Miracle-dz/UniFAR-FTUs huggingface dataset:
View-invariant Planar Object Detection for VisTRo

We present a simple and powerful method for planar object detection which is robust against changes in camera positions using image warping. Applying stereo camera, we efficiently reconstruct the sparse 3D scene and find the dominant plane. We then place a virtual camera facing directly at the selected plane from the predefined distance and angle so that the plane of interest appears in the intended size without any affine distortion due to a slanted view point. The exact 3D location of any point on the surface can be recovered by oriented chamfer matching using a single template and inverse warping. This algorithm shows a good performance on indoor robot vision application which often requires detecting planar objects such as locating an elevator button or recognizing signs on the wall. Keywords—Template matching, planar object alignment.
A new feature descriptor called Histogram of Oriented Gradients from Three Orthogonal Planes ( HOG-TOP ) is proposed to extract dynamic textures from video sequences

Emotion analysis is very significant from the perspective of many applications like E-learning, cognitive assessment, driver alert system, pain monitoring system, healthcare services, interactive TV, animation etc. Outcome of the work is depicted in the form of emotion profile which is defined as graphical representation of the degree of presence or absence of all the universally accepted seven emotions on a single scale. This simple but unique representation helps in determining the presence of complex/naturalistic emotions. Complex emotions represents the emotional state of a person when he/she is able to feel and express more than one emotions simultaneously at the time of observation. Emotion analysis through facial expressions is experimented on JAFFE and MIST Database a locally created context specific database of images. Images of the subjects under study are captured and experimented for all seven universally accepted emotions and depicted in the form of emotion profiles. Emotions are categorized as neutral, positive and negative for MIST database and in seven categories of emotions like happy, surprise, anger, sad, disgust, fear and neutral for JAFFE database. Average accuracy obtained as 91.31% for MIST database and 93.84% for JAFFE Database. FPR false positive rate and FNR false negative rate, values for JAFFE database are 6.38% and 6.62% respectively. FPR and FNR values for MIST database are 8.18% and 8.60% respectively. Emotion recognition time for JAFFE database 1.108 sec and for MIST database 1.392 Sec. These performance parameters especially the accuracy more than 90% which is at par with the research results published in renowned journals in this domain makes this work qualify to be used for various applications. Analysis of complex emotions is typically useful for the professionals working in the field of Human resource, clinical psychology, cognitive analysis etc. More accurate emotion recognition is still challenging and open research problem
Shipwreck Geoarchaeology Soil Micromorphology Submerged Continental Shelf Prehistory PALUDAL SETTINGS ( WETLAND ARCHAEOLOGY )

Trousset, P., Slim, H. , Paskoff, R. , and Oueslati, A. , 2004. Le littoral de la Tunisie: Etude géoarchéologique et historique. Paris: CNRS Editions. Vannière, B. , Colombaroli, D. , Chapron, E. , Leroux, A. , Tinner, W. , andMagny,M. , 2008. Climate versus human-driven fire regimes in Mediterranean landscapes: the Holocene record of Lago dell’Accesa (Tuscany, Italy). Quaternary Science Reviews, 27 (11–12), 1181–1196. Vött, A. , Schriever, A. , Handl, M. , and Brückner, H. , 2007. Holocene palaeogeographies of the central Acheloos River delta (NW Greece) in the vicinity of the ancient seaport Oiniadai. Geodinamica Acta, 20(4), 241–256.
A Framework for Machine Learning-Assisted Design and Execution of Numerical Parametric Studies in Evaluating the Seismic Response of Soil-Structure Systems

Numerical parametric studies are a critical tool for advancing the state of knowledge in the fields of structural and geotechnical earthquake engineering. Field observations can only be made post hoc and therefore (1) are limited by the occurrence of earthquakes and (2) typically do not include much, if any, data from instrumentation of the affected sites or structures. Physical experimentation addresses these limitations, but is still too time-and cost-intensive to use to generate large databases that explore the influence of all relevant parameters. Numerical studies must be validated using experimental or observational data, but are then limited only by the availability of computational resources, particularly when analyzing 3D, highly non-linear, soil-structure systems. Although extensive numerical studies are possible, efficiency and optimization of study design are critical as we increasingly need to model more complex systems and interactions. Typically, numerical studies are first designed (i.e., the parameters of all analyses are set), then executed, and then interpreted. This paper presents a framework for machine learning-assisted dynamic design of numerical studies based on iterative assessment of the information obtained from each analysis and maximization of the likelihood that further analyses will be informative. The steps of the framework are presented, and relevant statistical methods are reviewed. The steps include initial study design, assessment of the influence of individual analyses, and identification of the optimum subsequent analyses. A variety of machine learning techniques are applicable at each phase, and recommendations are made regarding model development and the quantification of the influence of each analysis in a dataset. A case study involving the consequences of soil liquefaction and its mitigation on building performance is considered to demonstrate the advantages of this method in terms of efficiency. The computational expense of a dynamically designed numerical study is compared to that of efficient statically designed studies, and the potential impacts of a priori uncertainty around final study quality are discussed. Lastly, the circumstances in which the dynamic approach offers significant advantages are identified.
Decomposition of sodium dodecylbenzene sulfonate surfactant by electro/Fe2+-activated persulfate process from aqueous solutions.

