Miracle-dz/UniFAR-FTUs huggingface dataset:
Technical Note: EPID's response to 6 MV photons in a strong, parallel magnetic field

PURPOSE Electronic portal imaging devices (EPIDs) are potentially useful for dosimetric verification in integrated MRI-linac systems. This work presents the reproducibility, linearity, image lag, and radiation field profiles in a conventional EPID, with and without a 0.5 T parallel magnetic field present in a 6 MV photon beam. METHODS An aS500 EPID was modified to function in strong magnetic fields. All measurements were made using the linac-MR installed at the Cross Cancer Institute. The EPID remained stationary on the couch between the measurements made with and without magnetic field. We measured short-term reproducibility of dark and flood fields, signal linearity from 1 to 500 MU irradiations, and image lag post 100 MU irradiation. An ion chamber was used to measure any linac output variations to correct the EPID signal due to these variations for the duration of experiment. X-axis and Y-axis radiation field profiles were obtained from the EPID image resulting from a 10 × 10 cm2 radiation field delivery. RESULTS The average pixel value (±standard deviation) of flood field with and without magnetic fields were 57,876 ± 379 and 57,703 ± 366, respectively, and the corresponding average dark field pixel values were -32.05 ± 0.85 and -32.19 ± 0.97. The maximum difference in image linearity data with and without magnetic field is 0.2% which is well within the measurement uncertainty of 0.65%. Similarly, the image lag curves, with and without the magnetic field, were nearly identical. The first measured point, with mean lag signal of 1.44% without and 1.41% with magnetic field, shows that the largest difference is well below the uncertainty in the EPID signal measurement. The radiation field profiles obtained with and without magnetic fields were nearly identical; 91.3% of the X-axis and 95.2% of the Y-axis profile points pass a gamma criterion of 1% and 1 mm. CONCLUSIONS A conventional EPID imager with a 0.1 cm copper plate responds to 6 MV photons similarly irrespective of the strong magnetic field being off or on in the parallel orientation to the radiation beam. Thus, the EPID is a potentially useful tool for pretreatment dosimetric verification in linac-MR systems using parallel magnetic field.
Inferring Cognitive Wellness from Motor Patterns

Changes in the motor pattern have been shown to be useful advanced indicators of cognitive disorders, such as Parkinson's disease (PD) and cerebral small vessel disease (SVD). It would be highly advantageous to tap into data containing people's motor patterns from motion sensing devices to analyze subtle changes in cognitive abilities, thereby providing personalized interventions before the actual onset of such conditions. However, this goal is very challenging due to two main technical problems: 1) the size of data labeled by doctors is small, and 2) the available data tends to be highly imbalanced (the vast majority tend to be from normal subjects with only a small fraction from subjects with cognitive disorder). In order to effectively deal with these challenges to infer cognitive wellness from motor patterns with high accuracy, we propose the MOtor-Cognitive Analytics (MOCA) framework. The proposed MOCA first uses the random oversampling iterative random forest based feature selection method to reduce the feature space dimensionality and avoid overfitting, and then adds a bias in the optimization problem of weighted extreme learning machine to achieve good generalization ability in handling imbalanced small-sampling dataset. Experimental results on two real-world datasets including SVD and stroke patients show that MOCA can effectively reduce the rate of misdiagnosis and significantly outperform state-of-the-art methods in inferring people's cognitive capabilities. This work opens up opportunities for population-level pre-screening using motion sensing devices and can inform current discussions on reforming the health-care infrastructure.
A Large-Scale Empirical Study on Linguistic Antipatterns Affecting APIs

The concept of monolithic stand-alone software systems developed completely from scratch has become obsolete, as modern systems nowadays leverage the abundant presence of Application Programming Interfaces (APIs) developed by third parties, which leads on the one hand to accelerated development, but on the other hand introduces potentially fragile dependencies on external resources. In this context, the design of any API strongly influences how developers write code utilizing it. A wrong design decision like a poorly chosen method name can lead to a steeper learning curve, due to misunderstandings, misuse and eventually bug-prone code in the client projects using the API. It is not unfrequent to find APIs with poorly expressive or misleading names, possibly lacking appropriate documentation. Such issues can manifest in what have been defined in the literature as Linguistic Antipatterns (LAs), i.e., inconsistencies among the naming, documentation, and implementation of a code entity. While previous studies showed the relevance of LAs for software developers, their impact on (developers of) client projects using APIs affected by LAs has not been investigated. This paper fills this gap by presenting a large-scale study conducted on 1.6k releases of popular Maven libraries, 14k open-source Java projects using these libraries, and 4.4k questions related to the investigated APIs asked on Stack Overflow. In particular, we investigate whether developers of client projects have higher chances of introducing bugs when using APIs affected by LAs and if these trigger more questions on Stack Overflow as compared to non-affected APIs.
Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl

