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2022 ◽  
pp. 1-13
Author(s):  
Denis Paperno

Abstract Can recurrent neural nets, inspired by human sequential data processing, learn to understand language? We construct simplified datasets reflecting core properties of natural language as modeled in formal syntax and semantics: recursive syntactic structure and compositionality. We find LSTM and GRU networks to generalise to compositional interpretation well, but only in the most favorable learning settings, with a well-paced curriculum, extensive training data, and left-to-right (but not right-to-left) composition.


Author(s):  
Е.П. Трофимов

Предложен алгоритм последовательной обработки данных на основе блочного псевдообращения матриц полного столбцового ранга. Показывается, что формула блочного псевдообращения, лежащая в основе алгоритма, является обобщением одного шага алгоритма Гревиля псевдообращения в невырожденном случае и потому может быть использована для обобщения метода нахождения весов нейросетевой функции LSHDI (linear solutions to higher dimensional interlayer networks), основанного на алгоритме Гревиля. Представленный алгоритм на каждом этапе использует найденные на предыдущих этапах псевдообратные к блокам матрицы и, следовательно, позволяет сократить вычисления не только за счет работы с матрицами меньшего размера, но и за счет повторного использования уже найденной информации. Приводятся примеры применения алгоритма для восстановления искаженных работой фильтра (шума) одномерных сигналов и двумерных сигналов (изображений). Рассматриваются случаи, когда фильтр является статическим, но на практике встречаются ситуации, когда матрица фильтра меняется с течением времени. Описанный алгоритм позволяет непосредственно в процессе получения входного сигнала перестраивать псевдообратную матрицу с учетом изменения одного или нескольких блоков матрицы фильтра, и потому алгоритм может быть использован и в случае зависящих от времени параметров фильтра (шума). Кроме того, как показывают вычислительные эксперименты, формула блочного псевдообращения, на которой основан описываемый алгоритм, хорошо работает и в случае плохо обусловленных матриц, что часто встречается на практике The paper proposes an algorithm for sequential data processing based on block pseudoinverse of full column rank matrixes. It is shown that the block pseudoinverse formula underlying the algorithm is a generalization of one step of the Greville’s pseudoinverse algorithm in the nonsingular case and can also be used as a generalization for finding weights of neural network function in the LSHDI algorithm (linear solutions to higher dimensional interlayer networks). The presented algorithm uses the pseudoinversed matrixes found at each step, and therefore allows one to reduce the computations not only by working with matrixes of smaller size but also by reusing the already found information. Examples of application of the algorithm for signal and image reconstruction are given. The article deals with cases where noise is static but the algorithm is similarly well suited to dynamically changing noises, allowing one to process input data in blocks on the fly, depending on changes. The block pseudoreverse formula, on which the described algorithm is based, works well in the case of ill-conditioned matrixes, which is often encountered in practice


2022 ◽  
Author(s):  
Rie Laurine Rosenthal Johansen ◽  
Anita Sørensen ◽  
Mads Seit Jespersen ◽  
Kamilla Hesthaven Mikkelsen ◽  
Christina Emme

Abstract BackgroundDuring the COVID-19 pandemic, one responsive strategy to ensure hospital staff capacity was reallocation of staff between departments. Unpredicted factors may influence how the strategy is executed. Knowledge of potential moderating factors is essential to improve future staff contingency plans. To understand barriers and promoters of staff realloctation, this study explored the dynamics of reallocating staff from departments with low activity to clinical practice during the first wave of the COVID-19 pandemic at a 530-bed university hospital in the Capital Region of Denmark. MethodsWe used a mixed-methods explanatory design with sequential data collection and analysis. This paper primarily describes the qualitative part of the study, which consisted of six interviews with staff reallocated to clinical practice as part of the staff contingency plan, and seven interviews with leaders of departments that contributed with staff for reallocation. Data was analyzed using inductive content analysis.ResultsThe results showed that the execution of a staff contingency plan during a pandemic is influenced by a complex set of structural, perceptional, social, individual, and psychological moderating factors. Although staff felt obligated and motivated to cover shifts, their actual behavior and experience was influenced by factors such as uncertainty about tasks, family obligations, other work-related tasks, the contingency plan set-up, how the contingency plan, roles, and sense of urgency were interpreted by staff and leaders, and how the leaders prioritized tasks and staff time. Introduction to the unit and tasks, the feeling of being needed, voluntary participation, transparency, collegial sparring, and familiarity with the workplace were factors that promoted a positive experience.ConclusionsThis study identified a variety of complex moderating factors, which should be considered when hospital contingency plans are developed. The study highlights the importance of understanding how reallocated staff and leaders experience and make interpretations and adjustments to a given plan, as this may have great significance for how the contingency plan is put into practice. Future staff contingency plans should take these factors into consideration to make better use of human resources in times of a crisis and to improve staff’s experience with reallocation.