The decomposition of sodium dodecylbenzene sulfonate (SDBS), which is a dangerous and anionic surfactant, was investigated by the electro/Fe/persulfate process from aqueous solutions. The activation of persulfate anion and production of active radicals were performed by means of heat, UV and iron ions (released from iron electrodes). The findings illustrated that the pH value of the solution, initial concentration of persulfate anion, the amount of input voltage and iron dosage were entirely effective in SDBS removal. Nearly 100% of SDBS was removed under the optimum conditions: pH 3, voltage 10 V, anion persulfate concentration 25 mM l, iron ion dosage 0.25 g l and reaction time 25 min. Furthermore, the application of the persulfate anion process in concert with the electrochemical process, in order to generate electrical iron and persulfate activation, had a better performance compared to separate methods.
Non – Linear Dynamics of Railway Wheel Set

The Governing equations of motion of railway wheel set moving on a straight track are derived using Lagrangian approach. The resulting nonlinear equations of motion of railway wheelset are solved using Fourth order Runge-Kutta method. Johnson and Vermeulen theory for creepage is employed. A general-purpose program is developed for the analysis. The results are presented in the form of Time series diagram, Phase plots, Poincare plots, Bifurcation diagrams and Lyapunov exponent diagrams. The influence of yaw stiffness on the response of wheelset is studied. Both symmetric and asymmetric oscillations and chaotic motions are observed. It is observed that broadband chaos is developed at various velocity levels due to flange contact. Periodic windows were observed in the bifurcation diagram. KeywordsChaotic oscillation, Numerical Simulation, Railway Wheel set.
A constructive heuristic for permutation flow-shop scheduling with the makespan criterion : in a particular case where the sum of the processing times of each job is the same on each machine

This paper describes an adaption of the fuzzy greedy heuristic (FGH) for the permutation flow-shop scheduling problem with the makespan criterion. In a particular case where the sum of the processing times of each job is the same on each machine, certain priority rule-based scheduling heuristics would assign the same priority to every job, and so in this situation, all possible permutations of the jobs would be equally likely to be selected by those heuristics. An efficient ranking method is proposed to prioritize the jobs in this particular case. Computational experiments using standard benchmark problems indicate that the proposed method is very efficient.
A Maximum Disparity Estimation Algorithm based on CLG-TV Optical Flow Method

Disparity range estimation is a very important part of stereo vision system in machine vision. In order to estimate the disparity range effectively, a new algorithm based on CLG-TV optical flow method is proposed. For the computation of the maximum disparity, the algorithm improves data item and smooth item of optical flow method. The position estimation of the maximum disparity is obtained by using the coarse-to-fine method at low resolution, then the image is cut in the position to carry out the accurate optical flow calculation, and finally the optical flow field is analysed to obtain the maximum disparity. Experimental results show that the proposed algorithm has a high accuracy in real images and synthetic sequences, and the maximum disparity can be effectively estimated in a short time.
Influence of the mass flow rate and void fraction on the vibration of a horizontal U-bend subjected to an upwards flowing air/water-mixture

U-bends are a common geometry in heat exchangers. In this paper, a U-bend is considered with horizontal straight pipes subjected to an initially stratified water/air flow which moves upwards against gravity. The flow is analyzed with an Unsteady Reynolds-Averaged Navier- Stokes Volume-Of-Fluid approach. For low mass flow rates, large gas bubbles are naturally formed at the entrance of the bend. The transient force on the tube allows to determine precisely the moments of bubble initiation and thus to quantify the bubble frequency. Firstly, the tube is assumed to be rigid and the dependence of force oscillation on the inlet conditions is investigated. Secondly, a Fluid-Structure Interaction calculation is applied in order to determine the vibration amplitude of the tube.
The Entrepreneurial Commercialization of Science: Evidence From 'Twin' Discoveries

When are scientific advances translated into commercial products via startup formation? Although prior literature has offered several categories of answers, the commercial potential of a scientific advance is generally unobserved and potentially confounding. We assemble a sample of over 20,000 “twin” scientific discoveries in order to hold constant differences in the nature of the scientific advance, thereby allowing us to more precisely examine characteristics that predict startup commercialization. We find that teams of academic scientists whose former collaborators include “star” serial entrepreneurs are much more likely to commercialize their own discoveries via startups, as are more interdisciplinary teams of scientists.
Predicting Real Estate Price Using Text Mining Automated Real Estate Description Analysis

