String Vector Research Articles

Quantum tricriticality of incommensurate phase induced by quantum strings in frustrated Ising magnetism

Incommensurability plays a critical role in many strongly correlated systems. In some cases, the origin of such exotic order can be theoretically understood in the framework of 1d line-like topological excitations known as “quantum strings”. Here we study an extended transverse field Ising model on a triangular lattice. Using large-scale quantum Monte Carlo simulations, we find that the spatial anisotropy can stabilize an incommensurate phase out of the commensurate clock order. Our results for the structure factor and the string density exhibit a linear relationship between incommensurate ordering wave vector and the density of quantum strings, which is reminiscent of hole density in under-doped cuprate superconductors. When introducing the next-nearest-neighbor interaction, we observe a quantum tricritical point out of the incommensurate phase. After carefully analyzing the ground state energies within different string topological sectors, we conclude that this tricriticality is non-trivially caused by effective long-range inter-string interactions with two competing terms following different decaying behaviors.

Analysis of the Abnormality of Traction Energy Consumption in Urban Rail Transit System

Traction energy consumption (TEC) is a critical part of the total energy consumption in urban rail transit (URT) systems. Energy consumption patterns and abnormal analysis of TEC guarantee the energy‐saving URT operation. With the rapid development of urbanization, the current energy consumption is becoming more and more prominent with some inherent drawbacks, such as complex original data, complicated statistical analysis, and abnormal energy consumption. This paper proposes a method for time accrual abnormal analysis of TEC. The system architecture of TEC typical values is presented, composed of three elements: research object, evaluation index, and time scale. The time series prediction algorithm calculates the typical values of the cumulative energy consumption index in each energy consumption mode. For the abnormality in TEC mode, the distance of the string vector is used as the similarity measure. Then, the similarity‐based anomaly analysis method is used to judge the pattern abnormality. By comparing the advantages and disadvantages of engineering practice and theoretical research methods, we analyze the applicability of traditional anomaly detection algorithms to perform anomaly analysis of TEC in URT systems. The adopted time accrual abnormal analysis achieves a high fault detection rate, outperforming other models.

IMPROVING MULTI-LABEL TEXT CLASSIFICATION USING WEIGHTED INFORMATION GAIN AND CO-TRAINED MULTINOMIAL NAÏVE BAYES CLASSIFIER

Over recent years, the emergence of electronic text processing systems has generated a vast amount of structured and unstructured data, thus creating a challenging situation for users to rummage through irrelevant information. Therefore, studies are continually looking to improve the classification process to produce more accurate results that would benefit users. This paper looks into the weighted information gain method that re-assigns wrongly classified features with new weights to provide better classification. The method focuses on the weights of the frequency bins, assuming every time a certain word frequency bin is iterated, it provides information on the target word feature. Therefore, the more iteration and re-assigning of weight occur within the bin, the more important the bin becomes, eventually providing better classification. The proposed algorithm was trained and tested using a corpus extracted from dedicated Facebook pages related to diabetes. The weighted information gain feature selection technique is then fed into a co-trained Multinomial Naïve Bayes classification algorithm that captures the labels' dependencies. The algorithm incorporates class value dependencies since the dataset used multi-label data before converting string vectors that allow the sparse distribution between features to be minimised, thus producing more accurate results. The results of this study show an improvement in classification to 61%.

Performance of Methods in Identifying Similar Languages Based on String to Word Vector

Indonesia has a large number of local languages that have cognate words, some of which have similarities among each other. Automatic identification within a family of languages faces problems, so it is necessary to learn the best performer of language identification methods in doing the task. This study made an effort to identification Indonesian local languages, which used String to Word Vector approach. A string vector refers to a collection of ordered words. In a string vector, a word is represented as an element or value, while the word becomes an attribute or feature in each numeric vector. Among Naive Bayes, SMO, J48, and ZeroR classifiers, SMO is found to be the most accurate classifier with a level of accuracy at 95.7% for 10-fold cross-validation and 94.4% for 60%: 40%. The best tokenizer in this classification is Character N-Gram. All classifiers, except ZeroR shows increased accuracy when using Character N-Gram Tokenizer compared to Word Tokenizer. The best features of this system are the TriGram and FourGram Character. The TriGram is preferred because it requires smaller training data. The highest accuracy value in the combination experiment is 0.965 obtained at a combination of IDF = FALSE and WC = TRUE, regardless the conditions of the TF.

