Sequence Of Symbols Research Articles

Music reading experience modulates eye movement pattern in English reading but not in Chinese reading

Here we tested the hypothesis that in Chinese-English bilinguals, music reading experience may modulate eye movement planning in reading English but not Chinese sentences due to the similarity in perceptual demands on processing sequential symbol strings separated by spaces between music notation and English sentence reading. Chinese–English bilingual musicians and non-musicians read legal, semantically incorrect, and syntactically (and semantically) incorrect sentences in both English and Chinese. In English reading, musicians showed more dispersed eye movement patterns in reading syntactically incorrect sentences than legal sentences, whereas non-musicians did not. This effect was not observed in Chinese reading. Musicians also had shorter saccade lengths when viewing syntactically incorrect than correct musical notations and sentences in an unfamiliar alphabetic language (Tibetan), whereas non-musicians did not. Thus, musicians’ eye movement planning was disturbed by syntactic violations in both music and English reading but not in Chinese reading, and this effect was generalized to an unfamiliar alphabetic language. These results suggested that music reading experience may modulate perceptual processes in reading differentially in bilinguals’ two languages, depending on their processing similarities.

Emergence of functional information from multivariate correlations.

The information content of symbolic sequences (such as nucleic or amino acid sequences, but also neuronal firings or strings of letters) can be calculated from an ensemble of such sequences, but because information cannot be assigned to single sequences, we cannot correlate information to other observables attached to the sequence. Here we show that an information score obtained from multivariate (multiple-variable) correlations within sequences of a 'training' ensemble can be used to predict observables of out-of-sample sequences with an accuracy that scales with the complexity of correlations, showing that functional information emerges from a hierarchy of multi-variable correlations. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Music Beat Generation through AI

Music Plays a significant role in our mental health, but composing a song is difficult for composers. While listening to music, Humans can quickly detect and move to beats. Similarly to Language, Music can be represented as a sequence of discrete symbols that form a hierarchical syntax, with notes being roughly like characters and motifs of notes like a word. Several Theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat is a question of how we learn to generate and hold a beat. In this paper, we introduce a Music Beat Generator that will interpolate music rhythms and notes that can be used by music artists, Using Artificial intelligence and Machine Learning. In the context of this project, rhythms are one long measure sequence of notes and rest occurring on natural pulse subdivisions of a beat. Music generation using machine learning techniques has been a topic of interest for the past two decades. Music proves to be a different challenge compared to images, among three main dimensions: Firstly, music is temporal, with a hierarchical structure with dependencies across time. Secondly, music consists of interdependent instruments that unfold across time. Thirdly, music is grouped into chords, arpeggios, and melodies – hence each time -step may have multiple outputs. Keywords – Machine Learning; Artificial Intelligence; Music Beats; Algorithm; Chords; Interpolate; Rhythms.

Making Artificial Brains: Components, Topology, and Optimization.

Making Artificial Brains: Components, Topology, and Optimization.

Bärnighausen Trees – A group–subgroup reference database

Abstract Group–subgroup schemes are a useful tool in crystal chemistry for systemizing crystal structures and they are an indispensable help during X-ray crystallographic studies of complex, twinned and modulated structures. Meanwhile many superstructure variants are summarized within so-called Bärnighausen trees. The present database lists relevant literature with respect to the crystallographic/group-theoretical tools and original work and gives a tabulated overview on the crystallographic fingerprints (aristotype, space group symbol, Pearson code and Wyckoff sequence) of the respective superstructures.

Perceptual effects of interpolated Austrian and German standard varieties

This article focuses on the perception of standard varieties produced by Austrian and German TV newscasters from the perspective of listeners from both countries, Germany and Austria. Thus, the paper's sociolinguistic scope is located in the pluricentric realm. It assesses (the perception of) standard language variation on fine phonetic levels. In addition to naturally produced stimuli (read sentences), “intermediate” samples were generated by means of a two-step interpolation procedure: First, the symbolic phone sequences were aligned with a Levenshtein-distance algorithm. Second, a phone-level Dynamic-Time-Warping (DTW) algorithm was applied to align the two utterances on a frame level, taking phoneme boundaries into account. Additionally, the spectrum and the fundamental frequency (F0) contour of the utterances was manipulated to be either interpolated or fixed to a given speaker. This procedure allowed for assessing these features’ contribution to listeners’ judgments of whether a given utterance sounds as if spoken by a speaker from (rather) Germany or (rather) Austria. Results of the judgment task showed that the interpolated samples were perceived in a continuous fashion, similarly by both groups, and with overall greater reliance on spectral than F0 information. These findings suggest that even fine phonetic differences between Standard German from Germany and Austria are recognized and evaluated by listeners from both countries. An overall bias towards perceiving all speech samples as closer to the listeners’ own national standard points to a factor of familiarity with one's own and uncertainty towards the ‘foreign’ standard. The similar degree of this bias indicates that both groups have similar levels of familiarity with their own and uncertainty about the other standard variety.