This study describes two methods for real estate price prediction using text mining: classification and regression. Both methods use stemmed n-grams (unigrams and bigrams) derived from real estate description to train different machine learning techniques. Real estate price predictions involve the following pricing indicators: selling price, asking price and price fluctuation. In our classification experiment we succeeded to predict all pricing indicators significantly above baseline with a maximum increase of 38% (F = .652). Our regression experiment shows that the SGD classifier performs best for all pricing indicators with highest performance achieved for predicting selling price (R = .303). Both can be seen as respectable results given the complex nature of the task. Our results indicate that performance increases when we enlarge the total amount of unigrams and bigrams used in our price prediction model. For the prediction of selling price we see a significant logarithmic increase, which means that the positive effect of adding more ngrams to the model reduces over time. A predictive system for real estate price using text mining reveals to be highly challenging and becomes increasingly complex for larger datasets. Extracting predictive n-grams from real estate description can be a unique and meaningful addition to the standard hedonic pricing models widely used for the prediction of real estate price. PREDICTING REAL ESTATE PRICE USING TEXT MINING 3
FIV 2018-001 DRAFT : SENSITIVITY ANALYSIS APPLIED FOR A FLOW AROUND SPRING – MOUNTED CYLINDER

Calculations of sensitivities for a flow around springmounted cylinder free to oscillate in the cross stream direction are introduced. In this work, the state vector of the fluid-structure interaction (FSI) problem is given by a base flow plus a perturbation, in which the base flow is steady and the displacement of the cylinder is given by a perturbation. The goal is to identify what regions of the domain originate the instabilities on the perturbation field. This region, called of wavemaker, are given by the sensitivity to external forcing at specific cases in which the external forcing is proportional to the perturbation field. The mathematical expression for the sensitivity calculation is obtained by considering an optimization problem. The wavemaker region is determined in two cases: at subcritical Reynolds number Re = 33 and supercritical Reynolds number Re = 60. We observed that the wavemaker region changes with the variation of the natural frequency and presented differences if compared with the wavemaker region of the fixed cylinder at Re = 33. However, for the flow past fixed and spring-mounted cylinders the wavemaker regions are similar at Re = 60. In the numerical simulations, the Spectral/hp element was used for spatial discretization and the linear adjoint FSI system was solved by considering a generalized eigenvalue problem, in which the Arnoldi method was employed.
Oscillating Pressure Experiments on Porcine Aorta

This paper addresses the problem of Traumatic Aorta Rupture (TAR) that is one of the causes of fatality in motor vehicle accidents. The mechanisms that have been suggested for TAR are speculative and inconclusive and most tests performed have not been repeatable. One of the main reasons for these speculations is an incomplete understanding of the material properties of the aorta. The goal of the presented experiments is to characterize the relationship between stress and strain in the aorta wall in the biaxial pressure tests. An experimental setup was developed such that sinusoidal pressure (between 4.5 and 74.2 kPa) was supplied into porcine aorta at frequencies ranging from . 5 Hz to 5 Hz. The aorta sample (n=7) was tested with both ends fixed and one end attached to the inlet tube for pressurization with normal saline solution. The deformation of aorta in the center and the pressure inside the aorta were recorded. The experimental results are represented in the form of Kirchhoff stress versus Green strain curves which show an increase in stiffness with an increase in the frequency. The curves also demonstrate that the loading and unloading paths of the aorta are different. The results of this study were then used to develop a material model of the aorta in biaxial loading conditions using the quasilinear viscoelastic theory.
Metadata of the chapter that will be visualized in SpringerLink

A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a no-reference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and denoising models alone cannot robustly rank denoising results, they often complement each other. We accordingly design denoising quality features based on these existing metrics and models and then use Random Forests Regression to aggregate them into a more powerful unified metric. Our experiments on images with various types and levels of noise show that our no-reference denoising quality assessment method significantly outperforms the state-of-the-art quality metrics. This paper also provides a method that leverages our quality assessment method to automatically tune the parameter settings of a denoising algorithm for an input noisy image to produce an optimal denoising result.
3 EVALUATING NWP ENSEMBLE CONFIGURATIONS FOR AT & D APPLICATIONS

