Search results for: Encoding

Authors: Om Viroje

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Quantum machine learning represents a frontier in computational technology, promising significant advancements in data processing capabilities. This study explores the significance of data encoding techniques, specifically amplitude and phase encoding, in this emerging field. By employing a comparative analysis methodology, the research evaluates how these encoding techniques affect the accuracy, efficiency, and noise resilience of quantum algorithms. Our findings reveal that amplitude encoding enhances algorithmic accuracy and noise tolerance, whereas phase encoding significantly boosts computational efficiency. These insights are crucial for developing robust quantum frameworks that can be effectively applied in real-world scenarios. In conclusion, optimizing encoding strategies is essential for advancing quantum machine learning, potentially transforming various industries through improved data processing and analysis.

Keywords: quantum machine learning, data encoding, amplitude encoding, phase encoding, noise resilience

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Authors: Farzaneh Badinlou, Reza Kormi-Nouri, Monika Knopf

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Studies of adults' episodic memory have found that enacted encoding not only improve recall performance but also retrieve faster during the recall period. The current study focused on exploring the temporal progression of different encoding conditions in younger and older school children. 204 students from two age group of 8 and 14 participated in this study. During the study phase, we studied action encoding in two forms; participants performed the phrases by themselves (SPT), and observed the performance of the experimenter (EPT), which were compared with verbal encoding; participants listened to verbal action phrases (VT). At test phase, we used immediate and delayed free recall tests. We observed significant differences in memory performance as function of age group, and encoding conditions in both immediate and delayed free recall tests. Moreover, temporal progression of recall was faster in older children when compared with younger ones. The interaction of age-group and encoding condition was only significant in delayed recall displaying that younger children performed better in EPT whereas older children outperformed in SPT. It was proposed that enactment effect in form of SPT enhances item-specific processing, whereas EPT improves relational information processing and this differential processes are responsible for the results achieved in younger and older children. The role of memory strategies and information processing methods in younger and older children were considered in this study. Moreover, the temporal progression of recall was faster in action encoding in the form of SPT and EPT compared with verbal encoding in both immediate and delayed free recall and size of enactment effect was constantly increased throughout the recall period. The results of the present study provide further evidence that the action memory is explained with an emphasis on the notion of information processing and strategic views. These results also reveal the temporal progression of recall as a new dimension of episodic memory in children.

Keywords: action memory, enactment effect, episodic memory, school-aged children, temporal progression

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Authors: Tzu-I Chiang

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A central question for finite-nonfinite distinction in Mandarin Chinese is how does Mandarin encode temporal information without the grammatical contrast between past and present tense. Moreover, how do L2 learners of Mandarin whose native language is English and whose L1 system has tense morphology, acquire the temporal encoding system in L2 Mandarin? The current study reports preliminary findings on the relationship between topic prominence and the temporal encoding in L1 and L2 Chinese. Oral narratives data from 30 natives and learners of Mandarin Chinese were collected via a film-retell task. In terms of coding, predicates collected from the narratives were transcribed and then coded based on four major verb types: n-degree Statives (quality-STA), point-scale Statives (status-STA), n-atom EVENT (ACT), and point EVENT (resultative-ACT). How native speakers and non-native speakers started retelling the story was calculated. Results of the study show that native speakers of Chinese tend to express Topic Time (TT) syntactically at the topic position; whereas L2 learners of Chinese across levels rely mainly on the default time encoded in the event types. Moreover, as the proficiency level of the learner increases, learners’ appropriate use of the event predicates increased, which supports the argument that L2 development of temporal encoding is affected by lexical aspect.

Keywords: topic prominence, temporal encoding, lexical aspect, L2 acquisition

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Authors: Gunasekaran Raja, Ramkumar Jayaraman, Rajakumar Arul, Kottilingam Kottursamy

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Broadband Wireless Network (BWN) is the promising technology nowadays due to the increased number of smartphones. Buffering scheme using network coding considers the reliability and proper degree distribution in Worldwide interoperability for Microwave Access (WiMAX) multi-hop network. Using network coding, a secure way of transmission is performed which helps in improving throughput and reduces the packet loss in the multicast network. At the outset, improved network coding is proposed in multicast wireless mesh network. Considering the problem of performance overhead, degree distribution makes a decision while performing buffer in the encoding / decoding process. Consequently, BuS (Buffer Scheme) based on network coding is proposed in the multi-hop network. Here the encoding process introduces buffer for temporary storage to transmit packets with proper degree distribution. The simulation results depend on the number of packets received in the encoding/decoding with proper degree distribution using buffering scheme.

