Variational data encoding and correlations in quantum-enhanced machine learning

Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum computation.Different encoding strategies can significantly influence quantum computer performance.Secondly,we address the need to counteract the inevitable noise that can hinder quantum acceleration.Our primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm models.By adapting the learning concept from machine learning,we render data encoding a learnable process.This allowed us to study the role of quantum correlation in data encoding.Through numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional data.Moreover,we delve into the role of quantum correlation in enhancing task performance,especially in noisy environments.Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing.

A novel encoding mechanism for particle physics

This study proposes a novel particle encoding mechanism that seamlessly incorporates the quantum properties of particles,with a specific emphasis on constituent quarks.The primary objective of this mechanism is to facilitate the digital registration and identification of a wide range of particle information.Its design ensures easy integration with different event generators and digital simulations commonly used in high-energy experiments.Moreover,this innovative framework can be easily expanded to encode complex multi-quark states comprising up to nine valence quarks and accommodating an angular momentum of up to 99/2.This versatility and scalability make it a valuable tool.

A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

A highly reliable encoding and decoding communication framework based on semantic information

Increasing research has focused on semantic communication,the goal of which is to convey accurately the meaning instead of transmitting symbols from the sender to the receiver.In this paper,we design a novel encoding and decoding semantic communication framework,which adopts the semantic information and the contextual correlations between items to optimize the performance of a communication system over various channels.On the sender side,the average semantic loss caused by the wrong detection is defined,and a semantic source encoding strategy is developed to minimize the average semantic loss.To further improve communication reliability,a decoding strategy that utilizes the semantic and the context information to recover messages is proposed in the receiver.Extensive simulation results validate the superior performance of our strategies over state-of-the-art semantic coding and decoding policies on different communication channels.

A Multi-Stream Scrambling and DNA Encoding Method Based Image Encryption

Information security has emerged as a key problem in encryption because of the rapid evolution of the internet and networks.Thus,the progress of image encryption techniques is becoming an increasingly serious issue and considerable problem.Small space of the key,encryption-based low confidentiality,low key sensitivity,and easily exploitable existing image encryption techniques integrating chaotic system and DNA computing are purposing the main problems to propose a new encryption technique in this study.In our proposed scheme,a three-dimensional Chen’s map and a one-dimensional Logistic map are employed to construct a double-layer image encryption scheme.In the confusion stage,different scrambling operations related to the original plain image pixels are designed using Chen’s map.A stream pixel scrambling operation related to the plain image is constructed.Then,a block scrambling-based image encryption-related stream pixel scrambled image is designed.In the diffusion stage,two rounds of pixel diffusion are generated related to the confusing image for intra-image diffusion.Chen’s map,logistic map,and DNA computing are employed to construct diffusion operations.A reverse complementary rule is applied to obtain a new form of DNA.A Chen’s map is used to produce a pseudorandom DNA sequence,and then another DNA form is constructed from a reverse pseudorandom DNA sequence.Finally,the XOR operation is performed multiple times to obtain the encrypted image.According to the simulation of experiments and security analysis,this approach extends the key space,has great sensitivity,and is able to withstand various typical attacks.An adequate encryption effect is achieved by the proposed algorithm,which can simultaneously decrease the correlation between adjacent pixels by making it near zero,also the information entropy is increased.The number of pixels changing rate(NPCR)and the unified average change intensity(UACI)both are very near to optimal values.

