Miracle-dz/UniFAR-FTUs huggingface dataset:
Pixel-Based Hyperparameter Selection for Feature-Based Image Registration

This paper deals with parametric image registration from point correspondences in deformable environments. In this problem, it is essential to determine correct values for hyperparameters such as the number of control points of the warp, a smoothing parameter weighting a term in the cost function, or an M-estimator threshold. This is usually carried out either manually by a trial-and-error procedure or automatically by optimizing a criterion such as the Cross-Validation score. In this paper, we propose a new criterion that makes use of all the available image photometric information. We use the point correspondences as a training set to determine the warp parameters and the photometric information as a test set to tune the hyperparameters. Our approach is fully robust in the sense that it copes with both erroneous point correspondences and outliers in the images caused by, for instance, occlusions or specularities.
Analysis of turnover intention power factors: A case study of retail company in Jakarta

Article history: Received: April 20, 2018 Received in revised format: June 21, 2018 Accepted: July 6, 2018 Available online: July 6, 2018 Employee turnover rate in the head office of retail companies in East Jakarta has been relatively high, recently. The turnover rate results in a disruption of operations in the head office, in forms of delays in the provision of data reporting, which causes a disruption in flow of information. This research covers such aspects as colleagues' relationships, work environment, salary level, job satisfaction, and organizational commitment. The primary objective of this paper is to identify different aspects of employee turnover intention. Partial Least Square Method (PLS) is applied to determine the important factors of employee turnover intention. The result shows that job satisfaction and organizational commitment maintained an adverse and significant effect on turnover intention while salary levels had an adverse but not significant effect on turnover intention. Coworkers and workplace relationships also had an adverse and significant effect but had no direct effect on turnover intention. © 2018 by the authors; licensee Growing Science, Canada
Improving Word Alignment by Semi-Supervised Ensemble

Supervised learning has been recently used to improve the performance of word alignment. However, due to the limited amount of labeled data, the performance of "pure" supervised learning, which only used labeled data, is limited. As a result, many existing methods employ features learnt from a large amount of unlabeled data to assist the task. In this paper, we propose a semi-supervised ensemble method to better incorporate both labeled and unlabeled data during learning. Firstly, we employ an ensemble learning framework, which effectively uses alignment results from different unsupervised alignment models. We then propose to use a semi-supervised learning method, namely Tri-training, to train classifiers using both labeled and unlabeled data collaboratively and further improve the result. Experimental results show that our methods can substantially improve the quality of word alignment. The final translation quality of a phrase-based translation system is slightly improved, as well.
A 1.8-V 1.8 GHz LC VCO for WSN application

A 1.8 GHz LC VCO in 1.8-V supply is presented. The VCO achieves low power consumption by optimum selection of inductance in the L-C tank. To increase the tuning range, a three-bit switching capacitor array is used for digital switched tuning. Designed in 0. 18μm RF CMOS technology, the proposed VCO achieves a phase noise of -126.2dBc/Hz at 1MHz offset and consumes 1.38mA core current at 1.8-V voltage supply.
System identification of large flexible appendage on satellite for autonomous control

Vibration of Large flexible appendage seriously reduces the stability and accuracy of satellite attitude control, which can be solved by adjusting controller via on-orbit system identification method. This paper focuses on large flexible close mode satellite antenna, and Observer/Kalman filter identification (OKID) method is put into practice to solve the problem of system identification. Mode parameters and displacement responses under impulse and white noise excitations are extracted by finite element method (FEM), and then the concept of singular entropy is introduced to reduce observation noise and determine system order. Markov parameters are computed from displacement response and mode order, which is adopted in minimal realization of state space based on eigensystem realization algorithm (ERA). Comparing with the results of FEM in numerical simulation, OKID method is much more accurate and efficient for large flexible close mode satellite antenna on-orbit system identification.
Joint Transceiver and Power Splitter Design Over Two-Way Relaying Channel With Lattice Codes and Energy Harvesting

