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    Learning of tree-structured gaussian graphical models on distributed data under communication constraints

    , Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) Tavassolipour, M ; Motahari, A ; Manzuri Shalmani, M. T ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our... 

    Learning of tree-structured Gaussian graphical models on distributed data under communication constraints

    , Article IEEE Transactions on Signal Processing ; Volume 67, Issue 1 , 2019 , Pages 17-28 ; 1053587X (ISSN) Tavassolipour, M ; Motahari, S. A ; Manzuri Shalmani, M. T ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our... 

    A hybrid deep model for automatic arrhythmia classification based on LSTM recurrent networks

    , Article 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020, 1 June 2020 through 3 June 2020 ; 2020 Bitarafan, A ; Amini, A ; Baghshah, M. S ; Khodajou Chokami, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Abstract
    Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term... 

    Optimal sensors layout design based on reference-free damage localization with lamb wave propagation

    , Article Structural Control and Health Monitoring ; Volume 27, Issue 4 , 10 January , 2020 Keshavarz Motamed, P ; Abedian, A ; Nasiri, M ; Sharif University of Technology
    John Wiley and Sons Ltd  2020
    Abstract
    This study presents a new approach for designing optimal sensors layout based on accuracy of defect mapping. It is obtained from combination of the reference-free damage detection technique and the probability-based diagnostic imaging method. Considering damage indices based on continuous wavelet transform of sensors signals, the core of this study involves with development of a database of continuous wavelet transform features of a crack. In fact, the database contains the data from 594 different states in crack positions, orientations, and the considered sensing path lengths. Eventually, this database is used for localization of damage by interpolating the stored data collected from the... 

    Spectral subtraction in likelihood-maximizing framework for robust speech recognition

    , Article INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association, Brisbane, QLD, 22 September 2008 through 26 September 2008 ; December , 2008 , Pages 980-983 ; 19909772 (ISSN) Baba Ali, B ; Sameti, H ; Safayani, M ; Sharif University of Technology
    2008
    Abstract
    Spectral Subtraction (SS), as a speech enhancement technique, originally designed for improving quality of speech signal judged by human listeners. it usually improve the quality and intelligibility of speech signals, while the speech recognition systems need compensation techniques capable of reducing the mismatch between the noisy speech features and the clean models. This paper proposes a novel approach for solving this problem by considering the SS and the speech recognizer as two interconnected components, sharing the common goal of improved speech recognition accuracy. The experimental evaluations on a real recorded database and the TIMIT database show that the proposed method can... 

    The effect of magnesium supplementation on anthropometric indices: A systematic review and dose-response meta-analysis of clinical trials

    , Article British Journal of Nutrition ; Volume 125, Issue 6 , 2021 , Pages 644-656 ; 00071145 (ISSN) Rafiee, M ; Ghavami, A ; Rashidian, A ; Hadi, A ; Askari, G ; Sharif University of Technology
    Cambridge University Press  2021
    Abstract
    Abstract The aim of this study was to determine the effects of Mg supplementation on anthropometric indices consisting of body weight, waist circumference (WC), BMI and body fat percentage. In this systematic review and dose-response meta-analysis, we searched PubMed, Cochrane Library, Scopus, Web of Science and Google Scholar from databases inception up to February 2020 for relevant randomised controlled trials. Quality of evidence was evaluated using the Cochrane Collaboration Tool. All the outcomes of this meta-analysis were pooled using the random effect model. Analysis of dose-response for Mg dosage was carried out using a fractional polynomial model. The systematic review and... 

