Miracle-dz/UniFAR-FTUs huggingface dataset:
Extended inflation with a curvature-coupled inflaton.

We examine extended inflation models enhanced by the addition of a coupling between the inflaton field and the space-time curvature. We examine two types of model, where the underlying inflaton potential takes on second-order and first-order forms, respectively. One aim is to provide models which satisfy the solar system constraints on the Brans-Dicke parameter \ensuremath{\omega}. This constraint has proven very problematic in previous extended inflation models, and we find circumstances where it can be successfully evaded, though the constraint must be carefully assessed in our model and can be much stronger than the usual \ensuremath{\omega}g500. In the simplest versions of the model, one may avoid the need to introduce a mass for the Brans-Dicke field in order to ensure that it takes on the correct value at the present epoch, as seems to be required in hyperextended inflation. We also briefly discuss aspects of the formation of topological defects in the inflaton field itself.
High-Speed Current dq PI Controller for Vector Controlled PMSM Drive

High-speed current controller for vector controlled permanent magnet synchronous motor (PMSM) is presented. The controller is developed based on modular design for faster calculation and uses fixed-point proportional-integral (PI) method for improved accuracy. Current dq controller is usually implemented in digital signal processor (DSP) based computer. However, DSP based solutions are reaching their physical limits, which are few microseconds. Besides, digital solutions suffer from high implementation cost. In this research, the overall controller is realizing in field programmable gate array (FPGA). FPGA implementation of the overall controlling algorithm will certainly trim down the execution time significantly to guarantee the steadiness of the motor. Agilent 16821A Logic Analyzer is employed to validate the result of the implemented design in FPGA. Experimental results indicate that the proposed current dq PI controller needs only 50 ns of execution time in 40 MHz clock, which is the lowest computational cycle for the era.
Design and implementation of a hierarchical SIP-based peer-to-peer network

Peer-to-peer (P2P) has gained popularity in Internet due to the increased number of users and distributed services such as file sharing and voice calls over IP (VoIP). Currently, the most popular P2P networks store information pertaining to its resources in a distributed manner using Distributed Hash Tables (DHT). In this type of networks, the peers are deterministically positioned and resources are also allocated to each peer for indexing in a deterministic manner. This paper presents a new hierarchical SIP based P2P network. A pure SIP solution was used because we believe that an open solution such as SIP can facilitate the creation of new types of services and permit the facilitated integration of different services. A two level hierarchy is used and is aimed towards the improvement of network efficiency where peers can move dynamically from one layer to another according to its available resources. In addition to this, the proposed implementation architecture allows the independence of the underlying DHT algorithms. The proposed architecture was implemented and tested in a realistic scenario which was created inside a Linux cluster. The DHT algorithms, Chord and EpiChord, were also implemented and used to index resources in either flat or hierarchical networks. Results indicate that the proposed two-layer hierarchy significantly improves the P2P overlay performance while in the presence of peers with limitations.
Joint Beat & Tatum Tracking from Music Signals

This paper presents a method for extracting two key metrical properties, the beat and the tatum, from acoustic signals of popular music. The method is computationally very efficient while performing comparably to earlier methods. High efficiency is achieved through multirate accent analysis, discrete cosine transform periodicity analysis, and phase estimation by adaptive comb filtering. During analysis, the music signals are first represented in terms of accentuation on four frequency subbands, and then the accent signals are transformed into periodicity domain. Beat and tatum periods and phases are estimated in a probabilistic setting, incorporating primitive musicological knowledge of beat–tatum relations, the prior distributions, and the temporal continuities of beats and tatums. In an evaluation with 192 songs, the beat tracking accuracy of the proposed method was found comparable to the state of the art. Complexity evaluation showed that the computational cost is less than 1% of earlier methods. The authors have written a real-time implementation of the method for the S60 smartphone platform.
On the Total Variation Dictionary Model

<para> The goal of this paper is to provide a theoretical study of a total variation (TV) dictionary model. Based on the properties of convex analysis and bounded variation functions, the existence of solutions of the TV dictionary model is proved. We then show that the dual form of the model can be given by the minimization of the sum of the <formula formulatype="inline"><tex Notation="TeX">$l^1$</tex> </formula>-norm of the dual solution and the Bregman distance between the curvature of the primal solution and the subdifferential of TV norm of the dual solution. This theoretical result suggests that the dictionary must represent sparsely the curvatures of solution image in order to obtain a better denoising performance. </para>
Model-Based Fusion of Multi- and Hyperspectral Images Using PCA and Wavelets

