ORCID Profile
0000-0001-6916-7907
Current Organisation
University of Western Australia
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Springer Science and Business Media LLC
Date: 14-12-2018
Publisher: Cold Spring Harbor Laboratory
Date: 21-03-2022
DOI: 10.1101/2022.03.21.485135
Abstract: The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions, but seldom address the much more difficult (and practical) inter-family problem. We demonstrate it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modeled after structure mapping data, that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalisation despite the widespread assumption in the literature, and provide strong evidence that many existing learning-based models have not generalised inter-family. Source code and data is available at arcellszi/dl-rna .
Publisher: Springer Berlin Heidelberg
Date: 1995
DOI: 10.1007/BFB0015406
Publisher: Elsevier BV
Date: 08-2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2005
DOI: 10.1109/TVCG.2005.1
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: IEEE
Date: 12-2012
Publisher: Oxford University Press (OUP)
Date: 08-10-2012
DOI: 10.1093/BIOINFORMATICS/BTS575
Abstract: Motivation: Laboratory RNA structure determination is demanding and costly and thus, computational structure prediction is an important task. Single sequence methods for RNA secondary structure prediction are limited by the accuracy of the underlying folding model, if a structure is supported by a family of evolutionarily related sequences, one can be more confident that the prediction is accurate. RNA pseudoknots are functional elements, which have highly conserved structures. However, few comparative structure prediction methods can handle pseudoknots due to the computational complexity. Results: A comparative pseudoknot prediction method called DotKnot-PW is introduced based on structural comparison of secondary structure elements and H-type pseudoknot candidates. DotKnot-PW outperforms other methods from the literature on a hand-curated test set of RNA structures with experimental support. Availability: DotKnot-PW and the RNA structure test set are available at the web site dotknot.csse.uwa.edu.au w. Contact: janaspe@csse.uwa.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: IEEE
Date: 09-2007
Publisher: Springer Berlin Heidelberg
Date: 2000
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Singapore
Date: 2021
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Elsevier BV
Date: 04-2022
Publisher: IEEE
Date: 2004
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: IEEE
Date: 12-2014
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 10-1992
Publisher: ACM
Date: 05-08-2013
Publisher: ACM
Date: 05-08-2013
Publisher: Elsevier BV
Date: 09-2000
Publisher: Springer Science and Business Media LLC
Date: 09-2004
Publisher: Oxford University Press (OUP)
Date: 05-02-2019
DOI: 10.1093/BIOINFORMATICS/BTH055
Abstract: Summary: Multiple sequence alignment is the NP-hard problem of aligning three or more DNA or amino acid sequences in an optimal way so as to match as many characters as possible from the set of sequences. The popular sequence alignment program ClustalW uses the classical method of approximating a sequence alignment, by first computing a distance matrix and then constructing a guide tree to show the evolutionary relationship of the sequences. We show that parallelizing the ClustalW algorithm can result in significant speedup. We used a cluster of workstations using C and message passing interface for our implementation. Experimental results show that speedup of over 5.5 on six processors is obtainable for most inputs. Availability: The software is available upon request from the second author.
Publisher: ACM
Date: 25-06-2012
Publisher: World Scientific Pub Co Pte Lt
Date: 20-04-2017
DOI: 10.1142/S0217979217420140
Abstract: We present the detailed dynamics of the particles in the [Formula: see text]-Fermi–Pasta–Ulam–Tsingou (FPUT) chain after the initiation of a localized nonlinear excitation (LNE) by squeezing a central bond in the monodispersed chain at time [Formula: see text] = 0 while all other particles remain in their original relaxed positions. In the absence of phonons in the system, the LNE appears to initiate its relaxation process by symmetrically emitting two very weak solitary waves. The next stage involves the spreading of the LNE and the formation of nonsolitary wave-like objects to broaden the excitation region until a stage is reached when many weak solitary wave-like objects can be emitted as the system begins its journey to quasi-equilibrium and then to equilibrium. In addition to being of fundamental interest, these systems may be realized using cantilever systems and could well hold the key to constructing the next generation of broadband energy harvesting systems.
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11424758_10
Publisher: IGI Global
Date: 2019
DOI: 10.4018/978-1-5225-8567-1.CH008
Abstract: Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.