2022 ◽  
Vol 18 (1) ◽  
pp. e1009672
Author(s):  
Gautam Reddy ◽  
Laura Desban ◽  
Hidenori Tanaka ◽  
Julian Roussel ◽  
Olivier Mirat ◽  
...  

Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called “BASS” to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data.


2021 ◽  
Vol 38 (6) ◽  
pp. 1809-1817
Author(s):  
Praveen Kumar Yechuri ◽  
Suguna Ramadass

The advent of social networking and the internet has resulted in a huge shift in how consumers express their loyalty and where firms acquire a reputation. Customers and businesses frequently leave comments, and entrepreneurs do the same. These write-ups may be useful to those with the ability to analyse them. However, analysing textual content without the use of computers and the associated tools is time-consuming and difficult. The goal of Sentiment Analysis (SA) is to discover client feedback, points of view, or complaints that describe the product in a more negative or optimistic light. You can expect this to be a result based on this data if you merely read and assess feedback or examine ratings. There was a time when only the use of standard techniques, such as linear regression and Support Vector Machines (SVM), was effective for the task of automatically discovering knowledge from written explanations, but the older approaches have now been mostly replaced by deep neural networks, and deep learning has gotten the job done. Convolution and compressing RNNs are useful for tasks like machine translation, caption creation, and language modelling, however they suffer from gradient disappearance or explosion issues with large words. This research uses a deep learning RNN for movie review sentiment prediction that is quite comparable to Long Short-Term Memory networks. A LSTM model was well suited for modelling long sequential data. Generally, sentence vectorization approaches are used to overcome the inconsistency of sentence form. We made an attempt to look into the effect of hyper parameters like dropout of layers, activation functions and we also tested the model with different neural network settings and showed results that have been presented in the various ways to take the data into account. IMDB is the official movie database which serves as the basis for all of the experimental studies in the proposed model.


2021 ◽  
Vol 2 (2) ◽  
pp. 121-132
Author(s):  
Husna Sartika ◽  
Eddy Purnama ◽  
Ilyas Ismail

The consequence of the state of the law is legislation to be an essential instrument in regulating public life. However, in some parts of Indonesia, they can make their regional regulation slightly different from the constitution, wherein this article will focus on Qanun in Aceh Province. The research used in this paper is normative law research. This research used sequential data or library data. Secondary data consists of primary law materials, secondary law materials, and tertiary law materials. The approach method used is the legislative approach and the conceptual approach. The formulation of the problem in this paper is how the standard pattern of consideration in the Law, Regional Regulations, and Qanun is based on legislation. The results show that in the Law in Consideration, Consider using the word "membentuk" or "form" because the law-making institution consists of legislative institutions and executive institutions. Regional regulation considers using the word "menetapkan" or "establish" because the institution that makes local regulations is a local government consisting of elements of local governments and local people's representative councils. This consideration follows Annex II of Law Number 12 of 2011 on the Establishment of Legislation as amended by Law Number 15 of 2019. However, the Qanun used the word "membentuk" or "form" due following Article 233 paragraph (1) of Law Number 11 the Year 2006 on Aceh Governance and Annex II of Aceh Qanun Number 5 of 2011 on the Procedures for the Establishment of Qanun.


Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 123
Author(s):  
Ariyo Oluwasanmi ◽  
Muhammad Umar Aftab ◽  
Edward Baagyere ◽  
Zhiguang Qin ◽  
Muhammad Ahmad ◽  
...  

Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios. To accomplish such frontiers, we propose three artificial intelligence models through the application of deep learning algorithms to analyze and detect anomalies in human heartbeat signals. The three proposed models include an attention autoencoder that maps input data to a lower-dimensional latent representation with maximum feature retention, and a reconstruction decoder with minimum remodeling loss. The autoencoder has an embedded attention module at the bottleneck to learn the salient activations of the encoded distribution. Additionally, a variational autoencoder (VAE) and a long short-term memory (LSTM) network is designed to learn the Gaussian distribution of the generative reconstruction and time-series sequential data analysis. The three proposed models displayed outstanding ability to detect anomalies on the evaluated five thousand electrocardiogram (ECG5000) signals with 99% accuracy and 99.3% precision score in detecting healthy heartbeats from patients with severe congestive heart failure.