This letter considers a compute-and-forward two-way relaying channel with simultaneous wireless information and power transfer based on nested lattice codes. Two single-antenna nodes exchange information via a multi-antenna intermediate relay station. Meanwhile, energies in wireless signals are beamed and flow from the relay to terminals for static circuit consumption Pc and uplink transmission power Pi. The optimization problem is formulated to minimize the transmit power Pr of relay, while guaranteeing the minimal transmission rate at each terminal. We propose an efficient iterative algorithm to jointly design the beam-former, receiver and power splitter, based on semi-definite relaxation and programming. Numerical results of transmission power comparisons validate our analysis.
Ieee Transactions on Knowledge and Data Engineering 1 Multi-dimensional Classification with Super-classes

—The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time.
Safety and efficacy of radiation and chemoradiation in patients over 70 years old with inoperable esophageal squamous cell carcinoma

The aim of the present study was to perform a retrospective analysis to investigate the outcome and toxicity of radiation (RT) and chemoradiation (CRT) in elderly, inoperable patients >70 years old. Between 2003 and 2012, 1,024 patients with squamous cell carcinoma (SCC) of the esophagus were treated at the Department of Thoracic Cancer, West China Hospital (Chengdu, China). Of these patients, 37 were >70 years old and had not undergone surgery, and were selected for analysis. Of these 37 patients, CRT had been administered to 20 (54%). Actuarial survival rates were determined by the Kaplan-Meier method. The one-year survival rate in the CRT group (n=20) was 85%, while 35% of patients in the RT group (n=17) survived for more than one year. The overall and progression-free survival in the CRT group versus the RT group were 17 months [95% confidence interval (CI), 11.861–22.139] versus eight months (95% CI, 6.674–9.326) (P=0.013) and 14 months (95% CI, 9.617–18.383) versus five months (95% CI, 2.311–7.689) (P=0.01), respectively. Patients irradiated with a dose of >50 Gy exhibited an improved survival rate compared with patients who received a dose of ≤50 Gy (18 vs. 14 months; P=0.049). Furthermore, patients with an Eastern Cooperative Oncology Group (ECOG) score of ≤1 had an improved prognosis compared with those with an ECOG score of 2 (14 vs. seven months; P=0.006). The two regimens were well-tolerated and there were no therapy-associated mortalities. The current retrospective study indicated that patients of >70 years old with inoperable esophageal SCC and a good ECOG score exhibit comparably better safety levels with CRT and improved survival rates compared with RT alone.
Processing Pathways in Mental Arithmetic—Evidence from Probabilistic Fiber Tracking

Numerical cognition is a case of multi-modular and distributed cerebral processing. So far neither the anatomo-functional connections between the cortex areas involved nor their integration into established frameworks such as the differentiation between dorsal and ventral processing streams have been specified. The current study addressed this issue combining a re-analysis of previously published fMRI data with probabilistic fiber tracking data from an independent sample. We aimed at differentiating neural correlates and connectivity for relatively easy and more difficult addition problems in healthy adults and their association with either rather verbally mediated fact retrieval or magnitude manipulations, respectively. The present data suggest that magnitude- and fact retrieval-related processing seem to be subserved by two largely separate networks, both of them comprising dorsal and ventral connections. Importantly, these networks not only differ in localization of activation but also in the connections between the cortical areas involved. However, it has to be noted that even though seemingly distinct anatomically, these networks operate as a functionally integrated circuit for mental calculation as revealed by a parametric analysis of brain activation.
Rank : A Novel Concept for Extraction Based Text Summarization 1 Dipti

Indexing used in text summarization has been an active area of current researches. Text summarization plays a crucial role in information retrieval. Snippets generated by web search engines for each query result is an application of text summarization. Existing text summarization techniques shows that the indexing is done on the basis of the words in the document and consists of an array of the posting lists. Document features like term frequency, text length are used to assign indexing weight to words. Hence indexing weights of the document words are used to calculate the sentence similarity value between document words which remains independent on context. The word based index seems to be less efficient due to information retrieval problems like polysemy and Synonymy. Thus the significance of term for building the index is reduced and the emphasis is laid on the context of the document. This paper proposes an indexing structure in which index is built on the basis of context of the document rather than on the terms basis. While doing so we have also used novel concept of Lexical association (semantic association) between document words to calculate the similarity between sentences using computed indexing Weights. The proposed concept of sentence similarity measure has been used with the graph-based ranking method to create document graph and get summary of document.
Critical points of master functions and flag varieties