Publisher: Springer International Publishing
Date: 2017
Publisher: Journal of Graph Algorithms and Applications
Date: 2017
DOI: 10.7155/JGAA.00428
Publisher: MDPI AG
Date: 03-06-2022
DOI: 10.3390/APP12115702
Abstract: Epilepsy is one of the most common brain diseases that affects more than 1% of the world’s population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and require professional skills. This paper presents a novel automatic technique that involves the extraction of specific features from epileptic seizures’ EEG signals using dual-tree complex wavelet transform (DTCWT) and classifying them into one of the seven types of seizures, including absence, complex-partial, focal non-specific, generalized non-specific, simple-partial, tonic-clonic, and tonic seizures. We evaluated the proposed technique on the TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with the existing state-of-the-art techniques using the overall F1-score due to class imbalance of seizure types. Our proposed technique achieved the best results of a weighted F1-score of 99.1% and 74.7% for seizure-wise and patient-wise classification, respectively, thereby setting new benchmark results for this dataset.
Publisher: Oxford University Press (OUP)
Date: 29-01-2010
DOI: 10.1093/NAR/GKQ021
Publisher: Springer Berlin Heidelberg
Date: 1988
Publisher: Springer Science and Business Media LLC
Date: 08-2023
DOI: 10.1007/S10723-023-09679-6
Abstract: Containers have emerged recently as a cloud technology for improving and managing cloud resources. They improve resource sharing by allowing instances to run on top of the host’s operating system. Container-based virtualization runs and manages hosted instances via the host kernel. Resource sharing can cause resource contention. In addition, dependent jobs, which may be deployed across multiple hosts, require frequent communication, resulting in a high volume of network traffic and network contention. The majority of existing research focuses on load balancing, with no consideration for the fact that network contention also plays a significant role in container performance. In this research, we propose a Dependency-aware Scheduling algorithm (DAScheduler) that deploys jobs into containers while accounting for both load balancing and job dependencies. The experimental results show that DAScheduler reduces network traffic by more than half and balances the loads. In comparison to one of the existing state-of-the-art techniques, DAScheduler improves overall cloud performance.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2003
Publisher: EManuscript Technologies
Date: 2018
Publisher: IEEE
Date: 12-2018
Publisher: Wiley
Date: 19-03-2016
DOI: 10.1111/PAN.12879
Abstract: Head-mounted devices (HMDs) are of significant interest for applications within medicine, including in anesthesia for patient monitoring. Previous devices trialed in anesthesia for this purpose were often bulky, involved cable tethers, or were otherwise ergonomically infeasible. Google Glass is a modern HMD that is lightweight and solves many of the issues identified with previous HMDs. To examine the acceptance of Google Glass as a patient monitoring device in a pediatric anesthesia context at Princess Margaret Hospital for Children, Perth, Australia. We developed a custom-designed software solution for integrating Google Glass into the anesthesia environment, which enabled the device user to continuously view patient monitoring parameters transmitted wirelessly from the anesthesia workstation. A total of 40 anesthetists were included in the study. Each anesthetist used the device for the duration of a theater list. We found 90% of anesthetists trialing the device agreed that it was comfortable to wear, 86% agreed the device was easy to read, and 82.5% agreed the device was not distracting. In 75% of cases, anesthetists reported unprompted that they were comfortable using the device in theater. Anesthetists reported that they would use the device again in 76% of cases, and indicated that they would recommend the device to a colleague in 58% of cases. Given the pilot nature of this study, we consider these results highly favorable. Anesthetists readily accepted Google Glass in the anesthetic environment, with further enhancements to device software, rather than hardware, now being the barrier to adoption. There are a number of applications for HMDs in pediatric anesthesia.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11890348_34
Publisher: Elsevier BV
Date: 10-2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
Publisher: Institution of Engineering and Technology (IET)
Date: 06-07-2020
Publisher: Cold Spring Harbor Laboratory
Date: 22-11-2011
DOI: 10.1261/RNA.2394511
Abstract: Pseudoknots are an essential feature of RNA tertiary structures. Simple H-type pseudoknots have been studied extensively in terms of biological functions, computational prediction, and energy models. Intramolecular kissing hairpins are a more complex and biologically important type of pseudoknot in which two hairpin loops form base pairs. They are hard to predict using free energy minimization due to high computational requirements. Heuristic methods that allow arbitrary pseudoknots strongly depend on the quality of energy parameters, which are not yet available for complex pseudoknots. We present an extension of the heuristic pseudoknot prediction algorithm DotKnot, which covers H-type pseudoknots and intramolecular kissing hairpins. Our framework allows for easy integration of advanced H-type pseudoknot energy models. For a test set of RNA sequences containing kissing hairpins and other types of pseudoknot structures, DotKnot outperforms competing methods from the literature. DotKnot is available as a web server under dotknot.csse.uwa.edu.au .