Semantic string operation for specializing AHC algorithm for text clustering

This article proposes the modified AHC (Agglomerative Hierarchical Clustering) algorithm which clusters string vectors, instead of numerical vectors, as the approach to the text clustering. The results from applying the string vector based algorithms to the text clustering were successful in previous works and synergy effect between the text clustering and the word clustering is expected by combining them with each other; the two facts become motivations for this research. In this research, we define the operation on string vectors called semantic similarity, and modify the AHC algorithm by adopting the proposed similarity metric as the approach to the text clustering. The proposed AHC algorithm is empirically validated as the better approach in clustering texts in news articles and opinions. We need to define and characterize mathematically more operations on string vectors for modifying more advanced machine learning algorithms.

Automatic text summarization using string vector based K nearest neighbor

Automatic text summarization using string vector based K nearest neighbor

A Fast Single-Key Two-Level Universal Hash Function

Universal hash functions based on univariate polynomials are well known, e.g. Poly1305 and GHASH. Using Horner’s rule to evaluate such hash functionsrequire l − 1 field multiplications for hashing a message consisting of l blocks where each block is one field element. A faster method is based on the class of Bernstein-Rabin-Winograd (BRW) polynomials which require ⌊l/2⌋ multiplications and ⌊lgl⌋ squarings for l≥3 blocks. Though this is significantly smaller than Horner’s rule based hashing, implementation of BRW polynomials for variable length messages present significant difficulties. In this work, we propose a two-level hash function where BRW polynomial based hashing is done at the lower level and Horner’s rule based hashing is done at the higher level. The BRW polynomial based hashing is applied to a fixed number of blocks and hence the difficulties in handling variable length messages is avoided. Even though the hash function has two levels, we show that it is sufficient to use a single field element as the hash key. The basic idea is instantiated to propose two new hash functions, one which hashes a single binary string and the other can hash a vector of binary strings. We describe two actual implementations, one over F2128 and the other over F2256 both using the pclmulqdq instruction available in modern Intel processors. On both the Haswell and Skylake processors, the implementation over F2128 is faster than both an implementation of GHASH by Gueron; and a highly optimised implementation, also by Gueron, of another polynomial based hash function called POLYVAL. We further show that the Fast Fourier Transform based field multiplication over F2256 proposed by Bernstein and Chou can be used to evaluate the new hash function at a cost of about at most 46 bit operations per bit of digest, but, unlike the Bernstein-Chou analysis, there is no hidden cost of generating the hash key. More generally, the new idea of building a two-level hash function having a single field element as the hash key can be applied to other finite fields to build new hash functions.

A Fast Single-Key Two-Level Universal Hash Function

Universal hash functions based on univariate polynomials are well known, e.g. Poly1305 and GHASH. Using Horner’s rule to evaluate such hash functionsrequire l − 1 field multiplications for hashing a message consisting of l blocks where each block is one field element. A faster method is based on the class of Bernstein-Rabin-Winograd (BRW) polynomials which require ⌊ l /2⌋ multiplications and ⌊lg l ⌋ squarings for l ≥3 blocks. Though this is significantly smaller than Horner’s rule based hashing, implementation of BRW polynomials for variable length messages present significant difficulties. In this work, we propose a two-level hash function where BRW polynomial based hashing is done at the lower level and Horner’s rule based hashing is done at the higher level. The BRW polynomial based hashing is applied to a fixed number of blocks and hence the difficulties in handling variable length messages is avoided. Even though the hash function has two levels, we show that it is sufficient to use a single field element as the hash key. The basic idea is instantiated to propose two new hash functions, one which hashes a single binary string and the other can hash a vector of binary strings. We describe two actual implementations, one over F 2 128 and the other over F 2 256 both using the pclmulqdq instruction available in modern Intel processors. On both the Haswell and Skylake processors, the implementation over F 2 128 is faster than both an implementation of GHASH by Gueron; and a highly optimised implementation, also by Gueron, of another polynomial based hash function called POLYVAL. We further show that the Fast Fourier Transform based field multiplication over F 2 256 proposed by Bernstein and Chou can be used to evaluate the new hash function at a cost of about at most 46 bit operations per bit of digest, but, unlike the Bernstein-Chou analysis, there is no hidden cost of generating the hash key. More generally, the new idea of building a two-level hash function having a single field element as the hash key can be applied to other finite fields to build new hash functions.