Keywords: encoding and decoding, buffer, network coding, degree distribution, broadband wireless networks, multicast

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Authors: Raghava Dhanya, Shashank S.

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This paper delves into the intersection of chess and computer science, specifically focusing on the efficient representation of chess game states. We propose two methods: the Static Method and the Dynamic Method, each offering unique advantages in terms of space efficiency and computational complexity. The Static Method aims to represent the game state using a fixedlength encoding, allocating 192 bits to capture the positions of all pieces on the board. This method introduces a protocol for ordering and encoding piece positions, ensuring efficient storage and retrieval. However, it faces challenges in representing pieces no longer in play. In contrast, the Dynamic Method adapts to the evolving game state by dynamically adjusting the encoding length based on the number of pieces in play. By incorporating Alive Bits for each piece kind, this method achieves greater flexibility and space efficiency. Additionally, it includes provisions for encoding additional game state information such as castling rights and en passant squares. Our findings demonstrate that the Dynamic Method offers superior space efficiency compared to traditional Forsyth-Edwards Notation (FEN), particularly as the game progresses and pieces are captured. However, it comes with increased complexity in encoding and decoding processes. In conclusion, this study provides insights into optimizing the representation of chess game states, offering potential applications in chess engines, game databases, and artificial intelligence research. The proposed methods offer a balance between space efficiency and computational overhead, paving the way for further advancements in the field.

Keywords: chess, optimisation, encoding, bit manipulation

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Authors: T. Toathom, M. Munlin, P. Sugunnasil

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The traveling salesman problem (TSP) is one of the best-known problems in optimization problem. There are many research regarding the TSP. One of the most usage tool for this problem is the genetic algorithm (GA). The chromosome of the GA for TSP is normally encoded by the order of the visited city. However, the traditional chromosome encoding scheme has some limitations which are twofold: the large solution space and the inability to encapsulate some information. The number of solution for a certain problem is exponentially grow by the number of city. Moreover, the traditional chromosome encoding scheme fails to recognize the misplaced correct relation. It implies that the tradition method focuses only on exact solution. In this work, we relax some of the concept in the GA for TSP which is the exactness of the solution. The proposed work exploits the relation between cities in order to reduce the solution space in the chromosome encoding. In this paper, a second order GA is proposed to solve the TSP. The term second order refers to how the solution is encoded into chromosome. The chromosome is divided into 2 types: the high order chromosome and the low order chromosome. The high order chromosome is the chromosome that focus on the relation between cities such as the city A should be visited before city B. On the other hand, the low order chromosome is a type of chromosome that is derived from a high order chromosome. In other word, low order chromosome is encoded by the traditional chromosome encoding scheme. The genetic operation, mutation and crossover, will be performed on the high order chromosome. Then, the high order chromosome will be mapped to a group of low order chromosomes whose characteristics are satisfied with the high order chromosome. From the mapped set of chromosomes, the champion chromosome will be selected based on the fitness value which will be later used as a representative for the high order chromosome. The experiment is performed on the city data from TSPLIB.

Keywords: genetic algorithm, traveling salesman problem, initial population, chromosomes encoding

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Authors: Yair Bleiberg, Michael Werman

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Coordinate-based Multi-Layer Perceptrons (MLPs) are known to have difficulty reconstructing high frequencies of the training data. A common solution to this problem is Positional Encoding (PE), which has become quite popular. However, PE has drawbacks. It has high-frequency artifacts and adds another hyper hyperparameter, just like batch normalization and dropout do. We believe that under certain circumstances, PE is not necessary, and a smarter construction of the network architecture together with a smart training method is sufficient to achieve similar results. In this paper, we show that very simple MLPs can quite easily output a frequency when given input of the half-frequency and quarter-frequency. Using this, we design a network architecture in blocks, where the input to each block is the output of the two previous blocks along with the original input. We call this a Fibonacci Network. By training each block on the corresponding frequencies of the signal, we show that Fibonacci Networks can reconstruct arbitrarily high frequencies.