In remote sensing, due to cost and complexity issues, multispectral (MS) and hyperspectral (HS) sensors have significantly lower spatial resolution than panchromatic (PAN) images. Recently, the problem of fusing coregistered MS and HS images has gained some attention. In this paper, we propose a novel method for fusion of MS/HS and PAN images and of MS and HS images. MS and, more so, HS images contain spectral redundancy, which makes the dimensionality reduction of the data via principal component (PC) analysis very effective. The fusion is performed in the lower dimensional PC subspace; thus, we only need to estimate the first few PCs, instead of every spectral reflectance band, and without compromising the spectral and spatial quality. The benefits of the approach are substantially lower computational requirements and very high tolerance to noise in the observed data. Examples are presented using WorldView 2 data and a simulated data set based on a real HS image, with and without added noise.
Intraosseous Follicular Adenomatoid Odontogenic Tumour—A Case Report

The adenomatoid odontogenic tumour is a relatively uncommon lesion which mainly affects females in their second decade of life. It exhibits a predilection for the anterior region of the maxilla. The lesion is usually associated with the crown of an embedded tooth, most commonly the maxillary canine. In this paper, we present a case of adenomatoid odontogenic tumor affecting the left maxillary region in a 24-year-old female. The authors also discuss clinical, radiographic, histopathologic, and therapeutic features of the case.
A polynomial algorithm for the computation of buffer capacities with throughput constraint for embedded system design

Marked TimedWeighted Event Graphs (in shortMTWEG), which are a sub-class of Petri nets, are widely used for modelling practical industrial problems. In this paper, a central practical problem for the design of streaming (e.g. multimedia or network packet processing) applications is modelled using a MTWEG. The optimization problem tackled here consists then on finding an initial marking minimizing the overall number of tokens for a minimum given throughput. If the firings of the transitions are periodic, this problem is NP-complete and can be modelled using an Integer Linear Program. A general lower bound on the minimum overall capacity is then proved. If the initial MTWEG has a unique circuit, a polynomial time algorithm based on the resolution of a particular Diophantine equation is presented to solve it exactly. We lastly experiment it on an industrial example.
Working Paper Series Learning and Monetary Policy Shifts

This paper estimates a dynamic stochastic equilibrium model in which agents use a Bayesian rule to learn about the state of monetary policy. Monetary policy follows a nominal interest rate rule that is subject to regime shifts. The following results are obtained. First, the author’s policy regime estimates are consistent with the popular view that policy was marked by a shift to a high-inflation regime in the early 1970s, which ended with Volcker’s stabilization policy at the beginning of the 1980s. Second, while Bayesian posterior odds favor the “full-information” version of the model in which agents know the policy regime, the fall of inflation and interest rates in the disinflation episode in the early 1980s is better captured by the delayed response of the “learning” specification. Third, the author examines the magnitude of the expectations-formation effect of monetary policy interventions in the “learning” specification by comparing impulse responses to a version of the model in which agents ignore the information contained in current and past monetary policy shocks about the likelihood of a regime shift. JEL classification: C11, C32, E52
Adaptation to Stable and Unstable Dynamics Achieved By Combined Impedance Control and Inverse Dynamics Model

[PDF] [Full Text] [Abstract] , January 1, 2007; 97 (1): 331-347. J Neurophysiol E. Guigon, P. Baraduc and M. Desmurget Computational Motor Control: Redundancy and Invariance [PDF] [Full Text] [Abstract] , May 1, 2007; 97 (5): 3736-3750. J Neurophysiol E. Trainin, R. Meir and A. Karniel and Limb Biomechanics Models Explaining Patterns of Neural Activity in the Primary Motor Cortex Using Spinal Cord [PDF] [Full Text] [Abstract] , July 18, 2007; 27 (29): 7705-7716. J. Neurosci. D. W. Franklin, G. Liaw, T. E. Milner, R. Osu, E. Burdet and M. Kawato Endpoint Stiffness of the Arm Is Directionally Tuned to Instability in the Environment [PDF] [Full Text] [Abstract] , November 1, 2007; 98 (5): 3072-3080. J Neurophysiol M. R. Hinder and T. E. Milner Rapid Adaptation to Scaled Changes of the Mechanical Environment [PDF] [Full Text] [Abstract] , March 1, 2008; 99 (3): 1253-1266. J Neurophysiol A. G. Richardson, G. Lassi-Tucci, C. Padoa-Schioppa and E. Bizzi Environment Neuronal Activity in the Cingulate Motor Areas During Adaptation to a New Dynamic
Cardiovascular risk factors—using repeated cross-sectional surveys to assess time trends in socioeconomic inequalities in neighbouring countries

