Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic segmentation based on aggregated features and contextual information

In this paper, a novel semantic segmentation model based on aggregated features and contextual information is proposed. Given an RGB-D image, we train a support vector machine (SVM) to predict initial labels using aggregated features, and then optimize the predicted results using contextual information. For aggregated features, the local features on regions are extracted to capture visual appearance of object, and the global features are exploited to represent scene information such that the proposed model can utilize more discriminative features. For contextual information, a novel multi-label conditional random field (CRF) model is constructed to jointly optimize the initial semantic and attribute predicted results. The experimental results on the public NYU v2 dataset demonstrate the proposed model outperforms the existing state-of-the-art methods on a challenging 40 classes task, yielding a higher mean IU accuracy of 33.7% and pixel average accuracy of 64.1%. Especially, the prediction accuracy of “small” classes has been improved significantly.
A Comparative Study of an Online and a Face-to-Face Chemistry Course

While online and face-to-face (F2F) courses have been compared in numerous studies, there has been a lack of focus on online chemistry courses. This study was conducted to compare the success of students instructed in an online or F2F general chemistry course for non-majors. One hundred forty six exam questions were categorized according to Bloom's revised taxonomy and student success on each problem was analyzed. Comparison of online and F2F courses showed significant differences at the lowest order of thinking, "remember," with online students performing better than F2F students. A similar result was seen with the next order of thinking, "understand," but there were no significant differences observed between online and F2F students for exam questions at the "analyze" level. The observed advantage for online students may be because online instruction promotes better memorization of facts or because students good at memorization gravitate towards online courses. No significant differences were seen between online and F2F courses when comparing the various chemistry topics covered in the exams. Online instruction appears to be as effective as F2F instruction when teaching introductory chemistry topics.
Anteroposterior Limb Skeletal Patterning Requires the Bifunctional Action of SWI/SNF Chromatin Remodeling Complex in Hedgehog Pathway

Graded Sonic hedgehog (Shh) signaling governs vertebrate limb skeletal patterning along the anteroposterior (AP) axis by regulating the activity of bifunctional Gli transcriptional regulators. The genetic networks involved in this patterning are well defined, however, the epigenetic control of the process by chromatin remodelers remains unknown. Here, we report that the SWI/SNF chromatin remodeling complex is essential for Shh-driven limb AP patterning. Specific inactivation of Srg3/mBaf155, a core subunit of the remodeling complex, in developing limb buds hampered the transcriptional upregulation of Shh/Gli target genes, including the Shh receptor Ptch1 and its downstream effector Gli1 in the posterior limb bud. In addition, Srg3 deficiency induced ectopic activation of the Hedgehog (Hh) pathway in the anterior mesenchyme, resulting in loss of progressive asymmetry. These defects in the Hh pathway accompanied aberrant BMP activity and disruption of chondrogenic differentiation in zeugopod and autopod primordia. Notably, our data revealed that dual control of the Hh pathway by the SWI/SNF complex is essential for spatiotemporal transcriptional regulation of the BMP antagonist Gremlin1, which affects the onset of chondrogenesis. This study uncovers the bifunctional role of the SWI/SNF complex in the Hh pathway to determine the fate of AP skeletal progenitors.
Leadership Insularity: A New Measure of Connectivity Between Central Nodes in Networks.

We combine two foci of interest with respect to community identification and node centrality and create a novel metric termed "leadership insularity. " By determining the most highly connected nodes within each community of a network, we designate the 'community leaders' within the graph. In doing this, we have the basis for a novel metric that examines how connected, or disconnected, the leaders are to each other. This measure has a number of appealing measurement properties and provides a new way of understanding how network structure can affect its dynamics, especially information flow. We explore leadership insularity in a variety of networks.
Occlusion detection for ICAO compliant facial photographs