Keywords: neural networks, positional encoding, high frequency intepolation, fully connected

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Authors: Won-Ho Kim

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In this paper, a design of H.263 based wireless video transceiver is presented for wireless camera system. It uses standard WIFI transceiver and the covering area is up to 100m. Furthermore the standard H.263 video encoding technique is used for video compression since wireless video transmitter is unable to transmit high capacity raw data in real time and the implemented system is capable of streaming at speed of less than 1Mbps using NTSC 720x480 video.

Keywords: wireless video transceiver, video surveillance camera, H.263 video encoding digital signal processing

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Authors: Asmaa Shehata

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Prior research indicates that second language (L2) learners encounter difficulty in the distinguishing novel L2 contrasting sounds that are not contrastive in their native languages. L2 orthographic information, however, is found to play a positive role in the acquisition of non-native phoneme contrasts. While most studies have mainly involved a familiar written script (i.e., the Roman script), the influence of a foreign, unfamiliar script is still unknown. Therefore, the present study asks: Does unfamiliar L2 script play a role in creating distinct phonological representations of novel contrasting phonemes? It is predicted that subjects’ performance in the unfamiliar orthography group will outperform their counterparts’ performance in the control group. Thus, training that entails orthographic inputs can yield a significant improvement in L2 adult learners’ identification and lexical encoding of novel L2 consonant contrasts. Results are discussed in terms of their implications for the type of input introduced to L2 learners to improve their language learning.

Keywords: Arabic, consonant contrasts, foreign script, lexical encoding, orthography, word learning

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Authors: Ilya B. Tsyrlov, Dmitry Y. Oshchepkov

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A computational search for dioxin response elements (DREs) in genes of proteins comprising the Ah receptor (AhR) cytosolic core complex was performed by highly efficient tool SITECON. Eventually, the following number of new DREs in 5’flanking region was detected by SITECON: one in AHR gene, five in XAP2, eight in HSP90AA1, and three in HSP90AB1 genes. Numerous DREs found in genes of AhR and AhR cytosolic complex members would shed a light on potential mechanisms of expression, the stoichiometry of unliganded AhR core complex, and its degradation vs biosynthesis dynamics resulted from treatment of target cells with the AhR most potent ligand, 2,3,7,8-TCDD. With human viruses, reduced susceptibility to TCDD of geneencoding HIV-1 P247 was justified by the only potential DRE determined in gag gene encoding HIV-1 P24 protein, whereas the regulatory region of CMV genes encoding IE gp/UL37 has five potent DRE, 1.65 kb/UL36 – six DRE, pp65 and pp71 – each has seven DRE, and pp150 – ten DRE. Also, from six to eight DRE were determined with SITECON in the regulatory region of HSV-1 IE genes encoding tegument proteins, UL36 and UL37, and of UL19 gene encoding bindingglycoprotein C (gC). So, TCDD in the low picomolar range may activate in human cells AhR: Arnt transcription pathway that triggers CMV and HSV-1 reactivation by binding to numerous promoter DRE within immediate-early (IE) genes UL37 and UL36, thus committing virus to the lytic cycle.

Keywords: dioxin response elements, Ah receptor, AhR: Arnt transcription pathway, human and viral genes

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Md. Enamul Haque, Abdullah Al Kaisan, Mahmudur R. Saniat, Aminur Rahman

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In this paper, we have implemented both sequential and parallel version of fractal image compression algorithms using CUDA (Compute Unified Device Architecture) programming model for parallelizing the program in Graphics Processing Unit for medical images, as they are highly similar within the image itself. There is several improvements in the implementation of the algorithm as well. Fractal image compression is based on the self similarity of an image, meaning an image having similarity in majority of the regions. We take this opportunity to implement the compression algorithm and monitor the effect of it using both parallel and sequential implementation. Fractal compression has the property of high compression rate and the dimensionless scheme. Compression scheme for fractal image is of two kinds, one is encoding and another is decoding. Encoding is very much computational expensive. On the other hand decoding is less computational. The application of fractal compression to medical images would allow obtaining much higher compression ratios. While the fractal magnification an inseparable feature of the fractal compression would be very useful in presenting the reconstructed image in a highly readable form. However, like all irreversible methods, the fractal compression is connected with the problem of information loss, which is especially troublesome in the medical imaging. A very time consuming encoding process, which can last even several hours, is another bothersome drawback of the fractal compression.

