Fermions: Spin, Hidden Variables, Violation of Bell’s Inequality and Quantum Entanglement

Using real fields instead of complex ones, it was recently claimed, that all fermions are made of pairs of coupled fields (strings) with an internal tension related to mutual attraction forces, related to Planck’s constant. Quantum mechanics is described with real fields and real operators. Schrodinger and Dirac equations then are solved. The solution to Dirac equation gives four, real, 2-vectors solutions ψ1=(U1D1)ψ2=(U2D2)ψ3=(U3D3)ψ4=(U4D4)where (ψ1,ψ4) are coupled via linear combinations to yield spin-up and spin-down fermions. Likewise, (ψ2,ψ3) are coupled via linear combinations to represent spin-up and spin-down anti-fermions. For an incoming entangled pair of fermions, the combined solution is Ψin=c1ψ1+c4ψ4where c1and c4are some hidden variables. By applying a magnetic field in +Z and +x the theoretical results of a triple Stern-Gerlach experiment are predicted correctly. Then, by repeating Bell’s and Mermin Gedanken experiment with three magnetic filters σθ, at three different inclination angles θ, the violation of Bell’s inequality is proven. It is shown that all fermions are in a mixed state of spins and the ratio between spin-up to spin-down depends on the hidden variables.

Deciphering the Hidden Secrets between the Early Skin Wrinkling & the Metabolic (X) Syndrome with the Possible Reversal of This Process at the Molecular Level

The aging process is a group of degenerative changes that physiologically occur in most of the people in the elderly. This affects one or more of the human body systems. The treatment of diseases related to the aging process has a huge impact on the economy of all nations. Aging of the skin comes on the top and despite that, the results of the already present lines of treatment are not always satisfactory. This acts as a stimulus for us to dig deeper to discover the root causes of the premature aging of the skin. This was simply caused by the accumulation of repeated minute damage to the internal structure skin. In other words, if the degree of minute damage is more than the capacity of the skin to repair, the repeated micro-damage is presented in the long run as a skin wrinkling. Moreover, the skin acts as a mirror that reflects the internal structures of the human body. Thus, the more degenerative changes in the human body systems, the more the skin could become wrinkled. Our strategy to prevent or at least slow down the aging process of the skin depends on 2 main steps;the 1st is to reduce the micro-damage as can as possible, and the 2nd is to enhance the capacity of tissue regeneration to be able to reverse the already present damaged skin. As the 2 processes are synchronized with each other, this strategy would be considered the ideal for prevention of skin wrinkling especially premature ones. This not only reverses premature skin wrinkling but also protects it from future wrinklings. This review sharply pointed out the role of the functional collagen of the dermal layer of the skin in the prevention of skin wrinklings. Therefore, it would be the target to study how collagen works in the complex machinery of the dermal layer of the skin. This concept deeply believes that the recovery of dermal collagen has a much better effect than simply ingesting collagen or receiving a topical collagen booster. .

Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.

Prediction of Hidden Blood Loss During Posterior Spinal Surgery

Objective Identification of the risk factors for extraordinary hidden blood loss(HBL) could clarify the underlying causes and provide more appropriate management. This study aims to identify the predictors of HBL in spinal surgery.Methods Medical records were retrospectively retrieved to collect the data of patients who undergoing posterior thoracic and lumbar fusion surgery or scoliosis surgery. Demographic information, perioperative visible blood loss volume, as well as laboratory results were recorded. The patients receiving fusion surgery or scoliosis surgery were further divided into the HBL positive subgroup and the HBL negative subgroup. Differences in the variables between the groups were then analyzed. Binary logistic regression analysis was performed to determine independent risk factors associated with HBL.Results For patients undergoing posterior spinal surgery, the independent risk factors associated with HBL were autologous transfusion(for fusion surgery P = 0.011, OR: 2.627, 95%CI: 1.574-2.782; for scoliosis surgery P < 0.001, OR: 2.268, 95%CI: 2.143-2.504) and allogeneic transfusion(for fusion surgery P < 0.001, OR: 6.487, 95%CI: 2.349-17.915; for scoliosis surgery P < 0.001, OR: 3.636, 95%CI: 2.389-5.231).Conclusion Intraoperative blood transfusion might be an early-warning indicator for perioperative HBL.

