Recurrent Neural Hidden Markov Model for High-order Transition

We propose a method to pay attention to high-order relations among latent states to improve the conventional HMMs that focus only on the latest latent state, since they assume Markov property. To address the high-order relations, we apply an RNN to each sequence of latent states, because the RNN can represent the information of an arbitrary-length sequence with their cell: a fixed-size vector. However, the simplest way, which provides all latent sequences explicitly for the RNN, is intractable due to the combinatorial explosion of the search space of latent states. Thus, we modify the RNN to represent the history of latent states from the beginning of the sequence to the current state with a fixed number of RNN cells whose number is equal to the number of possible states. We conduct experiments on unsupervised POS tagging and synthetic datasets. Experimental results show that the proposed method achieves better performance than previous methods. In addition, the results on the synthetic dataset indicate that the proposed method can capture the high-order relations.

Lateral scanning logging while drilling data processing using convolutional neural networks

This article discusses a new approach to processing lateral scanning logging while drilling data based on a combination of three-dimensional numerical modeling and convolutional neural networks. We prepared dataset for training neural networks. Dataset contains realistic synthetic resistivity images and geoelectric layer boundary layouts, obtained based on true values of their spatial orientation parameters. Using convolutional neural networks two algorithms have been developed and programmatically implemented: suppression of random noise and detection of layer boundaries on the resistivity images. The developed algorithms allow fast and accurate processing of large amounts of data, while, due to the absence of full-connection layers in the neural networks’ architectures, it is possible to process resistivity images of arbitrary length.

Ultrathin and Simple Frequency Selective Rasorber Based on Ferrite Absorber with Embedded Epsilon-Near-Zero Tunneling Channels

An ultrathin and simple frequency-selective rasorber (FSR) with a passband located within a wide absorption band is proposed. The ultrawide absorption band is obtained by employing commercial magnetic materials in the absorption channel and the passband is realized using epsilon-near-zero (ENZ) tunneling waveguides. The attractively ultrathin and simple feature is achieved by utilizing tunneling effect at the cutoff frequency of metallic waveguides with arbitrary length, permitting the overall thickness shrink into to the same as that of the absorber.

Ultrathin and Simple Frequency Selective Rasorber Based on Ferrite Absorber with Embedded Epsilon-Near-Zero Tunneling Channels

An ultrathin and simple frequency-selective rasorber (FSR) with a passband located within a wide absorption band is proposed. The ultrawide absorption band is obtained by employing commercial magnetic materials in the absorption channel and the passband is realized using epsilon-near-zero (ENZ) tunneling waveguides. The attractively ultrathin and simple feature is achieved by utilizing tunneling effect at the cutoff frequency of metallic waveguides with arbitrary length, permitting the overall thickness shrink into to the same as that of the absorber.

ON HIGHER DIMENSIONAL ARITHMETIC PROGRESSIONS IN MEYER SETS

In this paper we study the existence of higher dimensional arithmetic progressions in Meyer sets. We show that the case when the ratios are linearly dependent over ${\mathbb Z}$ is trivial and focus on arithmetic progressions for which the ratios are linearly independent. Given a Meyer set $\Lambda $ and a fully Euclidean model set with the property that finitely many translates of cover $\Lambda $ , we prove that we can find higher dimensional arithmetic progressions of arbitrary length with k linearly independent ratios in $\Lambda $ if and only if k is at most the rank of the ${\mathbb Z}$ -module generated by . We use this result to characterize the Meyer sets that are subsets of fully Euclidean model sets.

PerFSeeB: Designing Long High-weight Single Spaced Seeds for Full Sensitivity Alignment with a Given Number of Mismatches

Background: Technical progress in computational hardware allows researchers to use new approaches for sequence alignment problems. A standard procedure is usually based on pre-aligning of short subsequences followed by proper comparison of neighbouring parts. For this purpose index files are created that store all subsequences (or numbers associated with them) and their positions within a reference sequence. Index files designed on subsequences of 32–64 symbols for a human reference genome can now be easily stored without any compression even on a budget computer. The main goal now is to choose a combination of symbols (a spaced seed) that will tolerate various mismatches between reference and given sequences. An ideal spaced seed should allow us to find all such positions (full sensitivity). By increasing the seed’s weight by one we usually reduce the number of candidate positions fourfold. At the same time longer seeds also reduce the number of signatures to be checked. Results: Several algorithms to assist seed generation are presented. The first one allows us to find all permitted spaced seeds iteratively. The results obtained with the algorithm show specific patterns of the seeds of the highest weight. Among the best seeds, there are periodic seeds with a simple relation between the period of a seed, its length and the length of a read. The second algorithm generates blocks for periodic seeds. A list of blocks is found for blocks of up to 50 symbols and up to 9 mismatches. The third algorithm uses those lists to find spaced seeds for reads of an arbitrary length. Conclusions: Lists of long high-weight spaced seeds are found and available in Supplementary Materials. The seeds are best in terms of weights compared to seeds from other papers and can usually be applied to shorter reads. Codes for all algorithms are available at https://github.com/vtman/PerFSeeB.