    Numerical-probabilistic modeling of the liquefaction-induced free fields settlement

    , Article Soil Dynamics and Earthquake Engineering ; Volume 149 , 2021 ; 02677261 (ISSN) Sadeghi, H ; Pak, A ; Pakzad, A ; Ayoubi, P ; Sharif University of Technology
    Elsevier Ltd  2021
    Abstract
    Liquefaction is a phenomenon through which saturated sandy soil loses its shear strength and turns into a liquefied state. One of the most detrimental consequences of liquefaction is the reconsolidation volumetric settlements after the earthquakes, which is due to the dissipation of excess pore pressure caused by earthquakes. Severe floods can follow these settlements in free fields such as grounds close to the sea or rivers. Several researchers studied this phenomenon using data obtained from experiments in the lab or observations in the fields. Previous works were mainly based on a limited number of experimental observations and considered loadings and boundary conditions that were... 

    BIM and machine learning in seismic damage prediction for non-structural exterior infill walls

    , Article Automation in Construction ; Volume 139 , 2022 ; 09265805 (ISSN) Mousavi, M ; TohidiFar, A ; Alvanchi, A ; Sharif University of Technology
    Elsevier B.V  2022
    Abstract
    Despite the seismic vulnerability of non-structural Exterior Infill Walls (EIWs), their resilient design has received minimal attention. This study addresses the issue by proposing a novel framework for predicting possible damage states of EIWs. The framework benefits from an automated combination of Building Information Modeling as a visualized 3D database of the building's components and the Machine Learning classification as the prediction engine. The framework's applicability is studied in a Proof of Concept example of the exterior walls of the buildings damaged in the 2017 earthquake in Kermanshah, Iran. The Extremely Randomized Trees classifier produced the best results for predicting... 

    Discovering dominant pathways and signal-response relationships in signaling networks through nonparametric approaches

    , Article Genomics ; Volume 102, Issue 4 , October , 2013 , Pages 195-201 ; 08887543 (ISSN) Nassiri, I ; Masoudi Nejad, A ; Jalili, M ; Moeini, A ; Sharif University of Technology
    2013
    Abstract
    A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises... 

    A data mining approach for diagnosis of coronary artery disease

    , Article Computer Methods and Programs in Biomedicine ; Volume 111, Issue 1 , 2013 , Pages 52-61 ; 01692607 (ISSN) Alizadehsani, R ; Habibi, J ; Hosseini, M. J ; Mashayekhi, H ; Boghrati, R ; Ghandeharioun, A ; Bahadorian, B ; Sani, Z. A ; Sharif University of Technology
    2013
    Abstract
    Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to... 

    Knowledge-based design of space-time transmit code and receive filter for a multiple-input-multiple-output radar in signal-dependent interference

    , Article IET Radar, Sonar and Navigation ; Volume 9, Issue 8 , 2015 , Pages 1124-1135 ; 17518784 (ISSN) Karbasi, S. M ; Aubry, A ; Carotenuto, V ; Naghsh, M. M ; Bastani, M. H ; Sharif University of Technology
    Institution of Engineering and Technology  2015
    Abstract
    The authors deal with the robust design of multiple-input-multiple-output (MIMO) space-time transmit code (STTC) and space-time receive filter (STRF) for a point-like target embedded in signal-dependent interference. Specifically, they assume that the radar exploits knowledge provided by dynamic environmental database, to roughly predict the actual scattering scenario. Then, they devise an iterative method to optimise the (constrained) STTC and the (constrained) STRF which sequentially improves the worst-case (over interfering scatterers statistics) signal-to-interference-plus-noise ratio (SINR). Each iteration of the algorithm is handled via solving two (hidden) convex optimisation... 

    Using Web-GIS technology as a smart tool for resiliency management to monitor wind farms performances (Ganjeh site, Iran)

    , Article International Journal of Environmental Science and Technology ; Volume 16, Issue 9 , 2019 , Pages 5011-5022 ; 17351472 (ISSN) Aghajani, D ; Abbaspour, M ; Radfar, R ; Mohammadi, A ; Sharif University of Technology
    Center for Environmental and Energy Research and Studies  2019
    Abstract
    Considering the wide spread locations of wind farms in Iran, it is important to develop a suitable decision support system (DSS) to fulfill proper management of wind farms. Extensive literature survey indicates that there are no integrated forms of DSS to manage a set of wind farms. The existing wind farms are performing independently, and there is no practical method for exchanging the online data. DSS can contribute to optimal operation of wind farms, operation and maintenance scheduling, pricing policy, etc. In this study, a geographic information system and RETSCREEN software were linked to the designed DSS to achieve a more suitable result. Also, a huge number of data are constantly... 