基于启发式查询词选择算法的Hidden Web获取研究

Hidden Web因为其隐蔽性而难以直接抓取,因此成为信息检索研究的一个新领域。提出了一种获取Hidden Web信息的方法,讨论了实现的关键技术。通过设计提出的启发式查询词选择算法,提高了抓取的效率。实验证明了该模型和算法的有效性。

Ontology mapping based on hidden Markov model

The existing ontology mapping methods mainly consider the structure of the ontology and the mapping precision is lower to some extent. According to statistical theory, a method which is based on the hidden Markov model is presented to establish ontology mapping. This method considers concepts as models, and attributes, relations, hierarchies, siblings and rules of the concepts as the states of the HMM, respectively. The models corresponding to the concepts are built by virtue of learning many training instances. On the basis of the best state sequence that is decided by the Viterbi algorithm and corresponding to the instance, mapping between the concepts can be established by maximum likelihood estimation. Experimental results show that this method can improve the precision of heterogeneous ontology mapping effectively.

TONE MODELING BASED ON HIDDEN CONDITIONAL RANDOM FIELDS AND DISCRIMINATIVE MODEL WEIGHT TRAINING

The use of hidden conditional random fields （HCRFs） for tone modeling is explored. The tone recognition performance is improved using HCRFs by taking advantage of intra-syllable dynamic, inter-syllable dynamic and duration features. When the tone model is integrated into continuous speech recognition, the discriminative model weight training （DMWT） is proposed. Acoustic and tone scores are scaled by model weights discriminatively trained by the minimum phone error （MPE） criterion. Two schemes of weight training are evaluated and a smoothing technique is used to make training robust to overtraining problem. Experiments show that the accuracies of tone recognition and large vocabulary continuous speech recognition （LVCSR） can be improved by the HCRFs based tone model. Compared with the global weight scheme, continuous speech recognition can be improved by the discriminative trained weight combinations.

Hidden Web信息获取

如今Web上越来越多的信息可以通过查询接口获得,但为了获取某Hidden Web站点的页面,用户不得不键入一系列的关键词。由于没有直接指向Hidden Web页面的静态链接,当前大多搜索引擎不能发现和索引这些页面。然而,研究表明,由Hidden Web站点提供的高质量的信息对许多用户来说非常有价值。文章通过研究针对特定类型的表单,建立一个有效的Hidden Web爬虫,以便获取Hidden Web后台数据库信息。

Optimal state and branch sequence based parameter estimation of continuous hidden Markov model

A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

IMPROVEMENTS IN HIDDEN LINE REMOVAL ALGORITHM FOR BI-PARAMETRIC SURFACES

A hidden line removal algorithm for bi parametric surfaces is presented and illustrated by some experimental results. The enclosure test is done using area coordinates. A technique of moving box of encirclement is presented. It is found that the algorithm is of general purpose, requires minimal computer storage, has high accuracy and simplicity, and is very easy to be implemented on a computer.

Study on Problems and Countermeasures of English Hidden Curriculum Construction in Higher Vocational Colleges in the Era of “Internet Plus”

As the extension and supplement of explicit curriculum,the English hidden curriculum in higher vocational colleges plays animportant role in cultivating students' behaviors,stimulate their interest and arouse their enthusiasm,initiative and creativity in learningEnglish.This paper analyzes the problems of English hidden curriculum construction in higher vocational colleges under the back-ground o"fInternet plus",and puts forward some corresponding countermeasures to bring the role of college English hidden curriculuminto full play.

Frame erasure concealment in wideband speech coding based on large hidden Markov model

Frame erasure concealment is studied to solve the problem of rapid speech quality reduction due to the loss of speech parameters during speech transmission. A large hidden Markov model is applied to model the immittance spectral frequency （ISF） parameters in AMR-WB codec to optimally estimate the lost ISFs based on the minimum mean square error （MMSE） rule. The estimated ISFs are weighted with the ones of their previous neighbors to smooth the speech, resulting in the actual concealed ISF vectors. They are used instead of the lost ISFs in the speech synthesis on the receiver. Comparison is made between the speech concealed by this algorithm and by Annex I of G. 722. 2 specification, and simulation shows that the proposed concealment algorithm can lead to better performance in terms of frequency-weighted spectral distortion and signal-to-noise ratio compared to the baseline method, with an increase of 2.41 dB in signal-to-noise ratio （SNR） and a reduction of 0. 885 dB in frequency-weighted spectral distortion.

On-line Fault Diagnosis in Industrial Processes Using Variable Moving Window and Hidden Markov Model

An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.