Using data on waterfowl band recoveries, we identified spatially explicit hotspots of concentrated waterfowl movement to predict occurrence and spatial spread of a novel influenza A virus (clade 2.3.4.4) introduced from Asia by waterfowl from an initial outbreak in North America in November 2014. In response to the outbreak, the hotspots of waterfowl movement were used to help guide sampling for clade 2.3. 4.4 viruses in waterfowl as an early warning for the US poultry industry during the outbreak . After surveillance sampling of waterfowl, we tested whether there was greater detection of clade 2.3. 4.4 viruses inside hotspots. We found that hotspots defined using kernel density estimates of waterfowl band recoveries worked well in predicting areas with higher prevalence of the viruses in waterfowl. This approach exemplifies the value of ecological knowledge in predicting risk to agricultural security.
Mutational load, inbreeding depression and heterosis in subdivided populations

This paper examines the extent to which empirical estimates of inbreeding depression and inter-population heterosis in subdivided populations, as well as the effects of local population size on mean fitness, can be explained in terms of estimates of mutation rates, and the distribution of selection coefficients against deleterious mutations provided by population genomics data. Using results from population genetics models, numerical predictions of the genetic load, inbreeding depression and heterosis were obtained for a broad range of selection coefficients and mutation rates. The models allowed for the possibility of very high mutation rates per nucleotide site, as is sometimes observed for epiallelic mutations. There was fairly good quantitative agreement between the theoretical predictions and empirical estimates of heterosis and the effects of population size on genetic load, on the assumption that the deleterious mutation rate per individual per generation is approximately one, but there was less good agreement for inbreeding depression. Weak selection, of the order of magnitude suggested by population genomic analyses, is required to explain the observed patterns. Possible caveats concerning the applicability of the models are discussed.
Simultaneous Development of E-Learning in Russia and Information Society

The paper is dedicated to development of e-learning in Russia. Russia has its own domestic MOOC platforms and social networks, there are e-learning universities and programs, thus level of inclusion into information society is rather high. E-learning is developed in three ways: courses, programs and distance universities. The most important issue in this development is standardization and quality. Whereas regular universities and MOOC platforms have their own of government standards of education, many independent organizations have not. Therefore, distance universities are a kind of middle variant. These universities have government accreditation, but all or vast majority of their educational activities are realized via Internet that provides flexibility to this kind of education. Moreover, development of social networks and their usage influence on e-learning development and involvement into lifelong education.
An interactive system to automatically generate video summaries and perform subtitles synchronization for persons with hearing loss

According to World Health Organization (WHO), approximately 328 million of adults and 32 million of children present hearing loss in the world. Likewise, the number of people with such impairment increased from 42 million in 1985 to about 360 million in 2011. However, the most of multimedia and web contents, whether they are educational or leisure, are not accessible for persons with hearing loss. In this line, this paper presents an interactive system aimed at automatically generating video summaries and performing subtitles synchronization for persons with hearing loss. Our proposal relies on an educational platform (MOODLE) and Natural Language Processing (NLP) to provide an environment fully configurable for these persons. The module that generates the video summaries uses techniques such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA), whereas the synchronization module is based on forced alignment between audio streams and text. With the aim of validating our environment, we have tested our approach on 15 videos, obtaining a score of 80% for three criteria related to the summary content: understandability, concordance, and context appropriateness.
Speech Recognition Using Artificial Neural Network

This paper propose two approaches for speech recognition via supervised and unsupervised learning. Speech signals are non-stationary signals. Treating speech in computing domain falls under sequential learning task i.e. if we want to make a sense of current statement, we may need to go through the context in which it was spoken. Recurrent Neural Networks (RNN) is been used in speech recognition problems because of its powerful sequence modeling capacity. In this paper we have proposed Bi-directional Recurrent Neural Network with Long Short Term Memory model (LSTM), so that speech signal reconstruction can be done in a proper way without performance loss. For unsupervised learning, model is designed on the basis of Restricted Boltzmann Machine (RBM) which generates a reconstruction based output and helps in conversion of voice into text, each letter by letter.
Deblurring Underwater Image Degradations Based on Adaptive Regularization