Language As a Source of Novel Bioactive Peptides: Peptide SVENSKAAKADEMIEN

Most peptides have been discovered from natural sources or via combinatorial chemistry methods, but the International Union of Pure and Applied Chemistry’s one-letter symbolism for the names of amino acids facilitates the use of language as a novel source of potentially bioactive peptides. Any word, name, or phrase composed of 21 letters of the 26-letter English (or French, German, Spanish, or Swedish) alphabet can be viewed as not only a sequence of letters, but also as a sequence of unambiguous symbols for the names of amino acids (i.e., a peptide). Protein database searches reveal that is sometimes possible to find such name peptides within the larger sequences of known proteins, but most are novel. Peptides designed using this name-to-peptide method have been chemically synthesized, subjected to laboratory testing, and found to exhibit potentially useful biomedical properties. This article describes research in the new field of name peptides, including a theoretical analysis of a peptide based on the name of the Swedish Academy, the organization that selects recipients of the Nobel Prize in Literature.

Connecting weighted automata, tensor networks and recurrent neural networks through spectral learning

In this paper, we present connections between three models used in different research fields: weighted finite automata~(WFA) from formal languages and linguistics, recurrent neural networks used in machine learning, and tensor networks which encompasses a set of optimization techniques for high-order tensors used in quantum physics and numerical analysis. We first present an intrinsic relation between WFA and the tensor train decomposition, a particular form of tensor network. This relation allows us to exhibit a novel low rank structure of the Hankel matrix of a function computed by a WFA and to design an efficient spectral learning algorithm leveraging this structure to scale the algorithm up to very large Hankel matrices.We then unravel a fundamental connection between WFA and second-orderrecurrent neural networks~(2-RNN): in the case of sequences of discrete symbols, WFA and 2-RNN with linear activationfunctions are expressively equivalent. Leveraging this equivalence result combined with the classical spectral learning algorithm for weighted automata, we introduce the first provable learning algorithm for linear 2-RNN defined over sequences of continuous input vectors.This algorithm relies on estimating low rank sub-blocks of the Hankel tensor, from which the parameters of a linear 2-RNN can be provably recovered. The performances of the proposed learning algorithm are assessed in a simulation study on both synthetic and real-world data.

A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk

Predictive modeling of clinical data is fraught with challenges arising from the manner in which events are recorded. Patients typically fall ill at irregular intervals and experience dissimilar intervention trajectories. This results in irregularly sampled and uneven length data which poses a problem for standard multivariate tools. The alternative of feature extraction into equal-length vectors via methods like Bag-of-Words (BoW) potentially discards useful information. We propose an approach based on a kernel framework in which data is maintained in its native form: discrete sequences of symbols. Kernel functions derived from the edit distance between pairs of sequences may then be utilized in conjunction with support vector machines to classify the data. Our method is evaluated in the context of the prediction task of determining patients likely to develop type 2 diabetes following an earlier episode of elevated blood pressure of 130/80 mmHg. Kernels combined via multi kernel learning achieved an F1-score of 0.96, outperforming classification with SVM 0.63, logistic regression 0.63, Long Short Term Memory 0.61 and Multi-Layer Perceptron 0.54 applied to a BoW representation of the data. We achieved an F1-score of 0.97 on MKL on external dataset. The proposed approach is consequently able to overcome limitations associated with feature-based classification in the context of clinical data.

