI BERBASIS BUDAYA

Pendidikan sebagai proses budaya, sejatinya, adalah suatu upaya membudayakan manusia dengan segala sifat-sifat kemanusiaannya. Manusia di sini bukan sekadar dipandang sebagai objek tetapi lebih diposisikan sebagai subjek. Sebagai sebagai subjek, ia menjadi pelaku dalam memaknai nilai-nilai yang dihadapinya. Dalam pandangan ini manusia, ebagai mahluk budaya, merupakan totalitas atau keutuhan dari sebuah kepribadian yang memiliki daya intelektual, emosional, sosial, dan kultural. Oleh sebab itu, suatu pendidikan, secara budaya, seharusnya memiliki fungsi mengantarkan manusia untuk mengembangkan seluruh potensinya secara komprehensif. Seni sebagai sarana pendidikan memiliki konsep, tujuan, dan fungsi membentuk kepribadian subjek didik secara komprehensif sebagai mahluk individu, sosial, dan budaya. Untuk itu, budaya sebagai basis menjadi keniscayaan dalam setiap penyelenggaraan pendidikan seni.
Use of design optimization techniques in solving typical structural engineering related design optimization problems

High powered computers and engineering computer systems allow designers to routinely simulate complex physical phenomena. The presented work deals with the analysis of two finite element method optimization techniques (First Order Method-FOM and Subproblem Approximation Method-SAM) implemented in the individual Design Optimization module in the Ansys software to analyze the behavior of real problems. A design optimization is a difficult mathematical process, intended to find the minimum or maximum of an objective function, which is mostly based on iterative procedure. Using optimization techniques in engineering designs requires detailed knowledge of the analyzed problem but also an ability to select the appropriate optimization method. The methods embedded in advanced computer software are based on different optimization techniques and their efficiency is significantly influenced by the specific character of a problem. The efficiency, robustness and accuracy of the methods are studied through strictly convex two-dimensional optimization problem, which is represented by volume minimization of two bars" plane frame structure subjected to maximal vertical displacement limit. Advantages and disadvantages of the methods are described and some practical tips provided which could be beneficial in any efficient engineering design by using an optimization method.
Alternatif Turizm Türü Olarak Off-road Faaliyetlerinin Katılımcıların Yaşamlarındaki Yeri: Çanakkale Örneği

Ozellikle son yillarda, off-road etkinliklerinin bir bos zaman aktivitesi olarak deger kazandigi, bircok destinasyonda aktiviteler icin guzergâhlarin belirlendigi gozlemlenmektedir. Buradan hareketle bu calismada, Canakkale'de off-road etkinliklerine katilan bireylerin, bos zaman aktivitesi olarak off-road'u nasil gordukleri, yasam doyumlariyla iliskisi ve Canakkale’de off-road’un bir turistik urun olarak nasil algiladiklari arastirilmaktadir. Buradan hareketle Canakkale’de faaliyet gosteren off-road dernegi araciligiyla off-road etkinliklerine katilan 22 birey ile yari yapilandirilmis derinlemesine gorusme gerceklestirilmistir. Dolayisiyla calismada nitel arastirma yontemlerinden gorusme teknigi uygulanmistir. Yapilan gorusmeler, nitel arastirmalarda siklikla kullanilan icerik analizi ile analiz edilmistir. Elde edilen bulgular neticesinde, off-road’un bireylerin hayatinda onemli bir yere sahip oldugu, kesfetmeye, sosyallesmeye, dogayla mucadeleye ve zorluklarla basa cikmaya katkisi olan sportif bir etkinlik oldugu, ayni zamanda Canakkale icin bir turistik urun olarak algilandigi belirlenmistir. Bununla birlikte bireylerin off-road etkinliklerine katiliyor olmalari ile yasam doyumlari arasinda olumlu yonde bir iliskinin oldugu gozlemlenmistir.
All Men Count with You, but None Too Much: Information Aggregation and Learning in Prediction Markets

