Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders.Implementing Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as possible.The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate.Three models with a cross-validation approach do the required task.Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.For Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision measurements.The models are evaluated using the metrics and kappa statistics that illustrate the three models’comparability.The study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification.Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.

Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification

A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)in the smear images of human peripheral blood.Some existing approaches fail to predict the malaria parasitic features and reduce the prediction accuracy.The trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online dataset.The Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network(DNN)with the help of the contrastive divergence method to pre-train.The features are extracted from the images in the proposed system to train the DNN for initializing the visible variables.The smear images show the concatenated feature to be utilized as the feature vector in the proposed system.Lastly,hyper-parameters are used to fine-tune DNN to calculate the class labels’probability.The suggested system outperforms more modern methodologies with an accuracy of 91%,precision of 89%,recall of 93%and F1-score of 91%.The HPTDL-MPDC has the primary application in detecting the parasite of malaria in the smear images of human peripheral blood.

Application of artificial neural networks in optimal tuning of tuned mass dampers implemented in high-rise buildings subjected to wind load

High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper（TMD） can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom（MDF） systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.

Hyper-parameter optimization of multiple machine learning algorithms for molecular property prediction using hyperopt library

Due to outstanding performance in cheminformatics,machine learning algorithms have been increasingly used to mine molecular properties and biomedical big data.The performance of machine learning models is known to critically depend on the selection of the hyper-parameter configuration.However,many studies either explored the optimal hyper-parameters per the grid searching method or employed arbitrarily selected hyper-parameters,which can easily lead to achieving a suboptimal hyper-parameter configuration.In this study,Hyperopt library embedding with the Bayesian optimization is employed to find optimal hyper-parameters for different machine learning algorithms.Six drug discovery datasets,including solubility,probe-likeness,h ERG,Chagas disease,tuberculosis,and malaria,are used to compare different machine learning algorithms with ECFP6 fingerprints.This contribution aims to evaluate whether the Bernoulli Na?ve Bayes,logistic linear regression,Ada Boost decision tree,random forest,support vector machine,and deep neural networks algorithms with optimized hyper-parameters can offer any improvement in testing as compared with the referenced models assessed by an array of metrics including AUC,F1-score,Cohen’s kappa,Matthews correlation coefficient,recall,precision,and accuracy.Based on the rank normalized score approach,the Hyperopt models achieve better or comparable performance on 33 out 36 models for different drug discovery datasets,showing significant improvement achieved by employing the Hyperopt library.The open-source code of all the 6 machine learning frameworks employed in the Hyperopt python package is provided to make this approach accessible to more scientists,who are not familiar with writing code.

A highly sensitive LITES sensor based on a multi-pass cell with dense spot pattern and a novel quartz tuning fork with low frequency

A highly sensitive light-induced thermoelectric spectroscopy(LITES)sensor based on a multi-pass cell(MPC)with dense spot pattern and a novel quartz tuning fork(QTF)with low resonance frequency is reported in this manuscript.An erbi-um-doped fiber amplifier(EDFA)was employed to amplify the output optical power so that the signal level was further enhanced.The optical path length(OPL)and the ratio of optical path length to volume(RLV)of the MPC is 37.7 m and 13.8 cm^(-2),respectively.A commercial QTF and a self-designed trapezoidal-tip QTF with low frequency of 9461.83 Hz were used as the detectors of the sensor,respectively.The target gas selected to test the performance of the system was acetylene(C2H2).When the optical power was constant at 1000 mW,the minimum detection limit(MDL)of the C2H2-LITES sensor can be achieved 48.3 ppb when using the commercial QTF and 24.6 ppb when using the trapezoid-al-tip QTF.An improvement of the detection performance by a factor of 1.96 was achieved after replacing the commer-cial QTF with the trapezoidal-tip QTF.