Publisher: IEEE
Date: 09-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Publisher: MDPI AG
Date: 27-06-2023
DOI: 10.3390/S23135960
Abstract: Neurological disorders have an extreme impact on global health, affecting an estimated one billion in iduals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet–Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.
Publisher: Elsevier BV
Date: 09-2021
Publisher: MDPI AG
Date: 14-05-2021
DOI: 10.3390/FI13050129
Abstract: Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking. The objectives of this study are to present a comprehensive overview of research on hashtag recommendation for tweets and present insights from previous research papers. In this paper, we search for articles related to our research between 2010 and 2020 from CiteSeer, IEEE Xplore, Springer and ACM digital libraries. From the 61 articles included in this study, we notice that most of the research papers were focused on the textual content of tweets instead of other data. Furthermore, collaborative filtering methods are seldom used solely in hashtag recommendation. Taking this perspective, we present a taxonomy of hashtag recommendation based on the research methodologies that have been used. We provide a critical review of each of the classes in the taxonomy. We also discuss the challenges remaining in the field and outline future research directions in this area of study.
Publisher: Springer Science and Business Media LLC
Date: 12-08-2015
Publisher: Elsevier BV
Date: 07-2005
Publisher: Elsevier BV
Date: 10-2022
Publisher: American Physical Society (APS)
Date: 24-04-2018
Publisher: Elsevier BV
Date: 07-2023
Publisher: Springer Berlin Heidelberg
Date: 1994
Publisher: Springer Science and Business Media LLC
Date: 11-2012
Publisher: IEEE
Date: 03-2009
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 08-2004
Publisher: Springer Nature Singapore
Date: 2023
Publisher: Informa UK Limited
Date: 1990
Publisher: Oxford University Press (OUP)
Date: 06-06-2017
DOI: 10.1093/NAR/GKX512
Publisher: Elsevier
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2006
DOI: 10.1109/TMC.2006.83
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2008
DOI: 10.1142/S0219265908002254
Abstract: Existing TDMA-based MAC protocols for wireless sensor networks are not specifically built to consider communication channels that are prone to fading. We describe the impact of periodically changing environment on small-scale fading effects in industrial indoor wireless networks. Using a site-specific ray tracer, we show that the position of nodes and the periodic movements of objects with constant velocities in the environment have significant impact on signal fading. Finding that fading is approximately periodic, we propose a TDMA-based MAC protocol for wireless sensor networks built for industrial applications that uses link state dependent scheduling. In our approach, nodes gather s les of the channel quality and generate prediction sets from the s le sets in independent slots. Using the prediction sets, nodes only wake up to transmit/receive during scheduled slots that are predicted to be clear and sleep during scheduled slots that may potentially cause a transmitted signal to fade. We simulate our proposed protocol and compare its performance with the well published Z-MAC protocol. We found that our protocol significantly improves packet throughput and energy consumption as compared to Z-MAC. We also found that in conditions which are not perfect under our assumptions, the performance of our protocol degrades gracefully.
Publisher: No publisher found
Date: 2001
Publisher: Walter de Gruyter GmbH
Date: 03-2019
Abstract: Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset, where a subspace is a subset of dimensions of the data. But the exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, which means that parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation shows linear speedup. Moreover, we develop an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.