Keywords: accelerated GPU, CUDA, parallel computing, fractal image compression

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Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

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Authors: Manana Rusieshvili, Rusudan Dolidze

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This paper aims at 1) investigating the ways in which a traditional, monomodal written verbal metaphor can be transposed as a monomodal non-verbal (visual) or multimodal (aural and -visual) filmic metaphor ; 2) exploring similarities and differences in the process of encoding and decoding of monomodal and multimodal metaphors. The empiric data, on which the research is based, embrace three sources: the novel by Harry Gray ‘The Hoods’, the script of the film ‘Once Upon a Time in America’ (English version by David Mills) and the resultant film by Sergio Leone. In order to achieve the above mentioned goals, the research focuses on the following issues: 1) identification of verbal and non-verbal monomodal and multimodal metaphors in the above-mentioned sources and 2) investigation of the ways and modes the specific written monomodal metaphors appearing in the novel and the script are enacted in the film and become visual, aural or visual-aural filmic metaphors ; 3) study of the factors which play an important role in contributing to the encoding and decoding of the filmic metaphor. The collection and analysis of the data were carried out in two stages: firstly, the relevant data, i.e. the monomodal metaphors from the novel, the script and the film were identified and collected. In the second, final stage the metaphors taken from all of the three sources were analysed, compared and two types of phenomena were selected for discussion: (1) the monomodal written metaphors found in the novel and/or in the script which become monomodal visual/aural metaphors in the film; (2) the monomodal written metaphors found in the novel and/or in the script which become multimodal, filmic (visual-aural) metaphors in the film.

Keywords: encoding, decoding, filmic metaphor, multimodality

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Authors: Ming-Yang Guo, Cheng-Xian Wu, Wei-Xiang Chen, Chun-Yuan Lin, Yen-Jen Lin, Ann-Shyn Chiang

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With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Driver/Neuron images. In order to solve this problem, the intersection/union region for a set of images should be computed at first, then a comparison work is used to calculate the similarities between the region and other images. In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each image as a one-dimensional structure. Then, the intersection/union region from these images can be computed by using the compare operations, Boolean operators and lookup table method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by the Boolean scheme is the best than that by others; in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 12 based on 16 CPUs. This work was supported by the Ministry of Science and Technology under the grant MOST 106-2221-E-182-070.

Keywords: Drosophila driver image, Drosophila neuron images, intersection/union computation, parallel processing, OpenMP

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Authors: Linyu Wang, Mingjun Zhu, Jianhong Xiang, Hanyu Jiang

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Aiming at the problem that the spectral efficiency of hybrid precoding (HP) is too low in the current millimeter wave (mmWave) massive multiple input multiple output (MIMO) system, this paper proposes a digital joint equivalent channel hybrid precoding algorithm, which is based on the introduction of digital encoding matrix iteration. First, the objective function is expanded to obtain the relation equation, and the pseudo-inverse iterative function of the analog encoder is derived by using the pseudo-inverse method, which solves the problem of greatly increasing the amount of computation caused by the lack of rank of the digital encoding matrix and reduces the overall complexity of hybrid precoding. Secondly, the analog coding matrix and the millimeter-wave sparse channel matrix are combined into an equivalent channel, and then the equivalent channel is subjected to Singular Value Decomposition (SVD) to obtain a digital coding matrix, and then the derived pseudo-inverse iterative function is used to iteratively regenerate the simulated encoding matrix. The simulation results show that the proposed algorithm improves the system spectral efficiency by 10~20%compared with other algorithms and the stability is also improved.