Objectives This study compares trends in socioeconomic inequalities related to key cardiovascular risk factors in neighbouring countries Northern Ireland (NI) and the Republic of Ireland (RoI). Design Repeated cross-sectional studies. Setting Population based. Participants 3500–4000 in national surveys in NI and 5000–9000 in RoI, aged 20–69 years. Measures Educational attainment was used as a socioeconomic indicator by which the magnitude and direction of trends in inequalities for smoking, diabetes, obesity and physical inactivity in NI and RoI were examined between 1997/1998 and 2007/2011. Gender-specific relative and absolute inequalities were calculated using the Relative Index of Inequality (RII) and Slope Index of Inequality (SII) for both countries. Results In both countries, the prevalence of diabetes and obesity increased whereas levels of smoking and physical inactivity decreased over time. In NI relative inequalities increased for obesity (RII 1.1 in males and 2.1 in females in 2010/2011) and smoking (RII 4.5 in males and 4.2 in females in 2010/2011) for both genders and absolute inequalities increased for all risk factors in men and increased for diabetes and obesity in women. In RoI greater inequality was observed in women, particularly for smoking (RII 2.8 in 2007) and obesity (RII 8.2 in 2002) and in men for diabetes (RII 3.2 in 2002). Conclusions Interventions to reduce inequalities in risk factors, particularly smoking, obesity and diabetes are encouraged across both countries.
Learning Document Embeddings by Predicting N-grams for Sentiment Classification of Long Movie Reviews

Despite the loss of semantic information, bag-of-ngram based methods still achieve state-of-the-art results for tasks such as sentiment classification of long movie reviews. Many document embeddings methods have been proposed to capture semantics, but they still can't outperform bag-of-ngram based methods on this task. In this paper, we modify the architecture of the recently proposed Paragraph Vector, allowing it to learn document vectors by predicting not only words, but n-gram features as well. Our model is able to capture both semantics and word order in documents while keeping the expressive power of learned vectors. Experimental results on IMDB movie review dataset shows that our model outperforms previous deep learning models and bag-of-ngram based models due to the above advantages. More robust results are also obtained when our model is combined with other models. The source code of our model will be also published together with this paper.
Treatment of COPD by clinical phenotypes: putting old evidence into clinical practice

The new Global Initiative for Chronic Obstructive Lung Disease update has moved the principles of treatment of stable chronic obstructive pulmonary disease (COPD) forward by including the concepts of symptoms and risks into the decision of therapy. However, no mention of the concept of clinical phenotypes is included. It is recognised that COPD is a very heterogeneous disease and not all patients respond to all drugs available for treatment. The identification of responders to therapies is crucial in chronic diseases to provide the most appropriate treatment and avoid unnecessary medications. The classically defined phenotypes of chronic bronchitis and emphysema, together with the newly described phenotypes of overlap COPD-asthma and frequent exacerbator, allow a simple classification of patients that share clinical characteristics and outcomes and, more importantly, similar responses to existing treatments. These clinical phenotypes can help clinicians identify patients that respond to specific pharmacological interventions. For example, frequent exacerbators are the only subjects with an indication for anti-inflammatory treatment in COPD. Among them, those with chronic bronchitis are the only candidates to receive phosphodiesterase-4 inhibitors. Patients with overlap COPD-asthma phenotype show an enhanced response to inhaled corticosteroids and infrequent exacerbators should only receive bronchodilators. These well-defined clinical phenotypes could potentially be incorporated into treatment guidelines.
Revealing Trends Based on Defined Queries in Biological Publications Using Cosine Similarity

Extracting valuable information in terms of number and content of published papers in any field of research will simplify decision making for future researches and investments. A novel and simple text mining approach, called TrendFinder, has been developed in this paper to reveal the content-based trends of expert-defined queries in selected biological published papers during the last five decades. So, in order to evaluate the results, three different data sets were collected and four vectors of selected keywords were considered as the four queries. "Title", "Published date" and the "Abstract" were downloaded for three series of journals namely, "Conservation Biology", "Ecology", and "The American Naturalist" as data sets, including total number of 19,010 papers. In order to show the trend between each query and the Abstract of each paper, Cosine similarity method was used by TrendFinder. Afterwards, three diagrams were demonstrated content-based trends of the four defined queries on the three provided data sets.
Method-level code clone detection for java through hybrid approach

A software clone is an active research area where several researchers have investigated techniques to automatically detect duplicated code in programs. However their researches have limitations either in finding the structural or functional clones. Moreover, all these techniques detected only the first three types of clones. In this paper, we propose a hybrid approach combining metric-based approach with textual analysis of the source code for the detection of both syntactical and functional clones in a given Java source code. This proposal is also used to detect all four types of clones. The detection process makes use of a set of metrics calculated for each type of clones. A tool named CloneManager is developed based on this method in Java for high portability and platform-independency. The various types of clones detected by the tool are classified and clustered as clone clusters. The tool is also tested with seven existing open source projects developed in Java and compared with the existing approaches.
The Development of an Angiogenic Protein “Signature” in Ovarian Cancer Ascites as a Tool for Biologic and Prognostic Profiling