Facial image analysis is an important computer vision topic as a first step for biometric applications like face recognition/verification. The ICAO specification defines criteria to assess suitability of facial images for later use in such tasks. This standard prohibits photographs showing occlusions, thus there is the need to detect occluded images automatically. In this work we present a novel algorithm for occlusion detection and evaluate its performance on several databases. First, we use the publicly available AR faces database which contains many occluded face image samples. We show a straight-forward algorithm based on color space techniques which gives a very high performance on this database. We conclude that the AR faces database is too simple to evaluate occlusions and propose our own, more complex database, which includes, e.g., hands or arbitrary objects covering the face. Finally we extend our first algorithm by an Active Shape Model in combination with a PCA reconstruction verification. We show how our novel occlusion detection algorithm outperforms the simple approach on our more complex database.
Explicit Manifold Representations for Value-Function Approximation in Reinforcement Learning

We are interested in using reinforcement learning for large, real-world control problems. In particular, we are interested in problems with continuous, multi-dimensional state spaces, in which traditional reinforcement learning approached perform poorly. Value-function approximation addresses some of the problems of traditional algorithms (for example, continuous state spaces), and has been shown to be successful in a number of specific applications. However, it has been proven not to work in the general case, and has been known to fail even on simple problems. We propose a novel approach to value-function approximation, based on the theory of manifolds. We identify the key failing of current techniques, and show how our approach avoids this problem by explicitly modeling the topology of the state space. We begin with a brief description of the problems of current value-function approximation techniques. We then motivate our proposed approach, outlining the mathematics underpinning it, and provide the results of our initial experiments.
Refinements and Tests of an Advanced Controller to Mitigate Fatigue Loads in the Controls Advanced Research Turbine

Wind turbines are complex, nonlinear, dynamic systems forced by aerodynamic, gravitational, centrifugal, and gyroscopic loads. The aerodynamics of wind turbines are nonlinear, unsteady, and complex. Turbine rotors are subjected to a complicated 3-D turbulent wind inflow field, with imbedded coherent vortices that drive fatigue loads and reduce lifetime. Design of control algorithms for wind turbines must account for multiple control objectives. Future large multi-megawatt turbines must be designed with lighter weight structures, using active controls to mitigate fatigue loads, while maximizing energy capture. Active damping should be added to these dynamic structures to maintain stability for operation in a complex environment. At the National Renewable Energy Laboratory (NREL), we have designed, implemented, and tested advanced controls to maximize energy extraction and reduce structural dynamic loads. These control designs are based on linear models of the turbine that are generated by specialized modeling software. In this paper, we present field test results of an advanced control algorithm to mitigate blade, tower, and drivetrain loads in Region 3. The advanced state-space controller uses independent blade pitch to mitigate the effects of shear across the rotor disk, and a collective pitch component to add active damping to the tower’s first fore-aft bending mode, and, to regulate turbine speed. In addition, a separate generator torque control loop adds active damping to the tower’s first side-side mode and the first drivetrain-torsion mode. In this paper we show a refinement to the generator torque control loop to account for actuator delay. We discovered a delay in actuation between commanded generator torque and the torque actually applied to the highspeed shaft. If this delay is not properly accounted for in the plant model used for control design, the generator torque control loop tends to destabilize the first drivetrain torsion mode. We show modifications to the torque control loop to account for this delay, and, to prevent unnecessary control actuation at certain harmonics in the rotorspeed. We present field tests of this controller and make comparisons with a simple PID baseline controller for the 2-bladed Controls Advanced Research Turbine (CART2) located at NREL’s National Wind Technology Center.
Relaxation and clustering in a local search framework: application to linear placement

This paper presents two primary results relevant to physical design problems in CAD/VLSI through a case study of the linear placement problem. First a local search mechanism which incorporates a neighborhood operator based on constraint relaxation is proposed. The strategy exhibits many of the desirable features of analytical placement while retaining the flexibility and non-determinism of local search. The second and orthogonal contribution is in netlist clustering. We characterize local optima in the linear placement problem through a simple visualization tool-the displacement graph. This characterization reveals the relationship between clusters and local optima and motivates a dynamic clustering scheme designed specifically for escaping such local optima. Promising experimental results are reported.
Sensible approach for soil fertility management using GIS cloud