Morphological disruption and visual tuning alterations in the primary visual cortex in glaucoma(DBA/2J)mice

Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the brain.However,it is not yet clear how the primary visual cortex(V1)is altered in glaucoma.This study used DBA/2J mice as a model for spontaneous secondary glaucoma.The aim of the study was to compare the electrophysiological and histomorphological chara cteristics of neurons in the V1between 9-month-old DBA/2J mice and age-matched C57BL/6J mice.We conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses,including single-unit spiking and gamma band oscillations.The morphology of layerⅡ/Ⅲneurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue sections.Eighty-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were examined.Compared with the C57BL/6J group,V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial summation.Moreove r,fewer neuro ns were observed in the V1 of DBA/2J mice compared with C57BL/6J mice.These findings suggest that DBA/2J mice have fewer neurons in the VI compared with C57BL/6J mice,and that these neurons have impaired visual tuning.Our findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse model.This study might offer some innovative perspectives regarding the treatment of glaucoma.

Manipulation of High-Fidelity Sidebands under Large Detuning by Floquet Technology:Application to Multi-Mode Cooling

The Floquet technology,a powerful way to manipulate quantum states,is employed to drive sidebands transition under large detuning.Our results demonstrate that high fidelities over 99%can be achieved through optimizing suitable modulation frequencies under large detuning.We observe high-fidelity transitions within a high bandwidth by utilizing a single modulation frequency and reveal that this capability is due to the emergence of a flat-band structure in the bandwidth range.The key finding of high-fidelity sideband manipulation under large detuning is experimentally confirmed in nuclear magnetic resonance platform.Finally,we propose a new parallel sideband cooling scheme that enables simultaneous cooling of multiple motional modes.This approach improves the cooling rate compared to conventional schemes with fixed laser frequency and power,and eliminates the need for mode-specific addressing.Our Floquet parallel scheme is applicable to any harmonic oscillator system and is not limited by bandwidth in theory.

Conceptual design of a 714-MHz RFQ for compact proton injectors and development of a new tuning algorithm on its aluminium prototype

Radio frequency quadrupoles(RFQs),which are crucial components of proton injectors,significantly affect the performance of proton accelerator facilities.An RFQ with a high frequency of 714 MHz dedicated to compact proton injectors for medi-cal applications is designed in this study.The RFQ is designed to accelerate proton beams from 50 keV to 4 MeV within a short length of 2 m and can be matched closely with the downstream drift tube linac to capture more particles through a preliminary optimization.To develop an advanced RFQ,challenging techniques,including fabrication and tuning method,must be evaluated and verified using a prototype.An aluminium prototype is derived from the conceptual design of the RFQ and then redesigned to confirm the radio frequency performance,fabrication procedure,and feasibility of the tuning algorithm.Eventually,a new tuning algorithm based on the response matrix and least-squares method is developed,which yields favorable results based on the prototype,i.e.,the errors of the dipole and quadrupole components reduced to a low level after several tuning iterations.Benefiting from the conceptual design and techniques obtained from the prototype,the formal mechanical design of the 2-m RFQ is ready for the next manufacturing step.

基于prompt tuning的中文文本多领域情感分析研究

不同领域的情感文本表达方式不一样,通常需要为各个领域训练相应的情感分析模型。针对无法用一个模型进行高效多领域情感分析的问题,提出了基于提示微调(prompt tuning)的多领域文本情感分析方法MSAPT。借助hard prompt,指示情感文本的所属领域和待选的情感标签,调动不同领域情感分析相关的知识,再为情感分析预训练一个统一的“通才模型”,在下游的各领域文本学习中,保持模型冻结,通过prompt tuning使模型学习到下游各领域情感文本的特征。MSAPT仅需保存一个模型和一些参数量远远小于模型的prompt,实现了多领域情感分析。在多个属于不同领域的情感文本数据集上进行实验,结果表明仅进行prompt tuning时,MSAPT效果优于模型微调(model tuning)的。最后,分别对适应特定领域的prompt tuning、hard prompt、soft prompt的长度和中间训练数据集的大小进行消融实验,从证明其对情感分析效果的影响。

Improved isochronous mass spectrometry with tune measurement

In conventional isochronous mass spectrometry(IMS)performed on a storage ring,the precision of mass measurements for short-lived nuclei depends on the accurate determination of the revolution times(T)of stored ions.However,the resolution of T inevitably deteriorates due to the magnetic rigidity spread of the ions,limiting the mass-resolving power.In this study,we used the betatron tunes Q(the number of betatron oscillations per revolution)of the ions and established a correlation between T and Q.From this correlation,T was transformed to correspond to a fixed Q with higher resolution.Using these transformed T values,the masses of ^(63)Ge,^(65)As,^(67)Se,and ^(71)Kr agreed well with the mass values measured using the newly developed IMS(Bρ-IMS).We also studied the systematics of Coulomb displacement energies(CDEs)and found that anomalous staggering in CDEs was eliminated using new mass values.This method of T transformation is highly effective for conventional IMS equipped with a single time-of-flight detector.

