Comparison of ISFs and LSFs in Speech/Music Discrimination System

The immittance spectral frequencies （ISFs） is proposed as a new set of classification features and compared with the linear spectral frequencies （LSFs） applied in a frame-level wideband speech/music discrimination system. These two sets of features can be shared by the classifier and coding module to reduce the total computational complexity, making our classification system suitable for multi-mode audio coding applications. A performance assessment and comparison of the features are made. The experiment results show that the ISFs and LSFs have similar good performance when using full covariance matrices in classification models and the ISFs perform slightly better when using diagonal matrices. Their statistical differences for speech and music signals are also revealed.

Discrimination of polysorbate 20 by high-performance liquid chromatography-charged aerosol detection and characterization for components by expanding compound database and library

Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.

LociScan,a tool for screening genetic marker combinations for plant variety discrimination

To reduce the cost and increase the efficiency of plant genetic marker fingerprinting for variety discrimination,it is desirable to identify the optimal marker combinations.We describe a marker combination screening model based on the genetic algorithm(GA)and implemented in a software tool,Loci Scan.Ratio-based variety discrimination power provided the largest optimization space among multiple fitness functions.Among GA parameters,an increase in population size and generation number enlarged optimization depth but also calculation workload.Exhaustive algorithm afforded the same optimization depth as GA but vastly increased calculation time.In comparison with two other software tools,Loci Scan accommodated missing data,reduced calculation time,and offered more fitness functions.In large datasets,the sample size of training data exerted the strongest influence on calculation time,whereas the marker size of training data showed no effect,and target marker number had limited effect on analysis speed.

The Analysis of the Correlation between SPT and CPT Based on CNN-GA and Liquefaction Discrimination Research

The objective of this study is to investigate themethods for soil liquefaction discrimination. Typically, predicting soilliquefaction potential involves conducting the standard penetration test (SPT), which requires field testing and canbe time-consuming and labor-intensive. In contrast, the cone penetration test (CPT) provides a more convenientmethod and offers detailed and continuous information about soil layers. In this study, the feature matrix based onCPT data is proposed to predict the standard penetration test blow count N. The featurematrix comprises the CPTcharacteristic parameters at specific depths, such as tip resistance qc, sleeve resistance f s, and depth H. To fuse thefeatures on the matrix, the convolutional neural network (CNN) is employed for feature extraction. Additionally,Genetic Algorithm (GA) is utilized to obtain the best combination of convolutional kernels and the number ofneurons. The study evaluated the robustness of the proposed model using multiple engineering field data sets.Results demonstrated that the proposed model outperformed conventional methods in predicting N values forvarious soil categories, including sandy silt, silty sand, and clayey silt. Finally, the proposed model was employedfor liquefaction discrimination. The liquefaction discrimination based on the predicted N values was comparedwith the measured N values, and the results showed that the discrimination results were in 75% agreement. Thestudy has important practical application value for foundation liquefaction engineering. Also, the novel methodadopted in this research provides new ideas and methods for research in related fields, which is of great academicsignificance.

The Influence of Price Discrimination from Airports on the Route Development Behavior of Airlines

Given the prominence and magnitude of airport incentive schemes,it is surprising that literature hitherto remains silent as to their effectiveness.In this paper,the relationship between airport incentive schemes and the route development behavior of airlines is analyzed.Because of rare and often controversial findings in the extant literature regarding relevant influencing variables for attracting airlines at an airport,expert interviews are used as a complement to formulate testable hypotheses in this regard.A fixed effects regression model is used to test the hypotheses with a dataset that covers all seat capacity offered at the 22 largest German commercial airports in the week 46 from 2004 to 2011.It is found that incentives from primary choice,as well as secondary choice airports,have a significant influence on Low Cost Carriers.Furthermore,Low Cost Carriers,in general,do not leave any of both types of airports when the incentives cease.In the case of Network Carriers,no case is found where one joins a primary choice airport and receives an incentive.Insufficient data between Network Carriers and secondary choice airports in the time when incentives have ceased means that no statement can be given.

A complete discrimination system for polynomials

Given a polynomial with symbolic/literal coefficients,a complete discrimination system is a set of explicit expressions in terms of the coefficients,which is sufficient for determining the numbers and multiplicities of the real and imaginary roots.Though it is of great significance,such a criterion for root-classification has never been given for polynomials with degrees greater than 4.The lack of efficient tools in this aspect extremely prevents computer implementations for Tarski’s and other methods in automated theorem proving.To remedy this defect,a generic algorithm is proposed to produce a complete discrimination system for a polynomial with any degrees.This result has extensive applications in various fields,and its efficiency was demonstrated by computer implementations.