Publisher: Oxford University Press (OUP)
Date: 2010
Abstract: We present a new method by which the total masses of galaxies including dark matter can be estimated from the kinematics of their globular cluster systems (GCSs). In the proposed method, we apply the convolutional neural networks (CNNs) to the 2D maps of line-of-sight velocities (V) and velocity dispersions (σ) of GCSs predicted from numerical simulations of disc and elliptical galaxies. In this method, we first train the CNN using either only a larger number ($\\sim 200\\, 000$) of the synthesized 2D maps of σ (‘one-channel’) or those of both σ and V (‘two-channel’). Then, we use the CNN to predict the total masses of galaxies (i.e. test the CNN) for the totally unknown data set that is not used in training the CNN. The principal results show that overall accuracy for one-channel and two-channel data is 97.6 per cent and 97.8 per cent, respectively, which suggests that the new method is promising. The mean absolute errors (MAEs) for one-channel and two-channel data are 0.288 and 0.275, respectively, and the value of root mean square errors (RMSEs) are 0.539 and 0.51 for one-channel and two-channel, respectively. These smaller MAEs and RMSEs for two-channel data (i.e. better performance) suggest that the new method can properly consider the global rotation of GCSs in the mass estimation. We also applied our proposed method to real data collected from observations of NGC 3115 to compare the total mass predicted by our proposed method and other popular methods from the literature.
Publisher: Elsevier BV
Date: 02-2007
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11424857_23
Publisher: Springer Science and Business Media LLC
Date: 2002
Publisher: Elsevier BV
Date: 07-2010
Publisher: Cold Spring Harbor Laboratory
Date: 26-02-2008
DOI: 10.1261/RNA.968808
Abstract: Pseudoknots are folded structures in RNA molecules that perform essential functions as part of cellular transcription machinery and regulatory processes. The prediction of these structures in RNA molecules has important implications in antiviral drug design. It has been shown that the prediction of pseudoknots is an NP-complete problem. Practical structure prediction algorithms based on free energy minimization employ a restricted problem class and dynamic programming. However, these algorithms are computationally very expensive, and their accuracy deteriorates if the input sequence containing the pseudoknot is too long. Heuristic methods can be more efficient, but do not guarantee an optimal solution in regards to the minimum free energy model. We present KnotSeeker, a new heuristic algorithm for the detection of pseudoknots in RNA sequences as a preliminary step for structure prediction. Our method uses a hybrid sequence matching and free energy minimization approach to perform a screening of the primary sequence. We select short sequence fragments as possible candidates that may contain pseudoknots and verify them by using an existing dynamic programming algorithm and a minimum weight independent set calculation. KnotSeeker is significantly more accurate in detecting pseudoknots compared to other common methods as reported in the literature. It is very efficient and therefore a practical tool, especially for long sequences. The algorithm has been implemented in Python and it also uses C/C++ code from several other known techniques. The code is available from www.csse.uwa.edu.au/∼datta seudoknot .
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: American Physical Society (APS)
Date: 18-09-2017
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Elsevier BV
Date: 09-2023
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11576259_47
Publisher: Elsevier BV
Date: 06-1999
Publisher: IGI Global
Date: 2020
DOI: 10.4018/978-1-7998-0182-5.CH002
Abstract: Computational neuroscience is inspired by the mechanism of the human brain. Neural networks have reformed machine learning and artificial intelligence. Deep learning is a type of machine learning that teaches computers to do what comes naturally to in iduals: acquire by ex le. It is inspired by biological brains and became the essential class of models in the field of machine learning. Deep learning involves several layers of computation. In the current scenario, researchers and scientists around the world are focusing on the implementation of different deep models and architectures. This chapter consists the information about major architectures of deep network. That will give the information about convolutional neural network, recurrent neural network, multilayer perceptron, and many more. Further, it discusses CNN (convolutional neural network) and its different pretrained models due to its major requirements in visual imaginary. This chapter also deliberates about the similarity of deep model and architectures with the human brain.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2002
DOI: 10.1109/71.993203
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Springer Science and Business Media LLC
Date: 03-1992
DOI: 10.1007/BF02811344
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2005
DOI: 10.1109/TC.2005.172
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11527503_12
Publisher: MDPI AG
Date: 08-12-2020
DOI: 10.3390/W12123439
Abstract: Small leaks in water distribution networks have been a major problem both economically and environmentally, as they go undetected for years. We model the signature of small leaks as a unique Directed Acyclic Graph, called the Lean Graph, to find the best places for k sensors for detecting and locating small leaks. We use the sensors to develop dictionaries that map each leak signature to its location. We quantify leaks by matching out-of-normal flows detected by sensors against records in the selected dictionaries. The most similar records of the dictionaries are used to quantify the leaks. Finally, we investigate how much our approach can tolerate corrupted data due to sensor failures by introducing a subspace voting based quantification method. We tested our method on water distribution networks of literature and simulate small leaks ranging from [0.1, 1.0] liter per second. Our experimental results prove that our sensor placement strategy can effectively place k sensors to quantify single and multiple small leaks and can tolerate corrupted data up to some range while maintaining the performance of leak quantification. These outcomes indicate that our approach could be applied in real water distribution networks to minimize the loss caused by small leaks.