Images attained from underwater are typically corrupted by features such as poor perceptibility, bright object, color reduced, blurred and noise. Rebuilding of appearance after its distorted and blaring complement remains a challenging problem. The ill-posed nature of the problematic means on no account of specific solution so any solution is an estimated of the actual solution and this often leads to inconsistency in the form of degradation as complete smoothing of the reconstructed image. The sparse dominion systems provide unusual solutions to this inverse problem by giving the Il-norm sparsity prior to eliminate underwater image degradations. In this paper, we present adaptive regularization to confine the image patches residence on the inherent smoothing and include the image nonlocal self-similarity into sparse dominion to recover the exactness to reconstruct the expected image. The tentative consequences by means of the proposed technique contribute enhanced performance than former state-of-the-art techniques.
A Theory of Corporate Boards and Forced CEO Turnover

We develop a theory of corporate boards and their role in forcing CEO turnover. We consider a firm with an incumbent CEO of uncertain management ability and a board consisting of a number of directors whose role is to evaluate the CEO and fire her if a better replacement can be found. Each board member receives an independent private signal about the CEO's ability, after which board members vote on firing the CEO (or not). If the CEO is fired, the board hires a new CEO from the pool of candidates available. The true ability of the rm's CEO is revealed in the long run; the firm's long-run share price is determined by this ability. Each board member owns some equity in the firm, and thus prefers to fire a CEO of poor ability. However, if a board member votes to fire the incumbent CEO but the number of other board members also voting to fire her is not enough to successfully oust her, the CEO can impose significant costs of dissent on him. In this setting, we show that the board faces a coordination problem, leading it to retain an incompetent CEO even when a majority of board members receive private signals indicating that she is of poor quality. We solve for the optimal board size, and show that it depends on various board and rm characteristics: one size does not fit all firms. We develop extensions to our basic model to analyze the optimal composition of the board between firm insiders and outsiders and the effect of board members observing imprecise public signals in addition to their private signals on board decision-making. Finally, we develop a dynamic extension to our basic model to analyze why many boards do not fire CEOs even when they preside over a signi cant, publicly observable, reduction in shareholder wealth over a long period of time. We use this dynamic model to distinguish between the characteristics of such boards from those that fire bad CEOs proactively, before significant shareholder wealth reductions take place.
Screen Printed rGO-Pd Nana-Composite films on a Flexible Substrate as Temperature Sensor

We report, a screen printed nano-composite films made with reduced Graphene Oxide (rGO) and Palladium (Pd) for temperature sensor application. The rGO was synthesized using modified Hummer's method and Pd was mixed with as-synthesized rGO to avoid the agglomeration formed by rGO nano sheets. An array of rGO-Pd nano-composite films were fabricated on flexible kapton substrate using screen printing technique, which is of low cost and simple process. As-synthesized rGO and rGO-Pd nano-composite's surface morphology and crystallinity were studied using Field Emission-Scanning Electron Microscope (FE-SEM) and X-ray diffraction (XRD). As-prepared nanocomposite films were tested using cold and hot chamber to verify it's response with respect to change in temperature. It was observed that the film responds linearly with negative temperature coefficient (NTC). The sensitivity and temperature coefficient of resistance (TCR) of the as-fabricated nanocomposite films were observed to be $\pmb{0.7028}\ \boldsymbol{\Omega}/\mathbf{K}$ and. $\pmb{-2.0312}\ \mathbf{x}\ \pmb{10^{-3}} \boldsymbol{\Omega/\Omega}/\mathbf{K}$ respectively. These flexible temperature sensors can be used in the field of biomedical, food processing, aerospace, communication and automobile for monitoring of temperature.
LTSM: Lightweight and Time Sliced Measurement for Link State

Link state measurement is the fundamental part of routing control and has received wide attention in research community. However, due to the massive number of network nodes, it is considerably expensive to measure the network timely. In this work, we propose LTSM, a lightweight and time sliced measurement approach for link state to reduce the measurement cost. In LTSM, the measurement tasks are assigned to all nodes evenly and launched at proper time. Theoretically, the measurement cost can be reduced by 50%. We design a prototype of the approach and implement it on a real-world network consisting of 37 public cloud nodes and 1, 332 links. Experimental results show that our approach can acquire the link state of the network efficiently.
Optimized Classification Approach for Offline Signature Verification System

Now a day, security applications and person's identity verification using biometric systems is increasing rapidly for security aspects. In this field of security system, banking field also requires a proper authentication for transaction when any transaction is done through cheque. In that case, signature matching becomes a challenging task due to skilled forgery in signatures. Hence, a technique is required for offline signature verification systems. Generally, signatures may vary each time which may mislead to the verification process. To overcome this issue, we have presented an improved classification approach for offline signature verification system where simulated annealing optimization scheme is implemented for optimized trainset formulation by computing best cross-validation scores. In order to show the performance, we have considered GPDS &CEDAR database are considered where skilled forgery is present. Experimental study shows better performance when compared with other state of art techniques.
Review on Multi-Degree-of-Freedom Piezoelectric Motion Stage