We consider critical points of master functions associated with integral dominant weights of Kac-Moody algebras and introduce a generating procedure constructing new critical points starting from a given one. The set of all critical points constructed from a given one is called a population. We formulate a conjecture that a population is isomorphic to the flag variety of the Langlands dual Kac-Moody algebra and prove the conjecture for algebras $sl_{N+1}, so_{2N+1}$, and $sp_{2N}$. We show that populations associated with a collection of integral dominant $sl_{N+1}$-weights are in one to one correspondence with intersection points of suitable Schubert cycles in a Grassmannian variety.
CAMERA-CAR VIDEO ANALYSIS FOR STEERING WHEEL’S TRACKING

Monitoring and controlling the driver’s guidance by analyzing the rotation impressed to the steering-wheel can be a very important task in order to improve safety. This paper proposes a general-purpose method to track the steering wheel’s absolute angle by using a single camera vision system mounted inside the car. The absolute angle is computed by means of the accumulation of inter-frame relative rotations and the error propagation is prevented with an alignment process. The approach is based on the modeling of the motion of the steering wheel, as it appears perspectively distorted by the point of view of the un-calibrated camera. We modified the Lucas-Kanade method for an approximatively rotational motion model in order to provide the detection and tracking of significant features on the wheel. The experimental results are compared with ground-truthed data obtained with different types of sensors.
Population-Based Associations Among Cannabis Use, Anxiety, and Depression in Norwegian Adolescents

ABSTRACT Objective: The aim of the study was to explore the use of cannabis among Norwegian adolescents and examine associations with self-reported symptoms of anxiety and depression, age, and dose/frequency of use. Methods: A total of 36,714 Norwegian adolescents between the ages of 13 and 17 completed a cross-sectional national survey. Results: Cannabis users reported significantly more symptoms of anxiety and depression compared to non-users. There were no significant differences on anxiety and depression scores between those who had tried the drug once and those who had tried it six times or more. Both cannabis use and the prevalence of symptoms of anxiety and depression were found to increase with age. Girls reported less use of cannabis and slightly more symptoms of anxiety and depression compared to boys. Conclusions: The present study contributes to the existing knowledge about important associations between cannabis use and symptoms of anxiety and depression in adolescents. Future research should focus on longitudinal methods in order to better understand the role of environmental and neurobiological explanatory factors.
Problems in Precision Modeling of the MOS Transistor for Analog Applications

This paper summarizes the inadequacies of present MOSFET models as applied to analog circuit design and, in some cases, proposes solutions. Both efficient models suitable for CAD and more complex models are considered. Problem areas discussed include poor modeling of the moderate inversion region, poor modeling of the surface potential in strong inversion, ambiguous use of "threshold" voltages and poor expressions for them, poor modeling of the drain small-signal conductance, very poor modeling of intrinsic small-signal capacitances, inadequate small-signal equivalent circuit topologies, and poor implementation of known correct ideas in some CAD programs, including the dependence of the effective mobility on the substrate potential, the modeling of thermal noise in the nonsaturation region, and the modeling of ion-implanted devices.
Extraction des objets en mouvement : une approche mixte contours-régions

In this paper, we propose a new method to extract moving objects from a video stream without any motion estimation. The objective is to obtain a fast and efficient extraction method, robust to noise and large motions. Our approach consists in a frame differencing strategy combined with a hierarchical segmentation approach. First, we propose to extract moving edges with a new difference scheme, based on the spatial gradient of successive frames. In the second stage, the moving regions are extracted from previously detected moving edges by using the hierarchical segmentation. The obtained moving objects description is represented as a region adjacency graph. The method is validated on real sequences in the context of videosurveillance, assuming a static camera hypothesis.
Modeling self-Principal Component Analysis for age invariant face recognition

Face recognition is a biometric approach which can extract the facial characteristics of a person without his/her cooperation. The face recognition system fails to identify a person after some years because of the age related variations shown on the face. The face recognition system should be able to handle such age related variations and recognise the person irrespective of his age. The aging variations on a face are seen in the form of wrinkles, shape changes etc. The process of aging is highly composite and its effects on face are unpredictable. The aging effects are unique to every individual's face. In this paper we try to model the age variations of face using a self-Principal Component Analysis (PCA) based method. We have compared the recognition rate with the conventional PCA and an improved recognition rate is obtained using our method.
Transferrable Expertise From Bionic Arms to Robotic Exoskeletons: Perspectives for Stroke and Duchenne Muscular Dystrophy