Investigation of bifurcations in cyclic combustion dynamics of a CNG-diesel RCCI engine

This study uses nonlinear dynamical and chaotic methods to investigate the cyclic combustion dynamics of a CNG-Diesel reactivity control compression ignition (RCCI) engine. The experiments of RCCI combustion mode are conducted on a single-cylinder automotive diesel engine with the development ECU. Low reactivity fuel (i.e., CNG) is injected into the intake manifold, and high reactivity fuel (i.e., diesel) is injected directly into the engine cylinder during the RCCI experiments. Mass of fuel injection per cycle and their injection events are controlled by using ECU. The engine is tested for fixed engine load and speed of 3 bar BMEP and 1500 rpm, respectively. A double diesel injection strategy is used for injecting the diesel fuel into the engine cylinder. In-cylinder pressure is measured using a piezoelectric pressure transducer on a crank angle basis with a resolution of 0.1 CAD. Combustion parameters are calculated on a cyclic basis from measured in-cylinder pressure. In this study, the effect of diesel injection timing (SOI-2) on the behavior of combustion dynamics for two different masses of port-injected CNG fuel (mc) is investigated. The CA50 time series of 1000 consecutive engine cycles is analyzed for combustion dynamics analysis using symbol sequence analysis, return maps, recurrence plots (RPs), recurrence quantitative analysis (RQA), and 0–1 test. The peaks in symbol sequence histograms and the corresponding symbol series depict the period-2 nature of the cyclic combustion dynamics at the intermediate SOI-2 of 30° and 40° bTDC. Return maps also confirm the onset of noisy period-2 bifurcation at 30° bTDC SOI-2. The dominance of deterministic period-2 characteristics is found in the cyclic combustion dynamics at 30° and 40° bTDC SOI-2 using recurrence plots. The 0–1 test method depicts stronger chaotic cyclic combustion dynamics at 20° and 50° bTDC SOI-2. In summary, the present study supports the existence of a periodic window in between the chaotic combustion for the intermediate values of SOI-2. The dominance of the deterministic characteristics makes this regime of intermediate SOI-2 values suitable for a better cycle resolved control.

TA4L: Efficient temporal abstraction of multivariate time series

In this work, we introduce TA4L, a new efficient algorithm to transform multivariate time series into Lexicographical Symbolic Time Interval Sequences (LSTISs), that is, sequences ready to feed time-interval related pattern (TIRP) mining algorithms. The ultimate goal is to make explicit the embedded, ad-hoc pre-processes related to TIRP mining algorithms while offering an efficient solution for the required pre-processing. On the one hand, TA4L divides the signals into segments based on time duration (instead of the often-used practice based on the number of samples), which allows the construction of consistent time intervals. Concatenation of intervals is controlled by a maximum time gap constraint that reinforces the generated time intervals’ consistency. Moreover, different ways to parallelise the algorithm are explored that are accompanied by efficient data structures to speed up the pre-processing cost. TA4L has been experimentally evaluated with synthetic and real datasets, and the results show that TA4L requires significantly less computation time than other state-of-the-art approaches, revealing that it is an effective algorithm.

Combining user behavioural information at the feature level to enhance continuous authentication systems

The scientific and business communities are proposing new authentication methods more robust than traditional solutions relying on a single security point such as passwords (i.e. “something you know”). User and Entity Behavior Analysis (UEBA) has postulated as an excellent solution to improve authentication systems by performing continuous authentication to extend the authentication process over time. UEBA is based on detecting anomalies in the intrinsic behaviour of each user or entity (i.e. it is based on “something you are/do”). This paper presents a method for performing continuous authentication using UEBA techniques that allows combining information from multiple sources at the feature level. This combination is achieved through a novel Symbolic Aggregate approximation (SAX) using Random Trees Embeddings for each information source, producing a sequence of symbols. Then, these sequences of symbols are combined into a single sequence using temporal information. The resulting sequence of symbols feeds a density-based clustering model that uses a distance based on DNA sequence alignment techniques to extract behavioural cores. Finally, new samples are compared against these cores to detect anomalies using a risk model that evaluates if a behaviour is anomalous (suspected user impersonation). The model has been extensively tested and evaluated against well-known state-of-the-art datasets.

The Role of Attention, Language Ability, and Language Experience in Children's Artificial Grammar Learning.