Keywords: mmWave, massive MIMO, hybrid precoding, singular value decompositing, equivalent channel

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Authors: Narin Salehiyan, Ramin Ghasemi Shayan

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In order to investigate coronavirus RNA replication, transcription, recombination, protein processing and transport, virion assembly, the identification of coronavirus-specific cell receptors, and polymerase processing, the manipulation of coronavirus clones and complementary DNAs (cDNAs) of defective-interfering (DI) RNAs is the subject of this chapter. The idea of the Covid genome is nonsegmented, single-abandoned, and positive-sense RNA. When compared to other RNA viruses, its size is significantly greater, ranging from 27 to 32 kb. The quality encoding the enormous surface glycoprotein depends on 4.4 kb, encoding a forcing trimeric, profoundly glycosylated protein. This takes off exactly 20 nm over the virion envelope, giving the infection the appearance-with a little creative mind of a crown or coronet. Covid research has added to the comprehension of numerous parts of atomic science as a general rule, like the component of RNA union, translational control, and protein transport and handling. It stays a fortune equipped for creating startling experiences.

Keywords: covid-19, corona, virus, genome, genetic

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Authors: I. Nava-Arenas, N. Ruiz-Ordaz, C. J. Galindez-Mayer, M. L. Luna-Guido, S. L. Ruiz-López, A. Cabrera-Orozco, D. Nava-Arenas

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Among the most important herbicides, the organochlorine compounds are of considerable interest due to their recalcitrance to the chemical, biological, and photolytic degradation, their persistence in the environment, their mobility, and their bioacummulation. The most widely used herbicides in North America are primarily 2,4-dichlorophenoxyacetic acid (2,4-D), the triazines (atrazine and simazine), and to a lesser extent diuron. The contamination of soils and water bodies frequently occurs by mixtures of these xenobiotics. For this reason, in this work, the operational stability to changes in the composition of the medium supplied to an aerobic biofilm reactor was studied. The reactor was packed with fragments of volcanic rock that retained a complex microbial film, able to degrade a mixture of organochlorine herbicides atrazine, simazine, diuron and 2,4-D, and whose members have microbial genes encoding the main catabolic enzymes atzABCD, tfdACD and puhB. To acclimate the attached microbial community, the biofilm reactor was fed continuously with a mineral minimal medium containing the herbicides (in mg•L-1): diuron, 20.4; atrazine, 14.2, simazine, 11.4, and 2,4-D, 59.7, as carbon and nitrogen sources. Throughout the bioprocess, removal efficiencies of 92-100% for herbicides, 78-90% for COD, 92-96% for TOC and 61-83% for dehalogenation were reached. In the microbial community, the genes encoding catabolic enzymes of different herbicides tfdACD, puhB and, occasionally, the genes atzA and atzC were detected. After the acclimatization, the triazine herbicides were eliminated from the mixture formulation. Volumetric loading rates of the mixture 2,4-D and diuron were continuously supplied to the reactor (1.9-21.5 mg herbicides •L-1 •h-1). Along the bioprocess, the removal efficiencies obtained were 86-100% for the mixture of herbicides, 63-94% for for COD, 90-100% for COT, and dehalogenation values of 63-100%. It was also observed that the genes encoding the enzymes in the catabolism of both herbicides, tfdACD and puhB, were consistently detected; and, occasionally, the atzA and atzC. Subsequently, the triazine herbicide atrazine and simazine were restored to the medium supply. Different volumetric charges of this mixture were continuously fed to the reactor (2.9 to 12.6 mg herbicides •L-1 •h-1). During this new treatment process, removal efficiencies of 65-95% for the mixture of herbicides, 63-92% for COD, 66-89% for TOC and 73-94% of dehalogenation were observed. In this last case, the genes tfdACD, puhB and atzABC encoding for the enzymes involved in the catabolism of the distinct herbicides were consistently detected. The atzD gene, encoding the cyanuric hydrolase enzyme, could not be detected, though it was determined that there was partial degradation of cyanuric acid. In general, the community in the biofilm reactor showed some catabolic stability, adapting to changes in loading rates and composition of the mixture of herbicides, and preserving their ability to degrade the four herbicides tested; although, there was a significant delay in the response time to recover to degradation of the herbicides.