Publisher: IEEE
Date: 11-2018
Publisher: Wiley
Date: 03-2009
DOI: 10.1002/NEM.689
Publisher: Elsevier BV
Date: 04-2022
Publisher: Springer Berlin Heidelberg
Date: 1993
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Springer Berlin Heidelberg
Date: 1993
Publisher: IEEE
Date: 04-12-2021
Publisher: Elsevier BV
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: IEEE
Date: 09-2012
Publisher: Elsevier BV
Date: 09-2006
Publisher: World Scientific Pub Co Pte Ltd
Date: 02-2007
DOI: 10.1142/S0129054107004607
Abstract: We show that two data structures, min-augmented range tree and priority search pennant, efficiently support all the required operations for updatable IP router tables and argue that both structures are better suited for the one-dimensional dynamic IP lookup problem than the priority search tree (PST) used in a previous solution. It is possible to maintain both structures in time O(1) after a rotation, while PST with n elements may require Ω( log n) steps for a single rotation. Therefore, the proposed structures can be balanced using a larger class of rebalancing schemes compared to PST. Both structures are also of interest independently of the IP lookup problem and may be used as attractive implementations of priority search queues in other contexts as well.
Publisher: Elsevier BV
Date: 04-2002
Publisher: Springer Singapore
Date: 2019
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11424826_72
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Springer Science and Business Media LLC
Date: 19-01-2021
Publisher: World Scientific Pub Co Pte Ltd
Date: 04-2007
DOI: 10.1142/S0129054107004693
Abstract: We show that two data structures, min-augmented range tree and priority search pennant, efficiently support all the required operations for updatable IP router tables and argue that both structures are better suited for the one-dimensional dynamic IP lookup problem than the priority search tree (PST) used in a previous solution. It is possible to maintain both structures in time O(1) after a rotation, while PST with n elements may require Ω( log n) steps for a single rotation. Therefore, the proposed structures can be balanced using a larger class of rebalancing schemes compared to PST. Both structures are also of interest independently of the IP lookup problem and may be used as attractive implementations of priority search queues in other contexts as well.
Publisher: Springer Berlin Heidelberg
Date: 1997
Publisher: Elsevier BV
Date: 08-2021
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2003
Publisher: Elsevier BV
Date: 03-2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2004
Publisher: Elsevier BV
Date: 02-2010
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 1994
Publisher: Elsevier BV
Date: 08-2022
Publisher: Journal of Graph Algorithms and Applications
Date: 2018
DOI: 10.7155/JGAA.00476
Publisher: Oxford University Press (OUP)
Date: 28-03-2019
DOI: 10.1093/BIOINFORMATICS/BTZ222
Abstract: Predicting the secondary structure of RNA is a fundamental task in bioinformatics. Algorithms that predict secondary structure given only the primary sequence, and a model to evaluate the quality of a structure, are an integral part of this. These algorithms have been updated as our model of RNA thermodynamics changed and expanded. An exception to this has been the treatment of multi-loops. Although more advanced models of multi-loop free energy change have been suggested, a simple, linear model has been used since the 1980s. However, recently, new dynamic programing algorithms for secondary structure prediction that could incorporate these models were presented. Unfortunately, these models appear to have lower accuracy for secondary structure prediction. We apply linear regression and a new parameter optimization algorithm to find better parameters for the existing linear model and advanced non-linear multi-loop models. These include the Jacobson-Stockmayer and Aalberts & Nandagopal models. We find that the current linear model parameters may be near optimal for the linear model, and that no advanced model performs better than the existing linear model parameters even after parameter optimization. Source code and data is available at axhwardg/advanced_multiloops. Supplementary data are available at Bioinformatics online.