A complete discrimination system for polynomials with complex coefficients and its automatic generation

By establishing a complete discrimination system for polynomials, the problem of complete root classification for polynomials with complex coefficients is utterly solved, furthermore, the algorithm obtained is made into a general program in Maple, which enables the complete discrimination system and complete root classification of a polynomial to be automatically generated by computer, without any human intervention. Besides, by using the automatic generation of root classification, a method to determine the positive definiteness of a polynomial in one or two indeterminates is automatically presented.

Egg recognition and nestling discrimination in the Crested Myna(Acridotheres cristatellus):Size matters

Most studies exploring abilities of hosts to detect brood parasitism are based on detecting colour and/or pattern differences among parasitic and host eggs or nestlings,while only few were focused on size differences.True recognition and recognition by discordancy are used to explain cognitive mechanisms of host egg recognition;however,only a few studies have found that hosts use recognition by discordancy.This study investigated:1)whether egg and nestling recognitions in the Crested Myna(Acridotheres cristatellus) are based on size cues;2)whether the egg cognitive mechanism is recognition by discordancy based on size cues;and 3) whether the longer the experiment time,the higher the egg recognition rate.Our results showed that the Crested Myna uses egg or nestling size as a recognition cue while the egg and nestling colour and patterning are not associated with egg or nestling rejection,thus the cognitive mechanism of egg recognition in the Crested Myna is recognition by discordancy based on egg size cues.Furthermore,there is a rejection delay in time of egg rejection behaviour of the Crested Myna.Therefore,we suggest that the periodicity of egg rejection experiments could be appropriately extended,especially for species with relatively low egg recognition ability.

Remote interactions on two distributed quantum systems:nonlocal unambiguous quantum-state discrimination

Remote quantum-state discrimination is a critical step for the implementation of quantum communication network and distributed quantum computation. We present a protocol for remotely implementing the unambiguous discrimination between nonorthogonal states using quantum entanglements, local operations, and classical communications. This protocol consists of a remote generalized measurement described by a positive operator valued measurement （POVM）. We explicitly construct the required remote POVM. The remote POVM can be realized by performing a nonlocal controlled-rotation operation on two spatially separated qubits, one is an ancillary qubit and the other is the qubit which is encoded by two nonorthogonal states to be distinguished, and a conventional local Von Neumann orthogonal measurement on the ancilla. The particular pair of states that can be remotely and unambiguously distinguished is specified by the state of the ancilla. The probability of successful discrimination is not optimal for all admissible pairs. However, for some subset it can be very close to an optimal value in an ordinary local POVM.

The expert system of genotype discrimination for D5S818 locus based on near-infrared spectroscopy-principal discriminant variate

This paper studied the expert system of genotype discrimination for the STR locus D5S818 based on near-infrared spectroscopy-principal discriminant variate (PDV).Six genotypes,i.e.genotypes 10-10,10-11,11-11,11-12,11-13 and 13-13,were selected as research subjects.Based on the optimum polymerase chain reaction (PCR) conditions,about 54 measuring samples for each genotype were obtained;these samples were tested by near-infrared spectroscopy directly.With differences between homozygote genotypes and heterozygote ones,and differences of the total number of core repeat units between the six genotypes,two types of genotyping-tree structure were constructed and their respective PDV models were studied using the near-infrared spectra of the samples as recognition variables.Finally,based on the classification ability of these two genotyping-tree structures,an optimum expert system of genotype discrimination was built using the PDV models.The result demonstrated that the built expert system had good discriminability and robustness;without any preprocessing for PCR products,the six genotypes studied could be discriminated rapidly and correctly.It provided a methodological support for establishing an expert system of genotype discrimination for all genotypes of locus D5S818 and other STR loci.

Quantitative Method of Classification and Discrimination of a Porous Carbonate Reservoir Integrating K-means Clustering and Bayesian Theory

Reservoir classification is a key link in reservoir evaluation.However,traditional manual means are inefficient,subjective,and classification standards are not uniform.Therefore,taking the Mishrif Formation of the Western Iraq as an example,a new reservoir classification and discrimination method is established by using the K-means clustering method and the Bayesian discrimination method.These methods are applied to non-cored wells to calculate the discrimination accuracy of the reservoir type,and thus the main reasons for low accuracy of reservoir discrimination are clarified.The results show that the discrimination accuracy of reservoir type based on K-means clustering and Bayesian stepwise discrimination is strongly related to the accuracy of the core data.The discrimination accuracy rate of TypeⅠ,TypeⅡ,and TypeⅤreservoirs is found to be significantly higher than that of TypeⅢand TypeⅣreservoirs using the method of combining K-means clustering and Bayesian theory based on logging data.Although the recognition accuracy of the new methodology for the TypeⅣreservoir is low,with average accuracy the new method has reached more than 82%in the entire study area,which lays a good foundation for rapid and accurate discrimination of reservoir types and the fine evaluation of a reservoir.