Vibration attenuation performance of wind turbine tower using a prestressed tuned mass damper under seismic excitation

With the rapid development of large megawatt wind turbines,the operation environment of wind turbine towers(WTTs)has become increasingly complex.In particular,seismic excitation can create a resonance response and cause excessive vibration of the WTT.To investigate the vibration attenuation performance of the WTT under seismic excitations,a novel passive vibration control device,called a prestressed tuned mass damper(PS-TMD),is presented in this study.First,a mathematical model is established based on structural dynamics under seismic excitation.Then,the mathematical analytical expression of the dynamic coefficient is deduced,and the parameter design method is obtained by system tuning optimization.Next,based on a theoretical analysis and parameter design,the numerical results showed that the PS-TMD was able to effectively mitigate the resonance under the harmonic basal acceleration.Finally,the time-history analysis method is used to verify the effectiveness of the traditional pendulum tuned mass damper(PTMD)and the novel PS-TMD device,and the results indicate that the vibration attenuation performance of the PS-TMD is better than the PTMD.In addition,the PS-TMD avoids the nonlinear effect due to the large oscillation angle,and has the potential to dissipate hysteretic energy under seismic excitation.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

Parameters Optimization and Performance Evaluation of the Tuned Inerter Damper for the Seismic Protection of Adjacent Building Structures

In order to improve the seismic performance of adjacent buildings,two types of tuned inerter damper(TID)damping systems for adjacent buildings are proposed,which are composed of springs,inerter devices and dampers in serial or in parallel.The dynamic equations of TID adjacent building damping systems were derived,and the H2 norm criterion was used to optimize and adjust them,so that the system had the optimum damping performance under white noise random excitation.Taking TID frequency ratio and damping ratio as optimization parameters,the optimum analytical solutions of the displacement frequency response of the undamped structure under white noise excitation were obtained.The results showed that compared with the classic TMD,TID could obtain a better damping effect in the adjacent buildings.Comparing the TIDs composed of serial or parallel,it was found that the parallel TIDs had more significant advantages in controlling the peak displacement frequency response,while the H2 norm of the displacement frequency response of the damping system under the coupling of serial TID was smaller.Taking the adjacent building composed of two ten-story frame structures as an example,the displacement and energy collection time history analysis of the adjacent building coupled with the optimum design parameter TIDs were carried out.It was found that TID had a better damping effect in the full-time range compared with the classic TMD.This paper also studied the potential power of TID in adjacent buildings,which can be converted into available power resources during earthquakes.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Optimizing Enterprise Conversational AI: Accelerating Response Accuracy with Custom Dataset Fine-Tuning

As the realm of enterprise-level conversational AI continues to evolve, it becomes evident that while generalized Large Language Models (LLMs) like GPT-3.5 bring remarkable capabilities, they also bring forth formidable challenges. These models, honed on vast and diverse datasets, have undoubtedly pushed the boundaries of natural language understanding and generation. However, they often stumble when faced with the intricate demands of nuanced enterprise applications. This research advocates for a strategic paradigm shift, urging enterprises to embrace a fine-tuning approach as a means to optimize conversational AI. While generalized LLMs are linguistic marvels, their inability to cater to the specific needs of businesses across various industries poses a critical challenge. This strategic shift involves empowering enterprises to seamlessly integrate their own datasets into LLMs, a process that extends beyond linguistic enhancement. The core concept of this approach centers on customization, enabling businesses to fine-tune the AI’s functionality to fit precisely within their unique business landscapes. By immersing the LLM in industry-specific documents, customer interaction records, internal reports, and regulatory guidelines, the AI transcends its generic capabilities to become a sophisticated conversational partner aligned with the intricacies of the enterprise’s domain. The transformative potential of this fine-tuning approach cannot be overstated. It enables a transition from a universal AI solution to a highly customizable tool. The AI evolves from being a linguistic powerhouse to a contextually aware, industry-savvy assistant. As a result, it not only responds with linguistic accuracy but also with depth, relevance, and resonance, significantly elevating user experiences and operational efficiency. In the subsequent sections, this paper delves into the intricacies of fine-tuning, exploring the multifaceted challenges and abundant opportunities it presents. It addresses the technical intricacies of data integration, ethical considerations surrounding data usage, and the broader implications for the future of enterprise AI. The journey embarked upon in this research holds the potential to redefine the role of conversational AI in enterprises, ushering in an era where AI becomes a dynamic, deeply relevant, and highly effective tool, empowering businesses to excel in an ever-evolving digital landscape.