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: IEEE
Date: 04-2012
Publisher: IEEE
Date: 04-2012
Publisher: Springer Singapore
Date: 2019
Publisher: Oxford University Press (OUP)
Date: 24-06-2022
DOI: 10.1093/BIOINFORMATICS/BTAC415
Abstract: The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions but seldom address the much more difficult (and practical) inter-family problem. We demonstrate that it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modelled after structure mapping data that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalization despite the widespread assumption in the literature and provide strong evidence that many existing learning-based models have not generalized inter-family. Source code and data are available at arcellszi/dl-rna. Supplementary data are available at Bioinformatics online.
Publisher: Cold Spring Harbor Laboratory
Date: 04-08-2023
DOI: 10.1101/2023.08.04.551958
Abstract: Determining RNA secondary structure is a core problem in computational biology. Fast algorithms for predicting secondary structure are fundamental to this task. We describe a modified formulation of the Zuker-Stiegler algorithm with coaxial stacking, a stabilizing interaction in which the ends of multi-loops are stacked. In particular, optimal coaxial stacking is computed as part of the dynamic programming state, rather than inline. We introduce a new notion of sparsity, which we call replaceability . The modified formulation along with replaceability allows sparsification to be applied to coaxial stacking as well, which increases the speed of the algorithm. We implemented this algorithm in software we call memerna , which we show to have the fastest exact RNA folding implementation out of several popular RNA folding packages supporting coaxial stacking. We also introduce a new notation for secondary structure which includes coaxial stacking, terminal mismatches, and dangles (CTDs) information.
Publisher: IEEE
Date: 24-01-2021
Publisher: Association for Computing Machinery (ACM)
Date: 11-11-2013
Abstract: Every regression testing cycle of a software application results in new test cases being introduced in a test suite. Many test cases from previous regression testing cycles become unstable or unusable due to the removal/addition of the new functionalities. The execution of a large number of unusable test cases results in less test coverage and higher test execution time. The lower test coverage is due to the coverage of the non-existent code statements. The higher test execution time is due to the execution of unused and broken test cases. In this paper, we propose a new bipartite graph approach to eliminate the subset of test cases that are not relevant for the testing of the current version of a software application. The suggested approach helps in executing a minimal set of test cases that are required to cover more code statements.
Publisher: IEEE
Date: 2007
Publisher: ACM
Date: 21-04-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: American Physical Society (APS)
Date: 26-11-2019
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: IGI Global
Date: 2020
Abstract: In the last few years deep learning (DL) has gained a great attention in modern technology. By using a deep learning method, we can analyse different types of data in different domains which is near to the accuracy of humans. As DL is our upcoming technology and it is also under development, we can say DL is the successor of machine learning (ML) technique. In the present era, ML is used everywhere, wherever we need to analyse statistical data. As we can say DL is our future technology that going to cover every sector of our modern industry, one question always remains: why we are lagging? So, the simple answer in terms of analysing any algorithm is complexity, both time and space. DL needs a large artificial neural network (ANN) with hundreds of hidden layers trained with a huge amount of data. So, to performing these tasks we need high-performance computing device that is very expensive in nowadays. With the growing industries of semiconducting devices, we can easily say that the future of DL is about to come with developing artificial intelligence (AI). As an ex le, in 2009, the Google Brain, a deep learning artificial intelligence team of Google introduced a Nvidia GPU which increased the learning speed of DL system by 100 times. As of 2017, the intermediate connection of networks increases to a few million units from few thousand, this network can perform several tasks like object recognition, pattern recognition, speech recognition, and image restoration. It has a greater scope in bioengineering since each living organism contains a huge amount data it can be used for disease diagnosis, rehabilitation, and treatment. It can also help by using data to find the different features and helps us to take several possible decisions in real time. In this review, we found that DL can be very helpful for diagnosing neurological disorders by its symptoms, because DL can be used to identify patterns for each disorder for identification. The benefit is learning how DL can be helpful identifying different neuronal disorders based on different neuropsychiatric symptoms.
Publisher: Diva Enterprises Private Limited
Date: 2022
Publisher: Elsevier BV
Date: 2022
Publisher: Elsevier BV
Date: 11-1995
Publisher: IOP Publishing
Date: 14-05-2010
No related grants have been discovered for Amitava Datta.