Optimizing Search Results using Wikipedia based ESS and Enhanced TF-IDF Approach

The Triangular Search approach aims at recalculating authenticity of the Search Results provided by the Google API with the help of Semantic similarity provided by Wikipedia API and calculating the cosine similarity between the Document Vectors and query string vectors using enhanced approach of Tf-Idf that reduces calculation involved in it. The Search Engine Optimization traces anchor texts that are the values between tag of HTML and body texts of a web page. Using the Vector Space Model, the Term frequency and Inverse document frequency are calculated along with the Page ranking algorithm to get the Search Results. But consideration of anchor texts in search engine optimization techniques leads to some of the non-relevant body texts of a document. Also the top results of a search engine include trending and e-commerce links other than sponsored links but the intent of search is not considered. This approach proposes and gains user intents behind the search thereby focusing on providing intent related search results. General Terms Optimized Search Results Abbreviations ESS/ ESA: Explicit Semantic Similarity/Analysis TF-IDF : Term Frequency Inverse Domain Frequency API : Application Programmable Interface

Representation of Texts into String Vectors for Text Categorization

In this study, we propose a method for encoding documents into string vectors, instead of numerical vectors. A traditional approach to text categorization usually requires encoding documents into numerical vectors. The usual method of encoding documents therefore causes two main problems: huge dimensionality and sparse distribution. In this study, we modify or create machine learning-based approaches to text categorization, where string vectors are received as input vectors, instead of numerical vectors. As a result, we can improve text categorization performance by avoiding these two problems.

Inverted Index based Modified Version of K-Means Algorithm for Text Clustering

This research proposes a new strategy where documents are encoded into string vectors and modified version of k means algorithm to be adaptable to string vectors for text clustering. Traditionally, when k means algorithm is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text clustering, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the k means algorithm adaptable to string vectors for text clustering.

Inverted Index based Modified Version of KNN for Text Categorization

This research proposes a new strategy where documents are encoded into string vectors and modified version of KNN to be adaptable to string vectors for text categorization. Traditionally, when KNN are used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the supervised learning algorithms adaptable to string vectors for text categorization.

Modified Version of SVM for Text Categorization

This research proposes a new strategy where documents are encoded into string vectors for text categorization and modified versions of SVM to be adaptable to string vectors. Traditionally, when the traditional version of SVM is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and apply the modified version of SVM adaptable to string vectors for text categorization.

Generalised key delegation for hierarchical identity-based encryption

This paper introduces a new primitive called identity-based encryption with wildcard key derivation (WKD-IBE or 'wicked IBE') that enhances the concept of hierarchical identity-based encryption by allowing more general key delegation patterns. A secret key is derived for a vector of identity strings, where entries can be left blank using a wildcard. This key can then be used to derive keys for any pattern that replaces wildcards with concrete identity strings. For example, one may want to allow the university's head system administrator to derive secret keys (and hence the ability to decrypt) for all departmental sysadmin email addresses sysadmin@*.univ.edu, where * is a wildcard that can be replaced with any string. The authors provide appropriate security notions and provably secure instantiations with different tradeoffs in terms of ciphertext size and efficiency. The authors also present a generic construction of identity-based broadcast encryption (IBBE) from any WKD-IBE scheme. One of their instantiations yields an IBBE scheme with constant ciphertext size.

String predictions for near future colliders from one-loop scattering amplitudes around D-brane worlds

We derive compact expressions for one-loop scattering amplitudes of four open-string vector bosons around supersymmetric configurations with intersecting or magnetized D-branes on toroidal orbifolds. We check the validity of our formulae against the structure of their singularities and their behaviour under modular transformations to the transverse channel, exposing closed string exchange. We then specialize to the case of forward scattering and compute the total cross section for two massless open string vector bosons on the brane to decay into closed strings in the bulk, relying on the optical theorem. Although not directly related to collider signatures our predictions represent a step forward towards unveiling phenomenological implications of open and unoriented superstrings

Comparison of String Fluid Dynamical Models in General Relativity and Einstein–Cartan theory

Dynamical models of string fluids areconstructed from the general energy-momentum tensor forstring fluids in general relativity and theEinstein-Cartan theories obtained from the Ray-Hilbertvariational principle. Examples of solutions to the fieldequations for general relativistic spacetimes are givenand compared with solutions obtained from the postulatedenergy-momentum tensor of Letelier. Solutions to the field equations in Riemann-Cartanspacetimes are compared with an extended Leteliersolution. All calculations are given for both thestandard and the extended thermodynamics versions inwhich the latter includes the string as thermodynamicvariables. In general relativity, it is shown for blackhole solutions that the general feature of strings(through the string vector) is to produce a shrinkage of the black hole horizon. In RiemannCartanspacetimes, the torsion field equation shows that stringvector can be identified with the torsion vector. Themost striking feature of strings in Riemann-Cartan spacetimes is that in the Reissner-Nordstromsolution, the addition of torsional strings produces thecorrect asympototic behavior of the metric necessary tomatch the experimental galactic rotationcurves.