GIS embedded cloud storage of spatial data plays a significant role in current agricultural field. An alternative approach to our traditional method of soil fertility management in decision making processes using GIS Cloud (Geographic Information System) is a novel idea for e-governance and m-governance. In recent years, many researchers have studied the importance of cloud-computing in various sectors for storing large amount of data and GIS as a separate concept in viewing the spatial maps. The main objective of this study is to suggest a complete Decision Support System (DSS) using GIS enabled cloud technologies to administer agricultural related data required for soil fertility management based on different criteria's like crop, season, soil type, seed varieties, etc. The proposed DSS includes development of geo-referenced soil fertility maps showing distributions of soil nutrients and their spatial variability to provide fertilizer recommendations using Soil Test Crop Response (STCR) equations for targeted yield approach for different crops in India.
Bir ölçek geliştirme çalışması: Kemoterapi uygulanan hastalarda özbakım yetersizliği kuramına gore özbakım davranışları ölçeği

Amac: Bu arastirmanin amaci, kemoterapi uygulanan hastalarda Orem’in Oz Bakim Yetersizligi Kurami’na gore oz bakim davranislarini olcmeye yonelik bir olcme araci gelistirmektir. Yontem: Arastirma, temel bir arastirma niteliginde olup, Istanbul da Avrupa yakasinda bulunan uc egitim ve arastirma hastanesinde, arastirma kriterlerine uyan ve arastirmaya katilmayi kabul eden 600 hasta uzerinde, Agustos 2016–Şubat 2017 tarihleri arasinda gerceklestirilmistir. Verilerin toplanmasinda Hasta Bilgi Formu ve arastirmaci tarafindan gelistirilen taslak olcek kullanilmistir. Verilerin analizinde tanimlayici istatistikler,yapi gecerliligi icin acimlayici faktor analizi yapilmistir. Guvenirlik analizleri icin, Cronbach Alpha ve Spearman-Brown korelasyon testleri kullanilmistir. Bulgular: 5’li likert tipi olan o lcegin, 24 maddeden ve bireysel bakim,uyku duzeni,solunumu surdurme,aktivite ve hareket,beslenme aliskanligi ve sorunlarla basa cikma olmak uzere 6 faktorden olustugu,aciklanan toplam varyansin %64.75 oldugu saptanmistir. Yapilan guvenirlik analizi sonucunda, olcegin Cronbach’s alfa katsayisi 0.88 olarak hesaplanmistir. Test-tekrar test guvenirligini belirlemek icin yapilan analizler sonucunda (n=30) olcegin iki uygulamasi arasinda yuksek duzeyde tutarlilik saptanmistir (r:.945, p<0.001). Sonuc: Gelistirilmis olan bu olcegin, gecerlik ve guvenilirligi oldukca yuksek, bireylerin oz bakim davranislarini belirlemede guvenilir ve gecerli bir olcme araci oldugu belirlenmistir.
Data-based approximate policy iteration for nonlinear continuous-time optimal control design

This paper addresses the model-free nonlinear optimal problem with generalized cost functional, and a data-based reinforcement learning technique is developed. It is known that the nonlinear optimal control problem relies on the solution of the Hamilton-Jacobi-Bellman (HJB) equation, which is a nonlinear partial differential equation that is generally impossible to be solved analytically. Even worse, most of practical systems are too complicated to establish their accurate mathematical model. To overcome these difficulties, we propose a data-based approximate policy iteration (API) method by using real system data rather than system model. Firstly, a model-free policy iteration algorithm is derived for constrained optimal control problem and its convergence is proved, which can learn the solution of HJB equation and optimal control policy without requiring any knowledge of system mathematical model. The implementation of the algorithm is based on the thought of actor-critic structure, where actor and critic neural networks (NNs) are employed to approximate the control policy and cost function, respectively. To update the weights of actor and critic NNs, a least-square approach is developed based on the method of weighted residuals. The whole data-based API method includes two parts, where the first part is implemented online to collect real system information, and the second part is conducting offline policy iteration to learn the solution of HJB equation and the control policy. Then, the data-based API algorithm is simplified for solving unconstrained optimal control problem of nonlinear and linear systems. Finally, we test the efficiency of the data-based API control design method on a simple nonlinear system, and further apply it to a rotational/translational actuator system. The simulation results demonstrate the effectiveness of the proposed method.
Mindfulness: Instrumentos de evaluación. Una revisión bibliográfica.