The current study examined the role of attention and language ability in nonverbal rule induction performance in a demographically diverse sample of school-age children. The participants included 43 English-speaking monolingual and 65 Spanish-English bilingual children between the ages of 5 and 9 years. Core Language Index standard scores from the Clinical Evaluation of Language Fundamentals-Fourth Edition indexed children's language skills. Rule induction was measured via a visual artificial grammar learning task. Two equally complex finite-state artificial grammars were used. Children learned one grammar in a low attention condition (where children were exposed to symbol sequences with no distractors) and another grammar in a high attention condition (where distractor symbols were presented around the perimeter of the target symbol sequences). Overall, performance in the high attention condition was significantly worse than performance in the low attention condition. Children with robust language skills performed significantly better in the high attention condition than children with weaker language skills. Despite group differences in socioeconomic status, English language skills, and nonverbal intelligence, monolingual and bilingual children performed similarly to each other in both conditions. The results suggest that the ability to extract rules from visual input is attenuated by the presence of competing visual information and that language ability, but not bilingualism, may influence rule induction.

Accurate Channel Estimation and Adaptive Underwater Acoustic Communications Based on Gaussian Likelihood and Constellation Aggregation

Achieving accurate channel estimation and adaptive communications with moving transceivers is challenging due to rapid changes in the underwater acoustic channels. We achieve an accurate channel estimation of fast time-varying underwater acoustic channels by using the superimposed training scheme with a powerful channel estimation algorithm and turbo equalization, where the training sequence and the symbol sequence are linearly superimposed. To realize this, we develop a ‘global’ channel estimation algorithm based on Gaussian likelihood, where the channel correlation between (among) the segments is fully exploited by using the product of the Gaussian probability-density functions of the segments, thereby realizing an ideal channel estimation of each segment. Moreover, the Gaussian-likelihood-based channel estimation is embedded in turbo equalization, where the information exchange between the equalizer and the decoder is carried out in an iterative manner to achieve an accurate channel estimation of each segment. In addition, an adaptive communication algorithm based on constellation aggregation is proposed to resist the severe fast time-varying multipath interference and environmental noise, where the encoding rate is automatically determined for reliable underwater acoustic communications according to the constellation aggregation degree of equalization results. Field experiments with moving transceivers (the communication distance was approximately 5.5 km) were carried out in the Yellow Sea in 2021, and the experimental results verify the effectiveness of the two proposed algorithms.

Anti-integrability for Three-Dimensional Quadratic Maps

We study the dynamics of the three-dimensional quadratic diffeomorphism using a concept first introduced 30 years ago for the Frenkel--Kontorova model of condensed matter physics: the anti-integrable (AI) limit. At the traditional AI limit, orbits of a map degenerate to sequences of symbols and the dynamics is reduced to the shift operator, a pure form of chaos. Under nondegeneracy conditions, a contraction mapping argument can show that infinitely many AI states continue to orbits of the deterministic map. For the 3D quadratic map, the AI limit that we study is a quadratic correspondence whose branches, a pair of one-dimensional maps, introduce symbolic dynamics on two symbols. The AI states, however, are nontrivial orbits of this correspondence. The character of these orbits depends on whether the quadratic takes the form of an ellipse, a hyperbola, or a pair of lines. Using contraction arguments, we find parameter domains for each case such that each symbol sequence corresponds to a unique AI state. In some parameter domains, sufficient conditions are then found for each such AI state to continue away from the limit becoming an orbit of the original 3D map. Numerical continuation methods extend these results, allowing computation of bifurcations to obtain orbits with horseshoe-like structures and intriguing self-similarity. We conjecture that pairs of periodic orbits in saddle-node and period-doubling bifurcations have symbol sequences that differ in exactly one position.

A Motion Capture Data-Driven Automatic Labanotation Generation Model Using the Convolutional Neural Network Algorithm

All human movements can be effectively represented with labanotation, which is simple to read and preserve. However, manually recording the labanotation takes a long time, so figuring out how to use the labanotation to accurately and quickly record and preserve traditional dance movements is a key research question. An automatic labanotation generation algorithm based on DL (deep learning) is proposed in this study. The BVH file is first analyzed, and the data are then converted. On this foundation, a CNN (convolutional neural network) algorithm for generating the dance spectrum of human lower-limb movements is proposed, which is very good at learning action space information. The algorithm performs admirably in terms of classification and recognition. Finally, a spatial segmentation-based automatic labanotation generation algorithm is proposed. To begin, every frame of data is converted into a symbol sequence using spatial law, resulting in a very dense motion sequence. The motion sequence is then regulated according to the minimum beat of motion obtained through wavelet analysis. To arrive at the final result, the classifier is used to determine whether each symbol is reserved or not. As a result, we will be able to create more accurate dance music for simple human movements.

Primitive Action Based Combined Task and Motion Planning for the Service Robot.