all men count with you, but none too much: Information Aggregation and Learning in Prediction Markets by Sindhu Kutty Chair: Jacob D. Abernethy Prediction markets are markets that are set up to aggregate information from a population of traders in order to predict the outcome of an event. In this thesis, we consider the problem of designing prediction markets with discernible semantics of aggregation whose syntax is amenable to analysis. For this, we will use tools from computer science (in particular, machine learning), statistics and economics. First, we construct generalized log scoring rules for outcomes drawn from high-dimensional spaces. Next, based on this class of scoring rules, we design the class of exponential family prediction markets. We show that this market mechanism performs an aggregation of private beliefs of traders under various agent models. Finally, we present preliminary results extending this work to understand the dynamics of related markets using probabilistic graphical model techniques. We also consider the problem in reverse: using prediction markets to design machine learning algorithms. In particular, we use the idea of sequential aggregation from prediction markets to design machine learning algorithms that are suited to situations where data arrives sequentially. We focus on the design of algorithms for
Study and Realization of Automatic Focusing Algorithm for Remote-Sensing Camera Based on Power Spectrum

Digital image processing is applied to the autofocus of linear scan CCD remote-sensing camera with push broom.As for the camera working in linear CCD sweep imaging mode,the scenes shot are different at any time.This brings about difficulty to the selection of the focus measure.In this paper,when different scenes are analyzed in the power spectrum spatial frequency domain,it shows the invariance of power spectrum for natural scenes.The focus measure based on power spectrum is founded and realized by high speed DSP and FPGA.A series of focus experiments show the system has good automatic focusing behavior and the proposed focus measure is of feasibility.
ارائه الگوریتم نوین کنترلی در اصلاحگر دینامیکی ولتاژ ( DVR ) به منظور بهبود کیفیت توان و اصلاح ولتاژ در شبکه های توزیع

هدیکچ : یکی زا نیرتمهم لئاسم رد یاهراب ساسح ،یزورما تیفیک ژاتلو یم دشاب . راکهار لح نیا هلاسم هدافتسا زا تازیهجت ت ناو یشرافس ( Custom Power Devices ) رد هکبش یاه عیزوت یم دشاب . یکی زا تازیهجت ناوت یشرافس رگحلاصا یکیمانید ژاتلو ( DVR ) تسا . DVR اب فادها حلاصا ،ژاتلو حلاصا تاجاجوعا ناوت و فذح کینومراه یاه محازم و داجیا لداعت نیب DG اه رد هکبش یاه عیزوت و راشف فیعض دروم هدافتسا رارق یم دریگ . متسیس یلرتنک دیدج DVR هب کمک ود PI رلرتنک اهاطخ ار هب هفلؤم
A critique of current developments in simultaneous localization and mapping

The number of research publications dealing with the simultaneous localization and mapping problem has grown significantly over the past 15 years. Many fundamental and practical aspects of simultaneous localization and mapping have been addressed, and some efficient algorithms and practical solutions have been demonstrated. The aim of this paper is to provide a critical review of current theoretical understanding of the fundamental properties of the SLAM problem, such as observability, convergence, achievable accuracy and consistency. Recent research outcomes associated with these topics are briefly discussed together with potential future research directions.
A cloud computing security solution based on fully homomorphic encryption

In order to target the data security issues of cloud computing system,by introducing fully homomorphism encryption algorithm in the cloud computing data security,a new kind of data security solution to the insecurity of the cloud computing is proposed and the scenarios of this application is hereafter constructed.This new security solution is fully fit for the processing and retrieval of the encrypted data,and effectively leading to the broad applicable prospect,the security of data transmission and the storage of the cloud computing.
Producing Affective Language Content Selection, Message Formulation, and Computational Modelling

— We introduce a project investigating how emotional states influence language production using both experimental and corpus based approaches. Here, we illustrate our project by asking whether content selection (“deciding what to say”) and linguistic realization (“deciding how to say it”) are affected by the emotional state of a speaker. We do this first by assessing whether disgusted speakers are more or less prone to align with their dialogue partners than amused speakers. Second, we develop a corpus of emotionally laden soccer reports that, even though they refer to the same event, will differ depending on whether the report comes from the winning or losing team. In both cases, we focus on the production and analysis of referring expressions. Our findings will be used to build an affective natural language generation system.
A 4-μA quiescent-current dual-mode digitally controlled buck converter IC for cellular phone applications