Keywords: biodegradation, biofilm reactor, microbial community, organochlorine herbicides

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Authors: H. Sakamoto, S. Kurosawa, M. Suzuki, S. Oguri

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In Arabidopsis, several genes encoding proteins with ankyrin repeats and trans-membrane domains (AtANKTM) have been identified as mediators of biotic and abiotic stress responses. It has been known that the expression of an AtANKTM gene, At2g24600, is induced in response to abiotic stress and that there are four splicing variants derived from this locus. In this study, by RT-PCR and sequencing analysis, an unknown splicing variant of the At2g24600 transcript was identified. Based on differences in the predicted amino acid sequences, the five splicing variants are divided into three groups. The three predicted proteins are highly homologous, yet have different numbers of ankyrin repeats and trans-membrane domains. It is generally considered that ankyrin repeats mediate protein-protein interaction and that the number of trans-membrane domains affects membrane topology of proteins. The protein variants derived from the At2g24600 locus may have different molecular functions each other.

Keywords: alternative splicing, ankyrin repeats, trans-membrane domains, arabidopsis

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Authors: Mohammed M. Siddeq, Mohammed H. Rasheed, Omar M. Salih, Marcos A. Rodrigues

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This research proposes a data encoding and decoding method based on the Matrix Minimization algorithm. This algorithm is applied to high-frequency coefficients for compression/encoding. The algorithm starts by converting every three coefficients to a single value; this is accomplished based on three different keys. The decoding/decompression uses a search method called QSS (Quick Sequential Search) Decoding Algorithm presented in this research based on the sequential search to recover the exact coefficients. In the next step, the decoded data are saved in an auxiliary array. The basic idea behind the auxiliary array is to save all possible decoded coefficients; this is because another algorithm, such as conventional sequential search, could retrieve encoded/compressed data independently from the proposed algorithm. The experimental results showed that our proposed decoding algorithm retrieves original data faster than conventional sequential search algorithms.

Keywords: matrix minimization algorithm, decoding sequential search algorithm, image compression, DCT, DWT

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Authors: Alfonsina Buoniconto

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The aim of this study is to investigate the degree of cross-linguistic and intra-linguistic variation in the encoding of motion events (MEs) in English and Italian, these being typologically different languages both showing signs of disobedience to their respective types. As a matter of fact, the traditional typological classification of MEs encoding distributes languages into two macro-types, based on the preferred locus for the expression of Path, the main ME component (other components being Figure, Ground and Manner) characterized by conceptual and structural prominence. According to this model, Satellite-framed (SF) languages typically express Path information in verb-dependent items called satellites (e.g. preverbs and verb particles) with main verbs encoding Manner of motion; whereas Verb-framed languages (VF) tend to include Path information within the verbal locus, leaving Manner to adjuncts. Although this dichotomy is valid altogether, languages do not always behave according to their typical classification patterns. English, for example, is usually ascribed to the SF type due to the rich inventory of postverbal particles and phrasal verbs used to express spatial relations (i.e. the cat climbed down the tree); nevertheless, it is not uncommon to find constructions such as the fog descended slowly, which is typical of the VF type. Conversely, Italian is usually described as being VF (cf. Paolo uscì di corsa ‘Paolo went out running’), yet SF constructions like corse via in lacrime ‘She ran away in tears’ are also frequent. This paper will try to demonstrate that such a typological overlapping is due to the fact that the semantic units making up MEs are distributed within several loci of the sentence –not only verbs and satellites– thus determining a number of different constructions stemming from convergent factors. Indeed, the linguistic expression of motion events depends not only on the typological nature of languages in a traditional sense, but also on a series morphological, lexical, and syntactic resources, as well as on inferential, discursive, usage-related, and cultural factors that make semantic information more or less accessible, frequent, and easy to process. Hence, rather than describe English and Italian in dichotomic terms, this study focuses on the investigation of cross-linguistic and intra-linguistic variation in the use of all the strategies made available by each linguistic system to express motion. Evidence for these assumptions is provided by parallel corpora analysis. The sample texts are taken from two contemporary Italian novels and their respective English translations. The 400 motion occurrences selected (200 in English and 200 in Italian) were scanned according to the MODEG (an acronym for Motion Decoding Grid) methodology, which grants data comparability through the indexation and retrieval of combined morphosyntactic and semantic information at different levels of detail.