Resumen: Diversas investigaciones han tenido especial interes en el proceso psicologico de la atencion y conciencia plena, o mindfulness (da Costa da Silveira, A., 2017). El presente trabajo de investigacion realiza una revision de las escalas de evaluacion del mindfulness, iniciando una busqueda en las bases de datos como Redalyc, Scielo, Latindex, PsycInfo y Pubmed con las palabras claves que se mencionan a continuacion. Palabras claves: Conciencia plena. Atencion plena. Instrumentos. Psicometricos. Evaluacion. Abstract: Current psychological research has paid special attention to the psychological process of continuous monitoring of inner experience, i.e., mindfulness (da Costa da Silveira, A., 2017). This paper aims to present a review of scales that have been used to evaluate mindfulness, based on the search in different databases, such Redalyc, Scielo, Latindex, PsycInfo and Pubmed, using as keywords mentioned below. Keywords: Mindfulness. Awareness. Acceptance. Psychometric. Evaluation.
A Visual Similarity-Based 3D Search Engine

Retrieval systems for 3D objects are required because 3D databases used around the web are growing. In this paper, we propose a visual similarity based search engine for 3D objects. The system is based on a new representation of 3D objects given by a 3D closed curve that captures all information about the surface of the 3D object. We propose a new 3D descriptor, which is a combination of three signatures of this new representation, and we implement it in our interactive web based search engine. Our method is compared to some state of the art methods, tested using the Princeton-Shape Benchmark as a large database of 3D objects. The experimental results show that the enhanced curve analysis descriptor performs well.
The gas density around SN 1006

The density of the ambient medium where the supernova remnant evolves is a relevant parameter for its hydrodynamical evolution, for the mechanism of particle acceleration, and for the emission at TeV energies. Using XMM-Newton X-ray observations, we present a study of the ambient medium density of the historical supernova remnant SN 1006. We modelled the post-shock thermal emission to constrain the ambient medium density. Our study is focused on the North-West and the South-East rims of the remnant, where the thermal emission dominates. We used a plane-parallel shock plasma model plus another component for the ejecta that are not negligible in the regions of our study. The importance of the synchrotron component is also studied. In order to improve statistics, we combined several observations of the remnant. The density found in the South-East rim is low, roughly 0.05 cm-3, and seems to be representative of the rest of the remnant. However, in the North-West rim (close to the bright optical filament), the density is significantly higher (about 0.15-0.25 cm-3). This confirms a picture of SN 1006 evolving in a tenuous ambient medium, except in the North-West where the remnant has recently encountered a denser region. A density this low is compatible with the non-detection of the remnant by the HESS gamma-ray observatory. The lower density in the South-East implies a higher shock speed of 4900 km/s, higher than that of 2890 km/s measured in the North-West. This new estimate of the velocity could increase the maximum energy that accelerated particles can reach to energies of about 1 PeV.
Evaluating the Quality of Query Refinement Suggestions in Information Retrieval