This paper describes a dual-mode digitally controlled buck converter IC for cellular phone applications. An architecture employing internal power management is introduced to ensure voltage compatibility between a single-cell lithium-ion battery voltage and a low-voltage integrated circuit technology. Special purpose analog and digital interface elements are developed. These include a ring-oscillator-based A/D converter (ring-ADC), which is nearly entirely synthesizable, is robust against switching noise, and has flexible resolution control, and a very low power ring-oscillator-multiplexer-based digital pulse-width modulation (PWM) generation module (ring-MUX DPWM). The chip, which includes an output power stage rated for 400 mA, occupies an active area 2 mm 2 in 0.25-μm CMOS. Very high efficiencies are achieved over a load range of 0.1-400 mA. Measured quiescent current in PFM mode is 4 μA.
Polemical video annotation by Twitter

Abstract. In this paper we present a method to enhance video metadata by using microposts generated through social interactions during live events. Our goal is to make visible the audience “polemical activity” (the exchange of arguments, counter-arguments and references) elicited by the talk, and use it as a tool to browse the video record. To achieve it, we design a new interface and service that makes a synthetic view of microposts interaction. Keywords: micropost, annotation, video, social interactions, live, polemic 1 Introduction During a public event, more and more social Web tools are used to post real-time information (e.g.: Twitter, Foursquare, Facebook). In most cases, users can follow the production of microposts during the event thanks to tagging systems - for instance, the hashtags on Twitter. For live video streams broadcasts, various webservices already offer Web pages with an embedded video player and interfaces for reading and writing microposts. This design pattern is interesting because it contextualizes the production and consumption of microposts during the talk. Despite the undeniable contextualization offered by this kind of interfaces (video and tweets), their use is not accurate in all cases. Like asynchronous or distant users may encounter difficulties to link the purpose of the talk with existing microposts, and due to the heterogeneous nature of microposts, it is difficult to generate a synthetic overview from the polemical activity. After the event, the memory of the social interactions is lost (especially on Twitter), and it is hard to retrieve the video sequence in relation to a given micropost.
Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy

We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed with learning curves which report approximation errors as a function of training set size. Molecular structures and properties at hybrid density functional theory (DFT) level of theory used for training and testing come from the QM9 database [Ramakrishnan et al, Scientific Data 1 140022 (2014)] and include dipole moment, polarizability, HOMO/LUMO energies and gap, electronic spatial extent, zero point vibrational energy, enthalpies and free energies of atomization, heat capacity and the highest fundamental vibrational frequency. Various representations from the literature have been studied (Coulomb matrix, bag of bonds, BAML and ECFP4, molecular graphs (MG)), as well as newly developed distribution based variants including histograms of distances (HD), and angles (HDA/MARAD), and dihedrals (HDAD). Regressors include linear models (Bayesian ridge regression (BR) and linear regression with elastic net regularization (EN)), random forest (RF), kernel ridge regression (KRR) and two types of neural networks, graph convolutions (GC) and gated graph networks (GG). We present numerical evidence that ML model predictions for all properties can reach an approximation error to DFT which is on par with chemical accuracy. These findings indicate that ML models could be more accurate than DFT if explicitly electron correlated quantum (or experimental) data was provided.
Sensorless Control of Synchronous Motor Drives: Accurate Torque Estimation and Control Under Parameter Errors

Accurate torque control is of interest in traction and other industrial applications, whose realization is challenged by parameters errors and temperature-induced variations. This article proposes a sensorless technique for resilient torque control in salient synchronous machines. The position observer is driven by the adaptive projection vector for the position error estimation (APP) scheme. A second projection vector, orthogonal to APP for noninterference, serves for flux adaptation. Based on the torque estimation, a model-based optimal current reference generation for maximum torque per ampere (MTPA) condition is designed to realize accurate torque control. The proposed scheme is validated experimentally on a 1.1-kW synchronous reluctance (SyR) machine test bench.