    A new word clustering method for building n-gram language models in continuous speech recognition systems

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 18 June 2008 through 20 June 2008, Wroclaw ; Volume 5027 LNAI , 2008 , Pages 286-293 ; 03029743 (ISSN) ; 354069045X (ISBN); 9783540690450 (ISBN) Bahrani, M ; Sameti, H ; Hafezi, N ; Momtazi, S ; Sharif University of Technology
    2008
    Abstract
    In this paper a new method for automatic word clustering is presented. We used this method for building n-gram language models for Persian continuous speech recognition (CSR) systems. In this method, each word is specified by a feature vector that represents the statistics of parts of speech (POS) of that word. The feature vectors are clustered by k-means algorithm. Using this method causes a reduction in time complexity which is a defect in other automatic clustering methods. Also, the problem of high perplexity in manual clustering methods is abated. The experimental results are based on "Persian Text Corpus" which contains about 9 million words. The extracted language models are evaluated... 

    Secure steganography using Gabor filter and neural networks

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 4920 LNCS , 2008 , Pages 33-49 ; 03029743 (ISSN); 3540690166 (ISBN); 9783540690160 (ISBN) Jamzad, M ; Zahedi Kermani, Z ; Sharif University of Technology
    2008
    Abstract
    The main concern of steganography (image hiding) methods is to embed a secret image into a host image in such a way that it causes minimum distortion to the host; to make it possible to extract a version of secret image from the host in such a way that the extracted version of secret image be as similar as possible to its original version (this should be possible even after usual attacks on the host image), and to provide ways of embedding secret images with larger size into a given host image. In this paper we propose a method that covers all above mentioned concerns by suggesting the idea of finding from an image data base, the most suitable host for a given secret image. In our method,... 

    Pan-cancer analysis of microRNA expression profiles highlights microRNAs enriched in normal body cells as effective suppressors of multiple tumor types: A study based on TCGA database

    , Article PLoS ONE ; Volume 17, Issue 4 April , 2022 ; 19326203 (ISSN) Moradi, S ; Kamal, A ; Es, H. A ; Farhadi, F ; Ebrahimi, M ; Chitsaz, H ; Sharifi Zarchi, A ; Baharvand, H ; Sharif University of Technology
    Public Library of Science  2022
    Abstract
    Background MicroRNAs (miRNAs) are frequently deregulated in various types of cancer. While antisense oligonucleotides are used to block oncomiRs, delivery of tumour-suppressive miRNAs holds great potential as a potent anti-cancer strategy. Here, we aim to determine, and functionally analyse, miRNAs that are lowly expressed in various types of tumour but abundantly expressed in multiple normal tissues. Methods The miRNA sequencing data of 14 cancer types were downloaded from the TCGA dataset. Significant differences in miRNA expression between tumor and normal samples were calculated using limma package (R programming). An adjusted p value < 0.05 was used to compare normal versus tumor miRNA... 

    An interactive cbir system based on anfis learning scheme for human brain magnetic resonance images retrieval

    , Article Biomedical Engineering - Applications, Basis and Communications ; Volume 24, Issue 1 , 2012 , Pages 27-36 ; 10162372 (ISSN) Tarjoman, M ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
    2012
    Abstract
    Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the... 

    MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments

    , Article PLoS Computational Biology ; Volume 18, Issue 6 , 2022 ; 1553734X (ISSN) Alinejad Rokny, H ; Modegh, R. G ; Rabiee, H. R ; Sarbandi, E. R ; Rezaie, N ; Tam, K. T ; Forrest, A. R. R ; Sharif University of Technology
    Public Library of Science  2022
    Abstract
    Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly...