Hasil untuk "Instruments and machines"

NI Yuheng(倪宇恒), FU Xiaoming(傅孝明)

A novel method for estimating bounds of the maximum isometric distortion between two hexahedral elements is proposed in this paper. Given two hexahedral elements with geometric injections, the proposed approach applies Lipschitz continuity analysis combined with extremum estimation of polynomials. By decomposing the Jacobian matrices of the composite mapping into a combination of Bézier rational polynomials, we establish sufficiency criteria for extrema attainment at parametric domain corners, and construct tight upper and lower bounds for mapping distortion by integrating. Lipschitz bounds of derivatives. Through rigorous testing on extensive datasets, we demonstrate the effectiveness and reliability of our algorithm.提出了一种可估计两个六面体单元之间映射的最大等距扭曲上下界的方法。给定具有几何单射的两个六面体单元，基于Lipschitz连续性分析与多项式最值估计，通过将复合映射的雅可比矩阵分解为Bézier有理多项式的组合形式，建立了参数域在角点处取最值的充分性判据，并结合导数的Lipschitz界构建了映射扭曲的紧致上下界。通过在大量数据集上的严格测试，证明了本文算法的有效性和可靠性。

Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis

Policy-Relevant Treatment Effects (PRTEs) are generally not point-identified under standard instrumental variable (IV) assumptions when the instrument generates limited support in treatment propensity. Existing approaches typically optimize over marginal treatment response functions subject to moment restrictions and can discard identifying distributional information. We show that PRTE partial identification in the generalized Roy model can instead be formulated as a Constrained Conditional Optimal Transport (CCOT) problem. The resulting multidimensional CCOT problem reduces analytically to separable one-dimensional OT problems with product costs, yielding sharp closed-form bounds and avoiding direct solution of the original high-dimensional CCOT problem. We also develop estimation and inference procedures for these bounds: for discrete instruments, a Double Machine Learning (DML) approach based on Neyman-orthogonal scores that accommodates high-dimensional covariates while achieving the parametric $\sqrt{n}$ rate and asymptotic normality; for continuous instruments, we explicitly characterize the corresponding nonparametric convergence rates. The framework accommodates covariates, discrete and continuous instruments, and extensions to general treatment settings. In simulations and a bed-net subsidy application, the resulting bounds are substantially tighter than existing moment-relaxation methods.

Rasha A. Moyassar, Mohammed A. M. Abdullah

Electromyography (EMG) classification using machine learning techniques has gained significant attention in recent years due to its applications in various aspects such as prosthetic control, gesture recognition and muscle health monitoring. In this study, we explore the applications of both Machine Learning and Deep Learning techniques for the classification of EMG signals. In this work, a new CNN and DFNN models is proposed for this purpose achieving high accuracy with low computation time. Additionally, several machine learning algorithms are evaluated for EMG classification, including Random Forest, KNN, AdaBoost, Decision Tree with and without cross-validation were employed. Moreover, we investigate the impact of class balance on the performance of these models. Model selection and hyperparameter tuning are conducted to optimize the performance. The models are assessed based on accuracy, precision, recall and [Formula: see text]-score. The best results are obtained using random forest with an accuracy of 99.81% while the proposed CNN model achieved an accuracy of 99.61%. Experimental results proved the efficiency of the proposed work compared to other state-of-the-art works.

Feudjio Ghislain, Saha Tchinda Beaudelaire, Romain Atangana et al.

Context: Early detection of ophthalmic diseases, such as drusen and glaucoma, can be facilitated by analyzing changes in the retinal microvascular structure. The implementation of algorithms based on convolutional neural networks (CNNs) has seen significant growth in the automation of disease identification. However, the complexity of these algorithms increases with the diversity of pathologies to be classified. In this study, we introduce a new lightweight algorithm based on CNNs for the classification of multiple categories of eye diseases, using discrete wavelet transforms to enhance feature extraction. Methods: The proposed approach integrates a simple CNN architecture optimized for multi-class and multi-label classification, with an emphasis on maintaining a compact model size. We improved the feature extraction phase by implementing multi-scale decomposition techniques, such as biorthogonal wavelet transforms, allowing us to capture both fine and coarse features. The developed model was evaluated using a dataset of retinal images categorized into four classes, including a composite class for less common pathologies. Results: The feature extraction based on biorthogonal wavelets enabled our model to achieve perfect values of precision, recall, and F1-score for half of the targeted classes. The overall average accuracy of the model reached 0.9621. Conclusion: The integration of biorthogonal wavelet transforms into our CNN model has proven effective, surpassing the performance of several similar algorithms reported in the literature. This advancement not only enhances the accuracy of real-time diagnoses but also supports the development of sophisticated tools for the detection of a wide range of retinal pathologies, thereby improving clinical decision-making processes.

David J. Gunkel

Ashish Patel, James Lane, Stephen Burgess

The method of multivariable Mendelian randomization uses genetic variants to instrument multiple exposures, to estimate the effect that a given exposure has on an outcome conditional on all other exposures included in a linear model. Unfortunately, the inclusion of every additional exposure makes a weak instruments problem more likely, because we require conditionally strong genetic predictors of each exposure. This issue is well appreciated in practice, with different versions of F-statistics routinely reported as measures of instument strength. Less transparently, however, these F-statistics are sometimes used to guide instrument selection, and even to decide whether to report empirical results. Rather than discarding findings with low F-statistics, weak instrument-robust methods can provide valid inference under weak instruments. For multivariable Mendelian randomization with two-sample summary data, we encourage use of the inference strategy of Andrews (2018) that reports both robust and non-robust confidence sets, along with a statistic that measures how reliable the non-robust confidence set is in terms of coverage. We also propose a novel adjusted-Kleibergen statistic that corrects for overdispersion heterogeneity in genetic associations with the outcome.

Lorella Viola

This article offers a conceptual and methodological contribution to linguistics by exploring the potential value of using sentiment analysis (SA) for research in this field. Firstly, it discusses the limitations and advantages of using SA for linguistics research including the wider epistemological implications of its application outside of its original conception as a product reviews analysis tool. Methodologically, it tests its applicability against an established linguistic case: the correlation between subjective attitudes such as surprise, irritation and discontent and the use of the progressive. The language example is Italian for which this function of the progressive form has not been analyzed yet. The analysis applies FEEL-IT, a state-of-the-art transformer-based machine learning model for emotion and sentiment classification in Italian on language samples from various sources as collected in Evalita-2014 (238,556 words). The results show statistically significant correlations between negative subjective attitudes and the use of the progressive in line with previous accounts in other languages. The article concludes with a few additional propositions for practitioners and researchers using SA.

Shiuh Tong Lim, Joe Yee Yuan, Khai Wah Khaw et al.

Travel insurance serves as a crucial financial safeguard, offering coverage against unforeseen expenses and losses incurred during travel. With the advent of the proliferation of insurance types and the amplified demand for Covid-related coverage, insurance companies face the imperative task of accurately predicting customers’ likelihood to purchase insurance. This can assist the insurance providers in focusing on the most lucrative clients and boosting sales. By employing advanced machine learning techniques, this study aims to forecast the consumer segments most inclined to acquire travel insurance, allowing targeted strategies to be developed. A comprehensive analysis was carried out on a Kaggle dataset comprising prior clients of a travel insurance firm utilizing the K-Nearest Neighbors (KNN), Decision Tree Classifier (DT), Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF) models. Extensive data cleaning was done before model building. Performance evaluation was then based on accuracy, F1 score, and the Area Under Curve (AUC) with Receiver Operating Characteristics (ROC) curve. Inexplicably, KNN outperformed other models, achieving an accuracy of 0.81, precision of 0.82, recall of 0.82, F1 score of 0.80, and an AUC of 0.78. The findings of this study are a valuable guide for deploying machine learning algorithms in predicting travel insurance purchases, thus empowering insurance companies to target the most lucrative clientele and bolster revenue generation.

Zahra Asghari Varzaneh, Soodeh Hosseini

This paper proposed a fuzzy expert system for diagnosing diabetes. In the proposed method, at first, the fuzzy rules are generated based on the Pima Indians Diabetes Database (PIDD) and then the fuzzy membership functions are tuned using the Harris Hawks optimization (HHO). The experimental data set, PIDD with the age group from 25-30 is initially processed and the crisp values are converted into fuzzy values in the stage of fuzzification. The improved fuzzy expert system increases the classification accuracy which outperforms several famous methods for diabetes disease diagnosis. The HHO algorithm is applied to tune fuzzy membership functions to determine the best range for fuzzy membership functions and increase the accuracy of fuzzy rule classification. The experimental results in terms of accuracy, sensitivity, and specificity prove that the proposed expert system has a higher ability than other data mining models in diagnosing diabetes.

Swati A. Patil, G. Pradeepini, Thirupathi Rao Komati

Abstract Music Genre Classification (MGC) is a crucial undertaking that categorizes Music Genre (MG) based on auditory information. MGC is commonly employed in the retrieval of music information. The three main stages of the proposed system are data readiness, feature mining, and categorization. To categorize MG, a new neural network was deployed. The proposed system uses features from spectrographs derived from short clips of songs as inputs to a projected scheme building to categorize songs into an appropriate MG. Extensive experiment on the GTZAN dataset, Indian Music Genre(IMG) dataset, Hindustan Music Rhythm (HMR) and Tabala Dataset show that the proposed strategy is more effective than existing methods. Indian rhythms were used to test the proposed system design. The proposed system design was compared with other existing algorithms based on time and space complexity.

Filip Milisav, Vincent Bazinet, Yasser Iturria-Medina et al.

LI Cenhao, CUI Pengjie, YUAN Ye, WANG Guoren

Subgraph matching is an important method of data mining in complex networks. In recent years, the subgraph matching algorithm based on GPU (graphics processing units) has shown obvious speed advantages.However, due to the large scale of graph data and a large number of intermediate results of subgraph matching, the memory capacity of a single GPU soon becomes the main bottleneck for processing subgraph matching algorithm of large graph. Therefore, this paper proposes a multi-GPU programming model for large graph subgraph matching. Firstly, the framework of subgraph matching algorithm based on multi-GPU is proposed, and the cooperative operation of subgraph matching algorithm on multi-GPU is realized, which solves the problem of graph scale of subgraph matching on GPU. Secondly, a dynamic adjustment technique based on query graph is used to deal with cross-partition subgraph sets, which solves the cross-partition subgraph matching problem caused by graph segmentation. Finally, based on the characteristics of SIMT (single instruction multiple threads) architecture on GPU, a priority scheduling strategy is proposed to ensure the internal load balancing of GPU, and a pipeline mechanism of shared memory is designed to optimize the cache contention of multi-core concurrency. Experiments show that the proposed multi-GPU programming model can get the correct matching results on billions of datasets. Compared with the latest GPU-based solution, the proposed algorithm framework can achieve 1.2 to 2.6 times of acceleration ratio.

David Ackermann, Fabian Brinkmann, Stefan Weinzierl

We present a database of recordings and radiation patterns of individual notes for 41 modern and historical musical instruments, measured with a 32-channel spherical microphone array in anechoic conditions. In addition, directivities averaged in one-third octave bands have been calculated for each instrument, which are suitable for use in acoustic simulation and auralisation. The data are provided in SOFA format. Spatial upsampling of the directivities was performed based on spherical spline interpolation and converted to OpenDAFF and GLL format for use in room acoustic and electro-acoustic simulation software. For this purpose, a method is presented how these directivities can be referenced to a specific microphone position in order to achieve a physically correct auralisation without colouration. The data is available under the CC BY-SA 4.0 licence.

Sebastian Kreutzer, Christian Iwainsky, Marta Garcia-Gasulla et al.

Automated code instrumentation, i.e. the insertion of measurement hooks into a target application by the compiler, is an established technique for collecting reliable, fine-grained performance data. The set of functions to instrument has to be selected with care, as instrumenting every available function typically yields too large a runtime overhead, thus skewing the measurement. No "one-suits-all" selection mechanism exists, since the instrumentation decision is dependent on the measurement objective, the limit for tolerable runtime overhead and peculiarities of the target application. The Compiler-assisted Performance Instrumentation (CaPI) tool assists in creating such instrumentation configurations, by enabling the user to combine different selection mechanisms as part of a configurable selection pipeline, operating on a statically constructed whole-program call-graph. Previously, CaPI relied on a static instrumentation workflow which made the process of refining the initial selection quite cumbersome for large-scale codes, as the application had to be recompiled after each adjustment. In this work, we present new runtime-adaptable instrumentation capabilities for CaPI which do not require recompilation when instrumentation changes are made. To this end, the XRay instrumentation feature of the LLVM compiler was extended to support the instrumentation of shared dynamic objects. An XRay-compatible runtime system was added to CaPI that instruments selected functions at program start, thereby significantly reducing the required time for selection refinements. Furthermore, an interface to the TALP tool for recording parallel efficiency metrics was implemented, alongside a specialized selection module for creating suitable coarse-grained region instrumentations.

Raj Krishan, F. Paqué, A. Ossareh et al.

L. Heitz‐Mayfield, N. Lang

E. Orenstein, Devin Ratelle, C. Briseño‐Avena et al.

The large data sets provided by in situ optical microscopes are allowing us to answer longstanding questions about the dynamics of planktonic ecosystems. To deal with the influx of information, while facilitating ecological insights, the design of these instruments increasingly must consider the data: storage standards, human annotation, and automated classification. In that context, we detail the design of the Scripps Plankton Camera (SPC) system, an in situ microscopic imaging system. Broadly speaking, the SPC consists of three units: (1) an underwater, free‐space, dark‐field imaging microscope; (2) a server‐based management system for data storage and analysis; and (3) a web‐based user interface for real‐time data browsing and annotation. Combined, these components facilitate observations and insights into the diverse planktonic ecosystem. Here, we detail the basic design of the SPC and briefly present several preliminary, machine‐learning‐enabled studies illustrating its utility and efficacy.

Sanjay Goswami, Kshama D. Dhobale, Ravindra D. Wavhale et al.

Abstract The field of cancer nanomedicine has made significant progress, but its clinical translation is impeded by many challenges, such as the difficulty in analyzing intracellular anticancer drug release by the nanocarriers due to the lack of suitable tools. Here, we propose the development of an image‐based strategy involving machine learning (ML) to evaluate anticancer drug such as doxorubicin hydrochloride (DOX) released by a nanocarrier inside the HCT116 colon cancer cells and its subsequent intracellular accumulation. This technique combines fluorescent cell imaging with ML‐based image analysis to assess and quantify the delivery of DOX by nanoparticles within them. We show that DOX in HCT116 cells was higher for multifunctional CNT‐DOX‐Fe3O4 nanocarrier than free DOX, indicating efficient and steady release of DOX as well as superior retentive property of the nanocarrier. Initially (1 and 4 hours), the luminance intensity of DOX in the cell cytoplasm delivered by CNT‐DOX‐Fe3O4 nanocarrier was ~0.34 and ~0.42 times lesser than that of free DOX delivered normally. However, at 24 and 48 hours posttreatment, the luminance intensity of DOX for CNT‐DOX‐Fe3O4 nanocarrier was ~1.98 and ~1.92 times higher than that of free DOX. Furthermore, the luminance intensity of DOX for CNT‐DOX‐Fe3O4 in the whole cell was ~1.35 and ~1.62 times higher than that of free DOX at 24 and 48 hours, respectively. The high‐throughput nature of our image analysis workflow allowed us to automate the process of DOX retention analysis and enabled us to devise ML‐based modeling to predict the percentage of anticancer drug retention in cells. The development of models to automatically quantify and predict intracellular drug release in cancer cells could benefit personalized treatments by optimizing the design of nanocarriers.

Paolo Pegolo, Stefano Baroni, Federico Grasselli

Abstract Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic conductors and largely overestimate their thermal conductivity. In this work, we deploy a combination of state-of-the-art methods to calculate the thermal conductivity of a prototypical Li-ion conductor, the Li3ClO antiperovskite. By leveraging ab initio, machine learning, and force-field descriptions of interatomic forces, we are able to reveal the massive role of anharmonic interactions and diffusive defects on the thermal conductivity and its temperature dependence, and to eventually embed their effects into a simple rationale which is likely applicable to a wide class of ionic conductors.

nyoman purnama

Recommendations are a very useful tool in many industries. Recommendations provide the best selection of what the user wants and provide satisfaction compared to ordinary searches. In the music industry, recommendations are used to provide songs that have similarities in terms of genre or theme. There are various kinds of genres in the world of music, including pop, classic, reggae and others. With genre, the difference between one song and another can be heard clearly. This genre can be analyzed by spectrogram analysis. In this study, a spectrogram analysis was developed which will be the input feature for the Convolutional Neural Network. CNN will classify and provide song recommendations according to what the user wants. In addition, testing was carried out with two different architectures from CCN, namely VGG-16 and RESNET-50. From the results of the study obtained, the best accuracy results were obtained by the VGG-16 model with 20 epochs with accuracy 60%, compared to the RESNET-50 model with more than 20 epochs. The results of the recommendations generated on the test data obtained a good similarity value for VGG-16 compared to RESNET-50.