Multi-degree-of-freedom (multi-DOF) piezoelectric motion stage is an important part of modern manufacturing industry. In order to perform high precision outputs in a large travel range, various multi-DOF piezoelectric motion stages have been proposed and applied for decades. This paper is concentrated on reviewing the research on a piezoelectric motion stage. It involves the major components, operating principles of different actuation modes, design schemes, and structures of the multi-DOF piezoelectric motion stages. In this paper, the summary and classification of the actuation modes used in the multi-DOF piezoelectric motion stages are presented, mainly including the resonance actuation mode, inertial actuation mode, clamping and feeding actuation mode, and direct actuation mode; the multi-DOF piezoelectric motion stage is divided into multi-actuator type and single-actuator type according to the number of actuators used in the motion stage, in which the multi-actuator type includes series, parallel, and series–parallel types from the viewpoint of structure mechanism; in addition, the output performances of the stages are compared, and the state of the arts of the stages are discussed and summarized; the further efforts and future research perspectives are highlighted.
Sentiment Analysis of Movie Reviews in Hindi Language Using Machine Learning

Sentiment Analysis is an application of Natural Language Processing (NLP) which is used to find the sentiments of users reviews, comments etc. on the internet. Nowadays, social websites like facebook, twitter are widely used for posting the users reviews about different things such as movies, news, food, fashion, politics and much more. Reviews and opinions play a major role in identifying the level of satisfaction of users regarding a particular entity. These are then used to find the polarity i.e. positive, negative and neutral. In this paper an approach to Sentiment Analysis on movie reviews in Hindi language is discussed.
A Lexicon Generation Method for Aspect-Based Opinion Mining

Opining mining is the task of analyzing the written text about an entity to determine the expressed sentiment toward that entity. This paper presents a lexicon generation approach to determine the polarity of the aspects described in reviews written in e-commerce websites. The method we are to present here is an extension over a previous method, FBSA, which calculates scores for words in reviews rapidly. The previously mentioned method was designed for tweet level problems. The main contribution of this paper is altering the score generation algorithm of aforementioned method and adding new features to adapt it to aspect based problems. The results prove that our method produces lexicon performing better compared to reputed lexicons such as AFFIN and Bing Liu's lexicon on SemEval-2014 dataset by a minimum margin of 4.2 percentage points in laptop domain and 1.2 percentage points in restaurant domain on F-measure.
A New Exact Low-Complexity MMSE Equalizer for Continuous Phase Modulation

This letter introduces a new low-complexity frequency-domain equalizer for continuous phase modulations (CPM). The derivation of a fractionally spaced representation for circular block-based CPM leads, without any approximation, to a simple yet efficient frequency-domain equalization. The proposed equalizer is compared to the state-of-the-art approaches. Simulation results show the equivalence in terms of performance with a lower or similar complexity.
Discrete Nonlinear Optimization by State-Space Decompositions

This paper investigates a decomposition approach for binary optimization problems with nonlinear objectives and linear constraints. Our methodology relies on the partition of the objective function into separate low-dimensional dynamic programming (DP) models, each of which can be equivalently represented as a shortest-path problem in an underlying state-transition graph. We show that the associated transition graphs can be related by a mixed-integer linear program (MILP) so as to produce exact solutions to the original nonlinear problem. To address DPs with large state spaces, we present a general relaxation mechanism that dynamically aggregates states during the construction of the transition graphs. The resulting MILP provides both lower and upper bounds to the nonlinear function, and it may be embedded in branch-and-bound procedures to find provably optimal solutions. We describe how to specialize our technique for structured objectives (e.g., submodular functions) and consider three problems arising .
A Novel Engineering Implementation of SVPWM Based on Line Voltage Information

In this paper, a novel control strategy using the line voltage information to realize the space vector pulse width modulation (SVPWM) is proposed. The principles and implementation steps of SVPWM are given. In the algorithm flow, the phase angle $\boldsymbol{\theta}$ of phase voltage is the most important input information for SVPWM strategy. For the three phase system, the phase angle $\boldsymbol{\theta}$ can't be detected directly. This paper shows the derivation process of line voltage to phase voltage for the three phase three wire system. According to the intrinsic relationship of the line voltage and the phase voltage, the three phase three wire inverter prototype is produced to validate the SVPWM control strategy. It makes the three phase system use the SVPWM reliably and easily. The experimental results are shown. It validate the new control strategy using the line voltage information to realize the SVPWM.