Due to the chaotic nature of the atmosphere, there are inherent limitations to forecasting a single realization of the future atmospheric state. While numerical weather prediction (NWP) models have become more advanced in recent years in representing and predicting the atmospheric state, they are still limited because of imperfect model numerics, imperfect parameterizations of unresolved physical processes, and interpolations of input data that is sparsely located compared to current model grid resolutions. In recognition of these difficulties, contemporary NWP uses ensembles of simulations. Members in these ensembles often differ by imposed initial conditions (ICs), lateral and lower boundary conditions (LBCs), model physics parameterization schemes, and even the choice of NWP modeling system. Grimit and Mass (2002) state that there is a strong positive correlation between the ensemble spread and forecast error for short-range mesoscale NWP. For example, if the spread across the ensemble is low, the forecast error (and hence, the forecast uncertainty) is generally low, and vice-versa. There are a few different approaches to ensemble initialization that are documented in the literature. Each approach attempts to account for uncertainty in the forecast. Several approaches are designed specifically to account for uncertainty in the initial conditions. Some of these include using bred vectors, singular vectors, an ensemble Kalman filter, ensemble transform Kalman filter, or the ensemble transform approach. There are several studies in the literature that compare the relative performance of ensembles that are initialized with these different methods, but the ensemble Kalman filter has generally performed best (Wang and Bishop 2003; Buizza et al. 2005; Bowler 2006; Descamps and Talagrand 2006; Wei et al. 2006). Another source of uncertainty for regional NWP (or limited-area models) arises from the specification of the lateral boundary conditions. Warner et al. (1997) summarize many of the issues that modelers must consider with regard to LBCs. Notably, errors that arise due to the LBCs “sweep” across the model domain through the integration period, which can degrade model results and constrain ensemble dispersion (Nutter et al. 2004). Therefore, if there is a particular region that is of interest to the modeler, that region should be placed far enough away from the lateral boundaries so that errors from the boundary will not have advected into that region at the time of interest.
Energy Efficient and Trusted Clustering in WSN

Scalability and prolonging the network life time are the important matter for wireless sensor network designer due to energy constraint of sensor nodes. In this paper, we present a trust based secure and energy efficient clustering algorithm in wireless sensor network. The proposed energy efficient and trusted clustering algorithm can defend the wireless sensor network from various type of malicious nodes. Furthermore, we have also compared our proposed algorithm with most popular protocol LEACH. The MATLAB simulation of our proposed method shows better performance in terms of network life time than LEACH.
( Machine ) Learning About Immigration ’ s Impact in Local Labor Markets With Classified Ad Text

The decennial Census provides detailed information about local labor markets but at very low-frequencies, making it difficult for researchers to identify short-run effects in local labor markets. Machine learning techniques open the door to studies analyzing local labor markets at high frequency by allowing researchers to analyze large text corpora, such as those found in classified ads. Motivated by Census tabulations showing a Cuban immigration surge in the Miami metro area around 1980, we document a decline in the Help Wanted Index, a monthly count of help-wanted classifieds, for Miami relative to geographic baselines. We conduct a supervised learning exercise demonstrating a statistically significant decline in the fraction of job openings for roles typically associated with less-educated workers using a 1/29 sample of the Miami Herald ’s classifieds. We explore these results using topic modeling, which naturally generates occupational and industry categories with minimal researcher degrees of freedom. This exercise uncovers a statistically significant increase in the proportion of job openings advertised for positions in accounting, sales, and engineering and a corresponding decrease in the proportion advertised for positions in the food and automotive industry. Analysis of the Current Population Survey’s Annual Social and Economic Supplement confirms the relative importance of job openings for less-educated workers’ wage outcomes, but the linkage with employment outcomes is statistically indistinguishable from zero for all workers. We conclude with directions for future research and suggestions for agencies aiming to modernize their statistical releases.1 Title XIII and XXVI statistics approved for release by the Disclosure Review Board (DRB) under Approval Number CBDRB-FY19-190.
Addressing the Glucose Triad for Comprehensive Glycemic Control

THE IMPORTANCE OF LOWERING HbA1c The UKPDS established that HbA1c is the best prognosticator for T2DM and its complications.2,3 Since its publication in the late 1990s, clinicians worldwide were driven by the concept of “lower the better”. However, the publication of three trials in quick succession: The Action to Control Cardiovascular Risk in Diabetes (ACCORD),7 Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE)8 and Veterans’ Affair Diabetes Trial (VADT)9 trials, raised concerns about stringent control of HbA1c, indicating that it could be harmful to some patients. Indeed the ACCORD trial was stopped midway when it was found that there was an increase in mortality for patients who were treated to a target HbA1c of less than 6%. What it also showed was that patients who suffered from severe hypoglycemia had an increased risk of death even if they were not in the intensive control arm of the study. The ADVANCE and VADT studies failed to show any improvement in cardiovascular mortality or morbidity in spite of good glycemic control. To add to this, a retrospective cohort study of databases maintained by UK general practitioners published in 2010, (albeit with multiple shortcomings) suggested that the optimal HbA1c was 7.5%, lesser or higher being equally harmful. 10