On the sampling strategies and models for measuring diffusion exchange with a double diffusion encoding sequence

Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging(dMRI).Many analysis frameworks using dMRI data have been proposed to describe exchange,often using a double diffusion encoding(DDE)stimulated echo sequence.Techniques such as diffusion exchange weighted imaging(DEWI)and the filter exchange and rapid exchange models,use a specific subset of the full space DDE signal.In this work,a general representation of the DDE signal was employed with different sampling schemes(namely constant b1,diagonal and anti-diagonal)from the data reduction models to estimate exchange.A near-uniform sampling scheme was proposed and compared with the other sampling schemes.The filter exchange and rapid exchange models were also applied to estimate exchange with their own subsampling schemes.These subsampling schemes and models were compared on both simulated data and experimental data acquired with a benchtop MR scanner.In synthetic data,the diagonal and near-uniform sampling schemes performed the best due to the consistency of their estimates with the ground truth.In experimental data,the shifted diagonal and near-uniform sampling schemes outperformed the others,yielding the most consistent estimates with the full space estimation.The results suggest the feasibility of measuring exchange using a general representation of the DDE signal along with variable sampling schemes.In future studies,algorithms could be further developed for the optimization of sampling schemes,as well as incorporating additional properties,such as geometry and diffusion anisotropy,into exchange frameworks.

The Dissemination of Chinese Tea Culture Based on Encoding/Decoding Theory

Tea has a history of thousands of years in China and it plays an important role in the working-life and daily life of people.Tea culture rich in connotation is an important part of Chinese traditional culture,and its existence and development are also of great significance to the diversified development of world culture.Based on Stuart Hall’s encoding/decoding theory,this paper analyzes the problems in the spreading of Chinese tea in and out of the country and provides solutions from the perspective of encoding,communication,and decoding.It is expected to provide a reference for the domestic and international dissemination of Chinese tea culture.

The Dissemination of Intangible Cultural Heritage Along Beijing-Hangzhou Canal Based on Encoding/Decoding Theory

The Beijing-Hangzhou Grand Canal carries a wealth of Chinese cultural symbols,showing the lifestyle and wisdom of working people through ages.The preservation and inheritance of its intangible cultural heritage can help to evoke cultural memories and cultural identification of the Canal and build cultural confidence.This paper applies Stuart Hall’s encoding/decoding theory to analyze the dissemination of intangible heritage tourism culture.On the basis of a practical study of the villages along the Beijing-Hangzhou Grand Canal,this paper analyses the problems in the transmission of its intangible cultural heritage and proposes specific methods to solve them in four processes,encoding,decoding,communication,and secondary encoding,in order to propose references for the transmission of intangible heritage culture at home and abroad.

Continuous Pseudocolor Encoding of Thermal Image Display

The visual features of continuous pseudocolor encoding is discussed and the optimiz- ing design algorithm of continuous pseudocolor scale is derived.The algorithm is restricting the varying range and direction of lightness,hue and saturation according to correlation and naturalness,automatically calculating the chromaticity coordinates of nodes in uniform color space to get the longest length of scale path,then interpolating points between nodes in equal color differences to obtain continuous pseudocolor scale with visual uniformity.When it was applied to the pseudocolor encoding of thermal image displays,the results showed that the correlation and the naturalness of original images and cognitive characteristics of target pattern were reserved well;the dynamic range of visual perception and the amount of visual information increased obviously;the contrast sensitivity of target identification improved;and the blindness of scale design were avoided.

Delay and Energy Efficient Design of an On-Chip Bus with Repeaters Using a New Spatial and Temporal Encoding Technique

On-chip global buses in deep sub-micron designs consume significant amounts of energy and have large propagation delays. Thus, minimizing energy dissipation and propagation delay is an important design objective. In this paper, we propose a new spatial and temporal encoding approach for generic on-chip global buses with repeaters that enables higher performance while reducing peak energy and average energy. The proposed encoding approach exploits the benefits of a temporal encoding circuit and spatial bus-invert coding techniques to simultaneously eliminate opposite transitions on adjacent wires and reduce the number of self-transitions and coupling-transitions. In the design process of applying encoding techniques for reduced bus delay and energy, we present a repeater insertion design methodology to determine the repeater size and inter-repeater bus length, which minimizes the total bus energy dissipation while satisfying target delay and slew-rate constraints. This methodology is employed to obtain optimal energy versus delay trade-offs under slew-rate constraints for various encoding techniques.

A VIDEO SPECTRUM SPLITTING ENCODING SCHEME BASED ON HUMAN VISION AND ITS COMPUTER SIMULATION

In this paper, a 3-D video encoding scheme suitable for digital TV/HDTV (high definition television) is studied through computer simulation. The encoding scheme is designed to provide a good match to human vision. Basically, this involves transmission of low frequency luminance information at full frame rate for good motion rendition and transmission of high frequency luminance signal at reduced frame rate for good detail in static images.

The Application and Adaptation of the Two Sources of Code and Natural Encoding Method for Designing a Model of Microprogram Control Unit with Base Structure

The article presents a modification to the method which applies two sources of data. The modification is depicted on the example of a compositional microprogram control unit (CMCU) model with base structure implemented in the complex programmable logic devices (CPLD). First, the conditions needed to apply the method are presented, followed by the results of its implementation in real hardware.

On the Manipulation of the Selectivity of Encoding and Decoding overthe Translator's Subjectivity

The translation activity is a process of the interlinguistic transmission of information realized by the information encoding and decoding.Encoding and decoding,cognitive practices operated in objective contexts,are inevitably of selectivity ascribing to the restriction of contextual reasons.The translator as the intermediary agent connects the original author(encoder)and the target readers(decoder),shouldering the dual duties of the decoder and the encoder,for which his subjectivity is irrevocably manipulated by the selectivity of encoding and decoding.

Dynamic matrix encoding strategy for voice-over-IP steganography

In order to optionally regulate embedding capacity and embedding transparency according to user's requirements in voice-over-IP(VoIP) steganography,a dynamic matrix encoding strategy(DMES) was presented.Differing from the traditional matrix encoding strategy,DMES dynamically chose the size of each message group in a given set of adoptable message sizes.The appearance possibilities of all adoptable sizes were set in accordance with the desired embedding performance(embedding rate or bit-change rate).Accordingly,a searching algorithm that could provide an optimal combination of appearance possibilities was proposed.Furthermore,the roulette wheel algorithm was employed to determine the size of each message group according to the optimal combination of appearance possibilities.The effectiveness of DMES was evaluated in StegVoIP,which is a typical covert communication system based on VoIP.The experimental results demonstrate that DMES can adjust embedding capacity and embedding transparency effectively and flexibly,and achieve the desired embedding performance in any case.For the desired embedding rate,the average errors are not more than 0.000 8,and the standard deviations are not more than 0.002 0;for the desired bit-change rate,the average errors are not more than 0.001 4,and the standard deviations are not more than 0.002 6.

Cloning and Characterization of Largemouth Bass(Micropterus salmoides) Myostatin Encoding Gene and Its Promoter

Myostatin or GDF-8, a member of the transforming growth factor-β （TGF-β） superfamily, has been demonstrated to be a negative regulator of skeletal muscle mass in mammals. In the present study, we obtained a 5.64 kb sequence of myostatin encoding gene and its promoter from largemouth bass （Micropterus salmoides）. The myostatin encoding gene consisted of three exons （488bp, 371 bp and 1779bp, respectively） and two introns （390bp and 855 bp, respectively）. The intron-exon boundaries were conservative in comparison with those of mammalian myostatin encoding genes, whereas the size of introns was smaller than that of mammals. Sequence analysis of 1.569 kb of the largemouth bass myostatin gene promoter region revealed that it contained two TATA boxes, one CAAT box and nine putative E-boxes. Putative muscle growth response elements for myocyte enhancer factor 2 （MEF2）, serum response factor （SRF）, activator protein 1 （AP1）, etc., and muscle-specific Mt binding site （MTBF） were also detected. Some of the transcription factor binding sites were conserved among five teleost species. This infunnation will be useful for studying the tran- scriptional regulation of myostatin in fish.

Expression and Chromosomal Mapping of Mouse Gpx2 Gene Encoding the Gastrointestinal Form of Glutathione Peroxidase, GPX-GI

GPX-GI is a cytosolic tetrameric Se-dependent glutathione peroxidase, similar in properties to GPX-1. Unlike the almost ubiquitous GPX-1, GPX-GI is mainly expressed in the epithelium of gastrointestinal tract. GPX-GI contributes to at least fifty percent of GPX activity in rodent small intestmal epithelium. The total GPX activity consists of at least 70% of selenium-dependent GPX activity in this compartment.By analyzing a panel of mouse mterspecies DNA from the Jackson Laboratory's backcross resource,we mapped Gpx2 gene to mouse chromosome 12 between D12Mit4 and D12Mit5, near the Ccs1 locus which contains a colon cancer susceptibility gene. A pseudogene, Gpx2-ps is mapped to mouse chromosome 7.Comparison of Gpx2 gene expression in three pairs of C57BL/6Ha and ICR/Ha mice which are respectively resistant and sensitive to dimethylhydrazine-induced colon cancer, we found a higher Gpx2 mRNA level in C57BL/6Ha colon than ICR/Ha colon. Interestingly, a lower level of GPX activity is found in the resistant strain of mice. Because GPX-1 has three times higher specific activity than GPX GI, our data suggest that the decreased GPX activity may result from a higher level of Gpx2 gene expression in those cells co-express GPx1 gene

New Algorithm for Binary Connected-Component Labeling Based on Run-Length Encoding and Union-Find Sets

Based on detailed analysis of advantages and disadvantages of the existing connected-component labeling （CCL） algorithm,a new algorithm for binary connected components labeling based on run-length encoding （RLE） and union-find sets has been put forward.The new algorithm uses RLE as the basic processing unit,converts the label merging of connected RLE into sets grouping in accordance with equivalence relation,and uses the union-find sets which is the realization method of sets grouping to solve the label merging of connected RLE.And the label merging procedure has been optimized：the union operation has been modified by adding the ＂weighted rule＂ to avoid getting a degenerated-tree,and the ＂path compression＂ has been adopted when implementing the find operation,then the time complexity of label merging is O（nα（n））.The experiments show that the new algorithm can label the connected components of any shapes very quickly and exactly,save more memory,and facilitate the subsequent image analysis.

Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction

Accurate remaining useful life(RUL)prediction is important in industrial systems.It prevents machines from working under failure conditions,and ensures that the industrial system works reliably and efficiently.Recently,many deep learning based methods have been proposed to predict RUL.Among these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential information.This allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction task.In this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their performances.Additionally,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based methods.To solve these problems,we propose a series of new CNNs,which show competitive results to RNN based methods.Compared with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily determined.To alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a CNN.Hence,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based methods.Extensive experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Brain encoding and decoding via functional magnetic resonance imaging(fMRI)are two important aspects of visual perception neuroscience.Although previous researchers have made significant advances in brain encoding and decoding models,existing methods still require improvement using advanced machine learning techniques.For example,traditional methods usually build the encoding and decoding models separately,and are prone to overfitting on a small dataset.In fact,effectively unifying the encoding and decoding procedures may allow for more accurate predictions.In this paper,we first review the existing encoding and decoding methods and discuss the potential advantages of a“bidirectional”modeling strategy.Next,we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules.Furthermore,deep generative models(e.g.,variational autoencoders(VAEs)and generative adversarial networks(GANs))have produced promising results in studies on brain encoding and decoding.Finally,we propose that the dual learning method,which was originally designed for machine translation tasks,could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.

ANALYSIS OF SPECTRAL PHASE ENCODING OCDMA SYSTEM WITH PSEUDORANDOM CODING

Multiple access interference (MAI) is the most serious interference in spectral phase encoding optical code division multiple access (SPE OCDMA) systems. This paper focuses on the behavior of MAI in SPE OCDMA systems with pseudorandom coding. The statistical expectation of multi access interference (MAI) is derived and plotted. The results confirm that MAI can be suppressed effectively by pseudorandom coding with m sequences.