Upper extremity function is affected by a variety of neurological conditions. Robotic exoskeletons offer a potential solution for motor restoration. However, their systematic adoption is limited by challenges relative to human intention detection and device control. This position article offers a focused perspective on this topic. That is, on how knowledge gained from the design and implementation of human–machine interfaces (HMIs) for bionic arms can benefit the field of rehabilitation exoskeletons. Three broadly used HMIs in bionic arms are here investigated, including surface electromyography, impedance, and body-powered control. We propose that combinations of these HMIs could push forward upper extremity exoskeleton development. In this context, we provide concrete applicative examples in two selected clinical scenarios, including post-stroke and Duchenne muscular dystrophy individuals. The discussed solutions can open new avenues for the translation of robotic exoskeletons in a large set of clinical settings and enable a class of exoskeleton technologies that could support a broader range of impairment and disease types.
Looking inside a circle: volunteer experiences of circles of support and accountability

Abstract Circles of Support and Accountability (CoSA) is a promising reintegration framework whereby community volunteers support individuals convicted for sexual offenses to reintegrate safely into the community. CoSA has grown in popularity, yet little is known about volunteers’ experiences of CoSA, including how they cope with their core member being recalled or reoffending. The aim of the current study is to explore the experiences of CoSA volunteers in New Zealand. In-depth interviews were used to explore experiences. The findings relating to general experiences explored balancing risk management and social support, questioning the place of religion in CoSA, confronting stereotypes, and volunteering in a risk-averse society. The findings also revealed both the commitment to CoSA, and the stress and guilt felt by the volunteers following a recall or re-offense. Findings are discussed in terms of their implications for current and ongoing volunteers, as well as for community approaches to criminal justice.
Tri-Training Based Learning from Positive and Unlabeled Data

This paper studies the problem of learning text classifier using positive and unlabeled examples with tri-training algorithm, which has been brought forward for semi-supervised learning. The key feature is that there are no negative examples. This paper proposed a new tri-training algorithm for the LPU problem that combines the step 1 of the three LPU algorithms to extract a reliable negative examples set, consequently to build an initial classifier for the tri-training and replace the bootstrap sampling procedure that has not been thought as a good method, and then iteratively use the three SVM classifiers until they convergence. Experiments on the popular Reuter21578 collection show the effectiveness of our proposed technique.
Disentangling the Galaxy at low Galactic latitudes

We have used the field stars from the open cluster survey BOCCE (Bologna Open Clusters Chemical Evolution), to study three low-latitude fields imaged with the Canada–France–Hawaii telescope (CFHT), with the aim of better understanding the Galactic structure in those directions. Because of the deep and accurate photometry in these fields, they provide a powerful discriminant among Galactic structure models. In the present paper we discuss if a canonical star count model, expressed in terms of thin and thick disc radial scales, thick disc normalization and reddening distribution, can explain the observed colour–magnitude diagrams (CMDs). Disc and thick disc are described with double exponentials, the spheroid is represented with a De Vaucouleurs density law. In order to assess the fitting quality of a particular set of parameters, the colour distribution and luminosity function of synthetic photometry is compared to that of target stars selected from the blue sequence of the observed CMDs. Through a Kolmogorov–Smirnov test, we find that the classical decomposition halo-thin/thick disc is sufficient to reproduce the observations – no additional population is strictly necessary. In terms of solutions common to all three fields, we have found a thick disc scalelength that is equal to (or slightly longer than) the thin disc scale.
Research on Auto Extraction of Interested Region in Fire Image Compression

Forest fire detection includes satellite monitoring, manual monitoring, aerial survey and so on. Compared with these expensive ways, a more practical fireproofing surveillance system needs to be built. An image capture system can be set up in the tower to sent fire image back to the command centre, using the short wave radios equipped for manual monitoring in the towers. Considering the finitude of short wave bandwidth, it is essential to compress the image. Region of interest coding can provide higher compression rate. In this paper, the image zoning is implemented using watershed and Normalized Cuts. Then, the mask of region of interest is extracted aiming at the features of fire. In order to improve the speed of calculating, zoning is done in the subband of image.