The need for combined task and motion planning (CTAMP) in robotics is well known as robotic technologies become more mature. The goal of CTAMP is to determine a proper sequence of a robot’s actions based on symbolic and geometric reasoning. Because of the fundamental difference in symbolic and geometric reasoning, a CTAMP system often requires an interface module between the two reasoning modules. We propose a CTAMP system in which a symbolic action sequence is generated in task planning, and each action is verified geometrically in motion planning using the off-the-shelf planners and reasoners. The approach is that a set of action models is defined with PDDL in the interface module (action library) and the required information to each planner is automatically provided by the interface module. The proposed method was successfully implemented in three simulated experiments that involve manipulation tasks. According to our findings, the proposed method is effective in responding to changes in the environment and uncertainty with errors in recognition of the environment and the robot motion control.

An Information Theoretic Approach to Symbolic Learning in Synthetic Languages.

An important aspect of using entropy-based models and proposed “synthetic languages”, is the seemingly simple task of knowing how to identify the probabilistic symbols. If the system has discrete features, then this task may be trivial; however, for observed analog behaviors described by continuous values, this raises the question of how we should determine such symbols. This task of symbolization extends the concept of scalar and vector quantization to consider explicit linguistic properties. Unlike previous quantization algorithms where the aim is primarily data compression and fidelity, the goal in this case is to produce a symbolic output sequence which incorporates some linguistic properties and hence is useful in forming language-based models. Hence, in this paper, we present methods for symbolization which take into account such properties in the form of probabilistic constraints. In particular, we propose new symbolization algorithms which constrain the symbols to have a Zipf–Mandelbrot–Li distribution which approximates the behavior of language elements. We introduce a novel constrained EM algorithm which is shown to effectively learn to produce symbols which approximate a Zipfian distribution. We demonstrate the efficacy of the proposed approaches on some examples using real world data in different tasks, including the translation of animal behavior into a possible human language understandable equivalent.

The development of nano-QSPR models for viscosity of nanofluids using the index of ideality of correlation and the correlation intensity index

Utilizing nanofluids as a suspension containing nanoparticles in an ordinary liquid is a relatively new field, which received great attention in recent years. The main object of this investigation is focused on modeling the effective viscosity of nanofluids using the nano-quantitative structure-property relationship (nano-QSPR) paradigm. Two distinct data sets were considered containing four types of nanoparticles (Al2O3, CuO, SiO2, and ZnO) dispersed in water (as the most common base fluid) at volume fraction ranges of 1–5% and various shapes (blades, bricks, cylindrical, spherical, and platelets). Simplified Molecular Input-Line Entry System (SMILES) is a tool to represent the molecular structure. Quasi-SMILES is a sequence of symbols that represents all available data e.g. molecular structure together with physicochemical conditions. Taking into account the capability of quasi-SMILES molecular representation to define the eclectic data such as size and shape of nanoparticles, this notation was chosen to exemplify nanofluids structure. It is remarkable to point out that the proposed attitude to generate nano-QSPR models introduced a comparison between two specific predictive potential criteria using by Monte Carlo technique. It was concluded that the development of models based on Correlation intensity index (CII) is statistically more reliable than model generation based on the Index of ideality of correlation (IIC).

A Myhill-Nerode theorem for register automata and symbolic trace languages

We propose a new symbolic trace semantics for register automata (extended finite state machines) which records both the sequence of input symbols that occur during a run as well as the constraints on input parameters that are imposed by this run. Our main result is a generalization of the classical Myhill-Nerode theorem to this symbolic setting. Our generalization requires the use of three relations to capture the additional structure of register automata. Location equivalence ≡l captures that symbolic traces end in the same location, transition equivalence ≡t captures that they share the same final transition, and a partial equivalence relation ≡r captures that symbolic values v and v′ are stored in the same register after symbolic traces w and w′, respectively. A symbolic language is defined to be regular if relations ≡l, ≡t and ≡r exist that satisfy certain conditions, in particular, they all have finite index. We show that the symbolic language associated to a register automaton is regular, and we construct, for each regular symbolic language, a register automaton that accepts this language. Our result provides a foundation for grey-box learning algorithms in settings where the constraints on data parameters can be extracted from code using e.g. tools for symbolic/concolic execution or tainting. Moving to a grey-box setting may overcome the scalability problems of state-of-the-art black-box learning algorithms.