产生“Tuned”模板的Bayesian Networks方法

介绍了Bayesian Networks(简称BNs)产生“Tuned”模板新方法的基本原理以及BNs法与蚁群行为仿真技术和单纯形法组合的方法。通过实际航空影像的实验结果表明,新方法对纹理影像的识别率是令人满意的,同时还将新方法与遗传算法的结果作了对比,结果表明新方法是很有应用前景的。

Effect of Weak Noise on the Frequency Tuning of Mouse Inferior Collicular Neurons

In order to study the effect of weak noise on the sound signal extraction of mouse (Mus musculus Km) inferior collicular (IC) neurons from environments,we examined the changes in frequency tuning curves (FTCs) of 32 neurons induced by a weak noise relative to 5 dB below minimum threshold of tone (reMT-5 dB) under free field stimulation conditions.The results were as follows:① There were three types of variations in FTCs,sharpened (34.4%),broadened (18.8%),and unaffected (46.9%),nevertheless,only the alteration of sharpened FTCs was statistically different.② Sharpness of frequency tuning induced by a reMT-5 dB noise was very strong.Q 10 and Q 30 of FTCs were increased by (34.42±17.04)% (P=0.026,n=11) and (46.34±22.88)% (P=0.009,n=7).③ The changes of inverse-slopes (ISs,kHz/dB) between high (IS high) and low (IS low) limbs of FTCs were dissymmetry.The IS high of FTCs decreased markedly (P=0.046,n=7),however,there was little change (P=0.947,n=7) in IS low.Our data revealed for the first time that the weak noise could sharpen frequency tuning and increase the sensitivity on the high frequency of sound signal in IC neurons of mouse.

Tune调制对HIRFL-CSRm动力学孔径影响的模拟研究

通过引进包含tune调制的传输矩阵,模拟计算了由四极铁电源纹波所引起的tune调制对HIRFL-CSRm动力学孔径的影响.模拟计算中,对HIRFL-CSRm实际(lattice)跟踪1.0×106圈.从结果可以看出,动力学孔径随调制振幅的增大迅速减小,大体呈线性变化;动力学孔径随调制tune值的变化在研究范围内也有变化,变化的范围在0.049～0.089 m之间.

Finite Element Simulation of the Vibratory Characteristics for Quartz Tuning Fork Gyroscope

The micro quartz crystal tuning fork gyroscope is a new type of vibratory gyroscope. The gyroscope should be analyzed and simulated early in the design stage in order to offer reliable basis for design and to shorten the period of development. Thus the vibratory characteristics of the gyroscope is simulated with the finite element method of coupled field. The optimum exciting frequency and the factors which influence the gyroscope sensitivity are determined. The method for adjusting the frequency deviation between driving and detecting modes is also proposed.

A 2GHz Low Power Differentially Tuned CMOS Monolithic LC-VCO

A 2GHz differentially tuned CMOS monolithic LC-VCO is designed and fabricated in a 0.18μm CMOS process. The VCO has a 16.15% tuning range （from 1. 8998 to 2. 2335GHz） through a combination of analog and digital tuning techniques （4-bit binary switch-capacitor array）. The measured phase noise is - 118.17dBc/Hz at a 1MHz offset from a 2. 158GHz carrier. With the presented improved switch,the phase noise varies no more than 3dB at different digital control bits. The phase noise changes only by about 2dB in the tuning range because of the pn-junctions as the varactors. The VCO draws a current of about 2. lmA from a 1.8V power supply and works normally with a 1.5V power supply.