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Dan-Dong Fang ◽  
Wei Huang ◽  
Gang Cheng ◽  
Xiao-Nan Liu ◽  
Shi-Min Liu ◽  
...  

Glioma is the most common malignant primary brain tumor with an inferior survival period and unsatisfactory prognoses. Identification of novel biomarkers is important for the improvements of clinical outcomes of glioma patients. In recent years, more and more biomarkers were identified in many types of tumors. However, the sensitive markers for diagnoses and prognoses of patients with glioma remained unknown. In the present research, our team intended to explore the expression and clinical significance of ABCC3 in glioma patients. Sequential data filtration (survival analyses, independent prognosis analyses, ROC curve analyses, and clinical association analyses) was completed, which gave rise to the determination of the relationship between glioma and the ABCC3 gene. Clinical assays on the foundation of CGGA and TCGA datasets unveiled that ABCC3 expression was distinctly upregulated in glioma and predicted a shorter overall survival. In the multivariable Cox analysis, our team discovered that the expression of ABCC3 was an independent prognosis marker for both 5-year OS (HR = 1.118, 95% CI: 1.052–1.188; P < 0.001 ). Moreover, our team also studied the association between ABCC3 expression and clinical features of glioma patients, finding that differential expression of ABCC3 was remarkably related to age, 1p19q codeletion, PRS type, chemo status, grade, IDH mutation state, and histology. Overall, our findings suggested ABCC3 might be a novel prognosis marker in glioma.


2021 ◽  
Author(s):  
Ritu ◽  
Sagar Gupta ◽  
Nitesh Kumar Sharma ◽  
Ravi Shankar

Various noncoding elements of genome have gained attention for their regulatory roles where the lncRNAs are very recent and most intriguing for their possible functions. Due to limited information about lncRNAs, their characterization remains a big challenge, especially in plants. Plant lncRNAs differ a lot from others even in the mode of transcription and display poor sequence conservation. Scarce resources exist to annotate for lncRNAs with satisfactory reliability. Here, we present a deep learning approach-based software, DeepPlnc, to accurately identify plant lncRNAs across the plant genomes. DeepPlnc, unlike most of the existing software, can even accurately annotate the incomplete length transcripts also which are very common in de novo assembled transcriptomes. It has incorporated a bi-modal architecture of Convolution Neural Nets while extracting information from the sequences of nucleotides and secondary structure representations for plant lncRNAs. DeepPlnc scored high on all the considered performance metrics while breaching the average accuracy of >95% when tested across different experimentally validated datasets. The software was comprehensively benchmarked against some of the recently published tools to identify the plant lncRNAs where it consistently outperformed all the compared tools for all the performance metrics and for all the considered benchmarking datasets. DeepPlnc will be an important resource for reference free identification and annotation of transcriptome and genome for lncRNAs in plants. DeepPlnc has been made freely available as a web-server at https://scbb.ihbt.res.in/DeepPlnc/. Besides this, a stand-alone version is also provided at GitHub at https://github.com/SCBB-LAB/DeepPlnc/.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Rongbo Chen ◽  
Haojun Sun ◽  
Lifei Chen ◽  
Jianfei Zhang ◽  
Shengrui Wang

AbstractMarkov models are extensively used for categorical sequence clustering and classification due to their inherent ability to capture complex chronological dependencies hidden in sequential data. Existing Markov models are based on an implicit assumption that the probability of the next state depends on the preceding context/pattern which is consist of consecutive states. This restriction hampers the models since some patterns, disrupted by noise, may be not frequent enough in a consecutive form, but frequent in a sparse form, which can not make use of the information hidden in the sequential data. A sparse pattern corresponds to a pattern in which one or some of the state(s) between the first and last one in the pattern is/are replaced by wildcard(s) that can be matched by a subset of values in the state set. In this paper, we propose a new model that generalizes the conventional Markov approach making it capable of dealing with the sparse pattern and handling the length of the sparse patterns adaptively, i.e. allowing variable length pattern with variable wildcards. The model, named Dynamic order Markov model (DOMM), allows deriving a new similarity measure between a sequence and a set of sequences/cluster. DOMM builds a sparse pattern from sub-frequent patterns that contain significant statistical information veiled by the noise. To implement DOMM, we propose a sparse pattern detector (SPD) based on the probability suffix tree (PST) capable of discovering both sparse and consecutive patterns, and then we develop a divisive clustering algorithm, named DMSC, for Dynamic order Markov model for categorical sequence clustering. Experimental results on real-world datasets demonstrate the promising performance of the proposed model.


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