Constacyclic Codes over Finite Chain Rings of Characteristic p

Let R be a finite commutative chain ring of characteristic p with invariants p,r, and k. In this paper, we study λ-constacyclic codes of an arbitrary length N over R, where λ is a unit of R. We first reduce this to investigate constacyclic codes of length ps (N=n1ps,p∤n1) over a certain finite chain ring CR(uk,rb) of characteristic p, which is an extension of R. Then we use discrete Fourier transform (DFT) to construct an isomorphism γ between R[x]/<xN−λ> and a direct sum ⊕b∈IS(rb) of certain local rings, where I is the complete set of representatives of p-cyclotomic cosets modulo n1. By this isomorphism, all codes over R and their dual codes are obtained from the ideals of S(rb). In addition, we determine explicitly the inverse of γ so that the unique polynomial representations of λ-constacyclic codes may be calculated. Finally, for k=2 the exact number of such codes is provided.

An Improved Model for Analyzing Textual Sentiment Based on a Deep Neural Network Using Multi-Head Attention Mechanism

Due to the increasing growth of social media content on websites such as Twitter and Facebook, analyzing textual sentiment has become a challenging task. Therefore, many studies have focused on textual sentiment analysis. Recently, deep learning models, such as convolutional neural networks and long short-term memory, have achieved promising performance in sentiment analysis. These models have proven their ability to cope with the arbitrary length of sequences. However, when they are used in the feature extraction layer, the feature distance is highly dimensional, the text data are sparse, and they assign equal importance to various features. To address these issues, we propose a hybrid model that combines a deep neural network with a multi-head attention mechanism (DNN–MHAT). In the DNN–MHAT model, we first design an improved deep neural network to capture the text's actual context and extract the local features of position invariants by combining recurrent bidirectional long short-term memory units (Bi-LSTM) with a convolutional neural network (CNN). Second, we present a multi-head attention mechanism to capture the words in the text that are significantly related to long space and encoding dependencies, which adds a different focus to the information outputted from the hidden layers of BiLSTM. Finally, a global average pooling is applied for transforming the vector into a high-level sentiment representation to avoid model overfitting, and a sigmoid classifier is applied to carry out the sentiment polarity classification of texts. The DNN–MHAT model is tested on four reviews and two Twitter datasets. The results of the experiments illustrate the effectiveness of the DNN–MHAT model, which achieved excellent performance compared to the state-of-the-art baseline methods based on short tweets and long reviews.

Hash Algorithm In Verification Of Certificate Data Integrity And Security

The hash function is the most important cryptographic primitive function and is an integral part of the blockchain data structure. Hashes are often used in cryptographic protocols, information security applications such as Digital Signatures and message authentication codes (MACs). In the current development of certificate data security, there are 2 (two) types of hashes that are widely applied, namely, MD and SHA. However, when it comes to efficiency, in this study the hash type SHA-256 is used because it can be calculated faster with a better level of security. In the hypothesis, the Merkle-Damgård construction method is also proposed to support data integrity verification. Moreover, a cryptographic hash function is a one-way function that converts input data of arbitrary length and produces output of a fixed length so that it can be used to securely authenticate users without storing passwords locally. Since basically, cryptographic hash functions have many different uses in various situations, this research resulted in the use of hash algorithms in verifying the integrity and authenticity of certificate information.

Synthesis of Scalable Single Length Cycle, Single Attractor Cellular Automata in Linear Time

This paper proposes the synthesis of single length cycle, single attractor cellular automata (SACAs) for arbitrary length. The n-cell single length cycle, single attractor cellular automaton (SACA), synthesized in linear time O(n), generates a pattern and finally settles to a point state called the single length cycle attractor state. An analytical framework is developed around the graph-based tool referred to as the next state transition diagram to explore the properties of SACA rules for three-neighborhood, one-dimensional cellular automata. This enables synthesis of an (n+1)-cell SACA from the available configuration of an n-cell SACA in constant time and an (n+m)-cell SACA from the available configuration of n-cell and m-cell SACAs also in constant time.