Performance of real‑time neutron/gamma discrimination methods

Nuclear security usually requires the simultaneous detection of neutrons and gamma rays.With the development of crystalline materials in recent years,Cs2LiLaBr6(CLLB)dual-readout detectors have attracted extensive attention from researchers,where real-time neutron/gamma pulse discrimination is the critical factor among detector performance parameters.This study investigated the discrimination performance of the charge comparison,amplitude comparison,time comparison,and pulse gradient_(m)ethods and the effects of a Sallen–Key filter on their performance.Experimental results show that the figure of merit(FOM)of all four methods is improved by proper filtering.Among them,the charge comparison method exhibits excellent noise resistance;moreover,it is the most_(s)uitable method of real-time discrimination for CLLB detectors.However,its discrimination performance depends on the parameters t_(s),t_(m),and t_(e).When t_(s)corresponds to the moment at which the pulse is at 10%of its peak value,t_(e)requires a delay of only 640–740 ns compared to t_(s),at which time the potentially optimal FOM of the charge comparison method at 3.1–3.3 MeV is greater than 1.46.The FOM obtained using the t_(m)value calculated by a proposed maximized discrimination difference model(MDDM)and the potentially optimal FOM differ by less than 3.9%,indicating that the model can provide good guidance for parameter selection in the charge comparison method.

Development of a high-speed digital pulse signal acquisition and processing system based on MTCA for liquid scintillator neutron detector on EAST

In this experimental study, involving deuterium–deuterium fusion neutron emission spectroscopy measurement on the experimental advanced superconducting tokamak(EAST), a liquid scintillator detector(BC501 A) was employed. This decision was based on the detector's superior sensitivity, optimal time-response, and its exceptional n–γ discrimination capability. This detector emits fast pulse signals that are as narrow as 100 ns, with high count rates that can peak at several Mcps. However, conventional nuclear circuits faced challenges in performing pulse height analysis, n–γ pulse shape discrimination, and in recording the entire pulse waveform under such high count rate conditions. To address these challenges, a high-speed digital pulse signal acquisition and processing system was designed. The system was developed around a micro-telecommunications computing architecture. Within this structure, a signal acquisition and processing(SAQP) module communicated through PCI Express links, achieving a bandwidth of up to 1.6 GB/s. To accurately capture the detailed shape of the pulses, four channels of analog-to-digital converters were used, each with a 500-MSPS sampling rate and a 14-bit resolution, ensuring an accuracy that surpassed 11 bits. An n–γ discrimination algorithm, based on the two-gate integral method, was also developed. Implemented within field programmable gate arrays, this algorithm provided a real-time n–γ discrimination spectrum for pulse height analysis. The system underwent rigorous testing in a laboratory setting and during an EAST experiment. The results confirmed that the innovative SAQP system can satisfy the demanding requirements of high-parameter experiments, manage count rates of up to 2 Mcps, execute real-time n–γ discrimination algorithms, and record entire pulse waveforms without any data loss.

Sex effect on growth performance and marker-aided sex discrimination of seedlings of Populus deltoides

Sex has a significant effect on various traits among dioecious plants.In this analysis of the sex effect on the radial growth and wood density of numerous 20-yearold trees of Populus deltoides growing in a common garden,male trees performed better than the females in radial growth,but sex did not significantly affect wood density.Growth rate and wood density were weakly negatively correlated.Sex selection is also critical for controlling seed-hair pollution from P.deltoides plantations.However,because the juvenile period of P.deltoides lasts for years,a reliable technique to determine the sex of juveniles has been needed.Here we developed a marker-aided technique to discriminate the sexes of P.deltoides seedlings.This study provides essential information on target traits and a highly desirable genetic toolkit for accelerate breeding programs for this important tree species.