Hidden cochlear impairments

Pure tone audiometry is a routine clinical examination used to identify hearing loss. A normal pure tone audiogram is usually taken as evidence of normal hearing. Auditory deficits detected in subjects with normal audiograms, such as poor sound discrimination and auditory perceptual disorders, are generally attributed to central problems. Does the pure tone audiogram truly reflect cochlear status？ Recent evidence suggests that individuals with normal audiogram may still have reduced peripheral auditory responses but normal central responses, indicating that the pure tone audiometry may not detect some types of cochlear injuries. In the cochlea, the outer hair cells （OHCs）, inner hair cells （IHCs）, and the spiral ganglion neurons that synapse with IHCs are the 3 key cochlear components in transducing acoustical vibrations into the neural signals. This report reviews three types of cochlear damage identified in laboratory animals that may not lead to overt hearing loss. The first type of cochlear impairment, such as missing a certain proportion of IHCs without damage to OHCs, may reduce the cochlear output and elevate response threshold; however, the reduced peripheral auditory sensitivity may be restored along the auditory pathway via central gain enhancement. The second type of cochlear impairment, such as selective damage to the synapses of the high-threshold thin auditory nerve fibers （ANFs）, reduces cochlear output at high stimulation levels with no effect on response threshold. In this case the reduced cochlear output may be compensated along the auditory pathway as well. The third type of cochlear impairment, such as missing a certain number of OHCs without damage to others, may not even affect cochlear function at all. These ＂hidden＂ cochlear impairments do not result in overt hearing loss, but they may increase the vulnerability of the cochlea to traumatic exposure and lead to disrupted central auditory processing.

Prediction of Hidden Granites in the Luziyuan Area of Yunnan Province and the Prospecting Direction

The Fe-Pb-Zn-Cu polymetallic deposits in the Luziyuan area, are of a sedimentary-reformed type related with magmatic hydrothermalism. Previous researches have suggested that the mineralization is closely related to the hidden granites, but little is known about these granites including their burial depth and scale, which has limited the establishment of prospecting models and the optimization of prospecting targets. Geophysical methods have a great exploration depth, and have played a unique role in the prediction of hidden granites. It is shown that granites have low density and high resistivity,

Improved hidden Markov model for speech recognition and POS tagging

In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.

Complex transient dynamics of hidden attractors in a simple 4D system

A simple four-dimensional system with only one control parameter is proposed in this paper. The novel system has a line or no equilibrium for the global control parameter and exhibits complex transient transition behaviors of hyperchaotic attractors, periodic orbits, and unstable sinks. Especially, for the nonzero-valued control parameter, there exists no equilibrium in the proposed system, leading to the formation of various hidden attractors with complex transient dynamics. The research results indicate that the dynamics of the system shows weak chaotic robustness and depends greatly on the initial states.

Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform

Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide.The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram(EEG) as a noninvasive procedure to record neuronal activities in the brain.EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals.Shannon entropy,collision entropy,transfer entropy,conditional probability,and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform.Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification.Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector.The accuracy of the proposed approach is higher for Q=2 and J=10.Transfer entropy is observed to be significant for different class combinations.Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time.The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals.The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.

A new nonlinear oscillator with infinite number of coexisting hidden and self-excited attractors

In this paper,we introduce a new two-dimensional nonlinear oscillator with an infinite number of coexisting limit cycles.These limit cycles form a layer-by-layer structure which is very unusual.Forty percent of these limit cycles are self-excited attractors while sixty percent of them are hidden attractors.Changing this new system to its forced version,we introduce a new chaotic system with an infinite number of coexisting strange attractors.We implement this system through field programmable gate arrays.

Detecting Hidden Malware Method Based on "In-VM" Model

Security tools are rapidly developed as network security threat is becoming more and more serious.To overcome the fundamental limitation of traditional host-based anti-malware system which is likely to be deceived and attacked by malicious codes,VMM-based anti-malware systems have recently become a hot research field.In this article,the existing malware hiding technique is analyzed,and a detecting model for hidden process based on "In-VM" idea is also proposed.Based on this detecting model,a hidden process detection technology which is based on HOOK SwapContext on the VMM platform is also implemented successfully.This technology can guarantee the detecting method not to be attacked by malwares and also resist all the current process hiding technologies.In order to detect the malwares which use remote injection method to hide themselves,a method by hijacking sysenter instruction is also proposed.Experiments show that the proposed methods guarantee the isolation of virtual machines,can detect all malware samples,and just bring little performance loss.