The spider structure in the photoelectron momentum distributions(PMDs)of ionized electrons from the hydrogen atom is simulated by solving the time-dependent Schrodinger equation(TDSE).We find that the spider structure...
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The spider structure in the photoelectron momentum distributions(PMDs)of ionized electrons from the hydrogen atom is simulated by solving the time-dependent Schrodinger equation(TDSE).We find that the spider structure exhibits sensitive dependence on carrier envelope phase(CEP)of the few-cycle *** elucidate the striking CEP dependence of the spider structure,we select three physical parameters IL,IR,and IR/IL to quantitatively characterize the variations of the spider structure induced by altering the *** is the sum of the left half panel of the transverse cut curves(i.e.,the sum of all the negative momenta along the laser polarization direction),IR is the sum of the right half panel of the transverse cut curves(i.e.,the sum of all the positive momenta along the laser polarization direction),and IR/IL is the ratio between the two *** parameters are shown to have monotonic relation with the CEP value,which is exploited to extract the *** anticipate that our method will be useful for obtaining CEPs encoded in the spider structure of PMDs.
Asymptotic Notations have been very effective in representing the efficiencies of algorithms by analyzing the number of steps and the nature of traversals and operations that the algorithm is bound to perform. Though ...
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ISBN:
(纸本)9781665426923
Asymptotic Notations have been very effective in representing the efficiencies of algorithms by analyzing the number of steps and the nature of traversals and operations that the algorithm is bound to perform. Though they represent the nature of growth of the time or space consumed by the algorithms, they only consider the fastest-growing term and not the other terms. The result from this kind of analysis often seems to be fruitful in grading the algorithms based on their efficiency, but theoretically, there seem to be evident deviations when these complexities are extrapolated to larger datasets, hypothetically ‘infinitely large’ datasets. This deviation is tracked down when the constant and slow-growing terms involved are considered. The practical difficulty of empirically verifying this fact due to the ambiguity in defining a very large number of inputs is overcome by theoretically calculating the ratio between the time complexities of algorithms of interest and applying their limit to be infinity. In this study, the algorithms heapsort and merge sort are analyzed and the drastic deviation between them is shown mathematically though they both have the complexity of $n(log(n))$ , concluding that the merge sort is actually twice as fast as heap sort when approaching infinitely large data elements.
Distributed optimization is an important direction of research in modern optimization theory. Its applications include large scale machine learning, distributed signal processing and many others. The paper studies dec...
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Face representation learning using datasets with a massive number of identities requires appropriate training methods Softmax-based approach, currently the state-of-the-art in face recognition, in its usual "full...
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Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comp...
Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.
Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various fea...
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ISBN:
(数字)9798350366846
ISBN:
(纸本)9798350366853
Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes—Low Performance Student, Average Student, and Smart Student—were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments.
In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on bui...
In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on building model that predicted the performance of computer science students in some courses they were examined through classification technique. The model built for the purpose of classification via decision tree classifier which was adopted in WEKA and KNIME analytics platform. The workflow model basically connected nodes to generate the decision trees while J4.8 Decision Tree Classifier programmable outputs were generated for the courses to obtain the confusion matrix and other instances. Bar Charts were plotted to display the students' performance in the courses. The mean absolute errors, RMS error and relative absolute error all read 0% while the kappa value obtained from the analysis ranged between 0.991 – 1.00 which perfectly agrees for most statistical values.
The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets...
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The nonlinear adaptation algorithm is considered as applied to stabilization of the center of mass of a moving object subject to nonrandom noise in the control variable. An unknown disturbance is a bounded and continu...
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The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition ...
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