Keywords: construction typology, motion event encoding, parallel corpora, satellite-framed vs. verb-framed type

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

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Authors: Andi Aliah Hidayani, Asmi Citra Malina A. R. Tassakka, Andi Parenrengi

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Vanname Shrimp is one of the high yielding varieties that are more resistant to virus attacks. However, now this shrimp more death due to virus attack such as white spot disease caused by white spot syndrome virus (WSSV). Various efforts have done to prevent the disease, like immunostimulatory, probiotics, and vaccine. White spot syndrome virus (WSSV) envelope protein VP19 gene is important because of its involvement in the system infection of shrimp. This study aimed to isolate and characterize an envelope protein VP19 – encoding gene of WSSV using WSSV infected Vanname Shrimp sample from some areas in South Sulawesi (Pangkep, Barru and Pinrang). The genomic of DNA were isolated from shrimp muscle using DTAB-CTAB method. Isolation of gene encoding envelope protein VP19 WSSV ws successfully performed with the results of the length of DNA fragment was 387 bp. The results of homology analysis using BLASTn homology suggested that these isolates genes from Barru, Pangkep and Pinrang have closest relationship with isolates from Mexican.

Keywords: vanname, shrimp, WSSV, viral protein 19

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Authors: George Zhou, Yunchan Chen, Candace Chien

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Kidney replacement therapy is the current standard of care for end-stage renal diseases. In-center or home hemodialysis remains an integral component of the therapeutic regimen. Arteriovenous fistulas (AVF) make up the vascular circuit through which blood is filtered and returned. Naturally, AVF patency determines whether adequate clearance and filtration can be achieved and directly influences clinical outcomes. Our aim was to build a deep learning model for automated AVF stenosis screening based on the sound of blood flow through the AVF. A total of 311 patients with AVF were enrolled in this study. Blood flow sounds were collected using a digital stethoscope. For each patient, blood flow sounds were collected at 6 different locations along the patient’s AVF. The 6 locations are artery, anastomosis, distal vein, middle vein, proximal vein, and venous arch. A total of 1866 sounds were collected. The blood flow sounds are labeled as “patent” (normal) or “stenotic” (abnormal). The labels are validated from concurrent ultrasound. Our dataset included 1527 “patent” and 339 “stenotic” sounds. We show that blood flow sounds vary significantly along the AVF. For example, the blood flow sound is loudest at the anastomosis site and softest at the cephalic arch. Contextualizing the sound with location metadata significantly improves classification performance. How to encode and incorporate categorical metadata is an active area of research1. Herein, we study ordinal (i.e., integer) encoding schemes. The numerical representation is concatenated to the flattened feature vector. We train a vision transformer (ViT) on spectrogram image representations of the sound and demonstrate that using scalar multiples of our integer encodings improves classification performance. Models are evaluated using a 10-fold cross-validation procedure. The baseline performance of our ViT without any location metadata achieves an AuROC and AuPRC of 0.68 ± 0.05 and 0.28 ± 0.09, respectively. Using the following encodings of Artery:0; Arch: 1; Proximal: 2; Middle: 3; Distal 4: Anastomosis: 5, the ViT achieves an AuROC and AuPRC of 0.69 ± 0.06 and 0.30 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 10; Proximal: 20; Middle: 30; Distal 40: Anastomosis: 50, the ViT achieves an AuROC and AuPRC of 0.74 ± 0.06 and 0.38 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 100; Proximal: 200; Middle: 300; Distal 400: Anastomosis: 500, the ViT achieves an AuROC and AuPRC of 0.78 ± 0.06 and 0.43 ± 0.11. respectively. Interestingly, we see that using increasing scalar multiples of our integer encoding scheme (i.e., encoding “venous arch” as 1,10,100) results in progressively improved performance. In theory, the integer values do not matter since we are optimizing the same loss function; the model can learn to increase or decrease the weights associated with location encodings and converge on the same solution. However, in the setting of limited data and computation resources, increasing the importance at initialization either leads to faster convergence or helps the model escape a local minimum.