Discrimination and quantification of scar tissue by Mueller matrix imaging with machine learning

Scarring is one of the biggest areas of unmet need in the long-term success of glaucoma filtration surgery.Quantitative evaluation of the scar tissue and the post-operative structure with micron scale resolution facilitates development of anti-fibrosis techniques.However,the distinguishment of conjunctiva,sclera and the scar tissue in the surgical area still relies on pathologists'experience.Since polarized light imaging is sensitive to anisotropic properties of the media,it is ideal for discrimination of scar in the subconjunctival and episcleral area by characterizing small differences between proportion,organization and the orientation of the fibers.In this paper,we defined the conjunctiva,sclera,and the scar tissue as three target tissues after glaucoma filtration surgery and obtained their polarization characteristics from the tissue sections by a Mueller matrix microscope.Discrimination score based on parameters derived from Mueller matrix and machine learning was calculated and tested as a diagnostic index.As a result,the discrimination score of three target tissues showed significant difference between each other(p<0.001).The visualization of the discrimination results showed significant contrast between target tissues.This study proved that Mueller matrix imaging is effective in ocular scar discrimination and paves the way for its application on other forms of ocular fibrosis as a substitute or supplementary for clinical practice.

Pulse-shaping method for real-time neutron/gamma discrimination at low sampling rates

The Na I:Tl scintillator is an innovative material for dual-gamma-ray and neutron detection with a low ^(6)Li concentration.To achieve real-time n/γ discrimination,a zero-crossing time comparison algorithm based on trapezoidal pulse shaping was developed.The algorithm can operate efficiently at low sampling rates and was implemented on a single-probe portable digital n/γ discriminator based on a field-programmable gate array.The discriminator and Na I:Tl,^(6)Li detector were tested in a neutron-gamma mixed field produced by an ^(241)Am-Be neutron source to evaluate the performance of the algorithm.The figure of merits was measured as 2.88 at a sampling rate of 50 MHz,indicating that the discriminator with its embedded algorithm has a promising n/γ discrimination capability.Efficient discrimination at sampling rates of 40 and 25 MHz demonstrates that the capability of this method is not limited by low sampling rates.

Artificial neural network algorithm for pulse shape discrimination in 2πα and 2πβ particle surface emission rate measurements

To enhance the accuracy of 2πα and 2πβ particle surface emission rate measurements and address the identification issues of nuclides in conventional methods, this study introduces two artificial neural network(ANN) algorithms: back-propagation(BP) and genetic algorithm-based back-propagation(GA-BP). These algorithms classify pulse signals from distinct α and β particles. Their discrimination efficacy is assessed by simulating standard pulse signals and those produced by contaminated sources, mixing α and β particles within the detector. This study initially showcases energy spectrum measurement outcomes, subsequently tests the ANNs on the measurement and validation datasets, and contrasts the pulse shape discrimination efficacy of both algorithms. Experimental findings reveal that the proportional counter's energy resolution is not ideal, thus rendering energy analysis insufficient for distinguishing between 2πα and 2πβ particles. The BP neural network realizes approximately 99% accuracy for 2πα particles and approximately 95% for 2πβ particles, thus surpassing the GA-BP's performance. Additionally, the results suggest enhancing β particle discrimination accuracy by increasing the digital acquisition card's threshold lower limit. This study offers an advanced solution for the 2πα and 2πβ surface emission rate measurement method, presenting superior adaptability and scalability over conventional techniques.

Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business envir-onments.However,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective information.Second,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass differences.These challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective information.To solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images.Moreover,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature images.In addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of strokes.We respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification methods.This is more helpful to the task of signature authentication in justice and finance.

Geometric discord of tripartite quantum systems

We study the quantification of geometric discord for tripartite quantum systems.Firstly,we obtain the analytic formula of geometric discord for tripartite pure states.It is already known that the geometric discord of pure states reduces to the geometric entanglement in bipartite systems,the results presented here show that this property is no longer true in tripartite systems.Furthermore,we provide an operational meaning for tripartite geometric discord by linking it to quantum state discrimination,that is,we prove that the geometric discord of tripartite states is equal to the minimum error probability to discriminate a set of quantum states with von Neumann measurement.Lastly,we calculate the geometric discord of three-qubit Bell diagonal states and then investigate the dynamic behavior of tripartite geometric discord under local decoherence.It is interesting that the frozen phenomenon exists for geometric discord in this scenario.

Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems

The amalgamation of artificial intelligence(AI)with various areas has been in the picture for the past few years.AI has enhanced the functioning of several services,such as accomplishing better budgets,automating multiple tasks,and data-driven decision-making.Conducting hassle-free polling has been one of them.However,at the onset of the coronavirus in 2020,almost all worldly affairs occurred online,and many sectors switched to digital mode.This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business.This paper proposes a three-layered deep learning(DL)-based authentication framework to develop a secure online polling system.It provides a novel way to overcome security breaches during the face identity(ID)recognition and verification process for online polling systems.This verification is done by training a pixel-2-pixel Pix2pix generative adversarial network(GAN)for face image reconstruction to remove facial objects present(if any).Furthermore,image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome,thus checking the electorate credentials.