Advanced ovarian cancer (AOC) is one of the leading lethal gynecological cancers in developed countries. Based on the important role of angiogenesis in ovarian cancer oncogenesis and expansion, we hypothesized that the development of an “angiogenic signature” might be helpful in prediction of prognosis and efficacy of anti-angiogenic therapies in this disease. Sixty-nine samples of ascitic fluid- 35 from platinum sensitive and 34 from platinum resistant patients managed with cytoreductive surgery and 1st-line carboplatin-based chemotherapy- were analyzed using the Proteome ProfilerTM Human Angiogenesis Array Kit, screening for the presence of 55 soluble angiogenesis-related factors. A protein profile based on the expression of a subset of 25 factors could accurately separate resistant from sensitive patients with a success rate of approximately 90%. The protein profile corresponding to the “sensitive” subset was associated with significantly longer PFS (8 [95% Confidence Interval {CI}: 8–9] vs. 20 months [95% CI: 15–28]; Hazard ratio {HR}: 8.3, p<0.001) and OS (20.5 months [95% CI: 13.5–30] vs. 74 months [95% CI: 36-not reached]; HR: 5.6 [95% CI: 2.8–11.2]; p<0.001). This prognostic performance was superior to that of stage, histology and residual disease after cytoreductive surgery and the levels of vascular endothelial growth factor (VEGF) in ascites. In conclusion, we developed an “angiogenic signature” for patients with AOC, which can be used, after appropriate validation, as a prognostic marker and a tool for selection for anti-angiogenic therapies.
Behavior Based Social Dimensions Extraction for Multi-Label Classification

Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes’ behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes’ connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions.
Application of DNA Hybridization Biosensor as a Screening Method for the Detection of Genetically Modified Food Components

An electrochemical biosensor for the detection of genetically modified food components is presented. The biosensor was based on 21-mer single-stranded oligonucleotide (ssDNA probe) specific to either 35S promoter or nos terminator, which are frequently present in transgenic DNA cassettes. ssDNA probe was covalently attached by 5′-phosphate end to amino group of cysteamine self-assembled monolayer (SAM) on gold electrode surface with the use of activating reagents – water soluble 1-ethyl-3(3′-dimethylaminopropyl)-carbodiimide (EDC) and N-hydroxy-sulfosuccinimide (NHS). The hybridization reaction on the electrode surface was detected via methylene blue (MB) presenting higher affinity to ssDNA probe than to DNA duplex. The electrode modification procedure was optimized using 19-mer oligoG and oligoC nucleotides. The biosensor enabled distinction between DNA samples isolated from soybean RoundupReady® (RR soybean) and non-genetically modified soybean. The frequent introduction of investigated DNA sequences in other genetically modified organisms (GMOs) give a broad perspectives for analytical application of the biosensor.
CrowdTrack : Interactive Tracking of Cells in Microscopy Image Sequences with Crowdsourcing Support ‡

Outlining live cells and keeping track of their shape in microscopy videos are urgent tasks for biologists and medical researchers. As manual annotation by experts is timeconsuming, automated solutions for specific types of cells or imaging modalities have been developed. We here propose CrowdTrack, a hybrid human-computer tracking method that can track various types of cells in fluorescence and phase-contrast microscopy videos by involving crowdsourcing whenever the performance of automated methods is unsatisfactory. We tested our proposed method on 1,523 frames from 12 different microscopy videos and obtained 14,351 cell outlines with only 32 5-worker rounds of crowdsourcing. CrowdTrack produced accurate cell outlines and correct cell lineage trees.
A probabilistic, multivariate approach for object recognition in thermal infra-red images

For any task that autonomous vehicles may encounter in unstructured outdoor environments a reliable vision system is a key point for success. That is especially true with an autonomous convoy, where each vehicle has to track and follow the one in front. When applying a multivariate based approach for object detection, dimensional reduction of processed data is a vital part of any algorithm. Based on probabilistic classification into two classes (positive and negative) three different approaches for dimensional reduction are examined in this paper: The first method transforms new images in two reduced principal component analysis (PCA) spaces, derived from negative and positive training images respectively. The second approach calculates a mutual PCA space from all training images and the third strategy uses linear discriminant analysis (LDA) for data reduction. In these reduced spaces image classification is done with the Gaussian classifier. Through experiments it is shown that classification in the mutual PCA and the LDA space result in fewer errors and a more reliable class assignment. Furthermore, the use of LDA is more robust if confronted with incomplete training data. Finally it is shown that a confidence approximation using Gaussian processes can, if trained, identify positive and negative images and evaluates untrained images with the appropriate uncertainty.