Keywords: arteriovenous fistula, blood flow sounds, metadata encoding, deep learning

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Authors: Diogen Babuc

Abstract:

The question that is a motivation of writing is how many devote themselves to discovering something in the world of science where much is discerned and revealed, but at the same time, much is unknown. Methods: The insightful elements of this algorithm are the ciphering and deciphering algorithms of Playfair, Caesar, and Vigenère. Only a few of their main properties are taken and modified, with the aim of forming a specific functionality of the algorithm called Latin Djokovic. Specifically, a string is entered as input data. A key k is given, with a random value between the values a and b = a+3. The obtained value is stored in a variable with the aim of being constant during the run of the algorithm. In correlation to the given key, the string is divided into several groups of substrings, and each substring has a length of k characters. The next step involves encoding each substring from the list of existing substrings. Encoding is performed using the basis of Caesar algorithm, i.e., shifting with k characters. However, that k is incremented by 1 when moving to the next substring in that list. When the value of k becomes greater than b+1, it’ll return to its initial value. The algorithm is executed, following the same procedure, until the last substring in the list is traversed. Results: Using this polyalphabetic method, ciphering and deciphering of strings are achieved. The algorithm also works for a 100-character string. The x character isn’t used when the number of characters in a substring is incompatible with the expected length. The algorithm is simple to implement, but it’s questionable if it works better than the other methods from the point of view of execution time and storage space.

Keywords: ciphering, deciphering, authentic, algorithm, polyalphabetic cipher, random key, methods comparison

Procedia PDF Downloads 103

Authors: Lokes Parvatha Kumaran S., Sakthi Jay Mahenthar C., Sathyaprakash P., Jayakumar V., Shobanadevi A.

Abstract:

With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network.

Keywords: quantum computing, quantum machine learning, variational quantum circuit, deep reinforcement learning, quantum information encoding scheme

Procedia PDF Downloads 134

Authors: Bowen Ding, Yihao Kuang

Abstract:

In recent years, large-scale pre-trained language models have achieved state-of-the-art performance on a variety of natural language processing tasks. The word vectors produced by these language models can be viewed as dense encoded presentations of natural language that in text form. However, it is unknown how much linguistic information is encoded and how. In this paper, we construct several corresponding probing tasks for multiple linguistic information to clarify the encoding capabilities of different language models and performed a visual display. We firstly obtain word presentations in vector form from different language models, including BERT, ELMo, RoBERTa and GPT. Classifiers with a small scale of parameters and unsupervised tasks are then applied on these word vectors to discriminate their capability to encode corresponding linguistic information. The constructed probe tasks contain both semantic and syntactic aspects. The semantic aspect includes the ability of the model to understand semantic entities such as numbers, time, and characters, and the grammatical aspect includes the ability of the language model to understand grammatical structures such as dependency relationships and reference relationships. We also compare encoding capabilities of different layers in the same language model to infer how linguistic information is encoded in the model.

Keywords: language models, probing task, text presentation, linguistic information

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Authors: A. Chillali, M. Sahmoudi

Abstract:

In this paper we will give a method for encoding the elements of the ring of integers of sextic extension, namely L = Q(a,b) which is a rational quadratic over cubic field K =Q(a) where a^{2} is a rational square free integer and b is a root of irreducible polynomiale of degree 3.

Keywords: coding, integral bases, sextic, quadratic

Procedia PDF Downloads 297

Authors: C. Babu Rao, Pandian Chelliah

Abstract:

Owing to their reliability, a number of fluorescent spectra based fiber optic sensors have been developed for detection and identification of hazardous chemicals such as explosives, narcotics etc. In High security regions, such as airports, it is important to monitor simultaneously multiple locations. This calls for deployment of a portable sensor at each location. However, the selectivity and sensitivity of these techniques depends on the spectral resolution of the spectral analyzer. The better the resolution the larger the repertoire of chemicals that can be detected. A portable unit will have limitations in meeting these requirements. Optical fibers can be employed for collecting and transmitting spectral signal from the portable sensor head to a sensitive central spectral analyzer (CSA). For multipoint sensing, optical multiplexing of multiple sensor heads with CSA has to be adopted. However with multiplexing, when one sensor head is connected to CSA, the rest may remain unconnected for the turn-around period. The larger the number of sensor heads the larger this turn-around time will be. To circumvent this imitation, we propose in this paper, an optical encoding methodology to use multiple portable sensor heads connected to a single CSA. Each portable sensor head is assigned an unique address. Spectra of every chemical detected through this sensor head, are encoded by its unique address and can be identified at the CSA end. The methodology proposed is demonstrated through a simulation using Matlab SIMULINK.

Keywords: optical encoding, fluorescence, multipoint sensing

Procedia PDF Downloads 710

Authors: David Percy

Abstract:

Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

Procedia PDF Downloads 66