Abstract : Automatic suggestion of alternative terms to refine a user's query is an effective technique to help the user quickly narrow down to his(her) specific information need. However, evaluating the effectiveness of these suggestions has remained quite subjective, with a vast majority of the past work relying on expensive user studies. In this work, we look at this problem from the IR perspective. We propose two objective measures that evaluate the quality of Query Refinement (QR) suggestions, based on the degree to which the documents retrieved by the QR suggestions, when used as queries, capture the overall sub-topical structure underlying the topic of the original query. The first measure, known as Maximum Matching Averaged Mean Average Precision (MM-AMAP) requires labeled documents for the sub-topics underlying the query's topic. The second measure which we call Distinctness and MAP based F1 (DMAP-F1) requires only labeled documents that are relevant to the original query. We also define a series of simple QR suggestion techniques, each of which is intuitively better than the previous ones and evaluate them using our measures on TDT3 and TDT4 corpora. Our experiments show that our evaluation metrics numerically capture our intuitive expectations on performance, thus informally validating our measures. Further, we also show that the second metric DMAP-F1, that does not require sub-topic judgments, is consistent in results as well as statistically highly correlated with the first metric. This allows us to perform extensive evaluations of the quality of QR suggestion techniques on standard TREC collections in the future.
Asymptotic Analysis of Hoppe Trees

We introduce and analyze a random tree model associated to Hoppe's urn. The tree is built successively by adding nodes to the existing tree when starting with the single root node. In each step a node is added to the tree as a child of an existing node, where these parent nodes are chosen randomly with probabilities proportional to their weights. The root node has weight ϑ>0, a given fixed parameter, all other nodes have weight 1. This resembles the stochastic dynamic of Hoppe's urn. For ϑ=1, the resulting tree is the well-studied random recursive tree. We analyze the height, internal path length, and number of leaves of the Hoppe tree with n nodes as well as the depth of the last inserted node asymptotically as n→∞. Mainly expectations, variances, and asymptotic distributions of these parameters are derived.
Cross-Sectional Analysis of the Utility of Pulmonary Function Tests in Predicting Emphysema in Ever-Smokers

Emphysema is largely an under-diagnosed medical condition that can exist in smokers in the absence of airway obstruction. We aimed to determine the sensitivity and specificity of pulmonary function tests (PFTs) in assessing emphysema using quantitative CT scans as the reference standard. We enrolled 224 ever-smokers (current or former) over the age of 40. CT of thorax was used to quantify the low attenuation area (% emphysema), and to measure the standardized airway wall thickness. PFTs were used individually and in combination to predict their ability to discriminate radiographic emphysema. Significant emphysema (>7%) was detected in 122 (54%) subjects. Twenty six (21%) emphysema subjects had no evidence of airflow obstruction (FEV1/FVC ratio <70%), while all subjects with >23% emphysema showed airflow obstruction. The sensitivity and specificity of spirometry for detecting radiographic emphysema were 79% and 75%, respectively. Standardized airway wall thickness was increased in subjects with airflow obstruction, but did not correlate with emphysema severity. In this cohort of lifetime ever-smokers, PFTs alone were inadequate for diagnosing emphysema. Airway wall thickness quantified by CT morphometry was associated with airflow limitation, but not with emphysema indicating that the heterogeneous nature of lung disease in smokers may represent distinct phenotypes.
Challenging SO(10) SUSY GUTs with family symmetries through FCNC processes

We perform a detailed analysis of the SO(10) SUSY GUT model with D3 family symmetry of Dermisek and Raby (DR). The model is specified in terms of 24 parameters and predicts, as a function of them, the whole MSSM set of parameters at low energy scales. Concerning the SM subset of such parameters, the model is able to give a satisfactory description of the quark and lepton masses, of the PMNS matrix and of the CKM matrix. We perform a global fit to the model, including flavour changing neutral current (FCNC) processes Bs → μ+μ−, B → Xsγ, B → Xsl+l− and the Bd,s−d,s mass differences ΔMd,s as well as the flavour changing (FC) process B+ → τ+ν. These observables provide at present the most sensitive probe of the SUSY mass spectrum and couplings predicted by the model. Our analysis demonstrates that the simultaneous description of the FC observables in question represents a serious challenge for the DR model, unless the masses of the scalars are moved to regions which are problematic from the point of view of naturalness and probably beyond the reach of the LHC. We emphasize that this problem could be a general feature of SUSY GUT models with third generation Yukawa unification and weak-scale minimal flavour violation.