This paper considers MEP - Mixed Exponential Polynomials as one class of real exponential polynomials. We introduce a method for proving the positivity of MEP inequalities over positive intervals using the Maclaurin s...
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This study focuses on the design and the implementation of a digital environment aimed at fostering strategic planning competence in problem-solving through individualization features: the Individualized Planned Strat...
This study focuses on the design and the implementation of a digital environment aimed at fostering strategic planning competence in problem-solving through individualization features: the Individualized Planned Strategy Environment (IPSE). Within IPSE, students are engaged in a sequence of oriented activities, guiding them in constructing and following a theoretically justified plan for solving a mathematical problem, thus promoting a gradual integration between conceptual and procedural knowledge. IPSE envisages also meta-level activities, aimed at fostering the handling of multiple representations toward a unifying and structural view of the subject at stake. We discuss the results of a case study conducted with engineering freshmen at the University of Salerno, involved in problem-solving activities devoted to peer assessment. This led us to identify certain student profiles both theory- and data-driven, according to the students’ progress in using the components of Habermas’ rationality when solving a problem. We highlighted that some students show a full realization of the dynamic nature of Habermas’ model of rationality, where knowing, acting and communicating interact and intertwine.
Efficient quantum repeaters are needed to combat photon losses in fibers in future quantum networks. Single atom coupled with photonic cavity offer a great platform for photon-atom gate. Here I propose a quantum repea...
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Nonlinear negative imaginary systems find application in a range of engineering fields, including the control of flexible structures and air vehicles. Nevertheless, unlike their linear counterparts, the theory for non...
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ISBN:
(数字)9798350374261
ISBN:
(纸本)9798350374278
Nonlinear negative imaginary systems find application in a range of engineering fields, including the control of flexible structures and air vehicles. Nevertheless, unlike their linear counterparts, the theory for nonlinear negative imaginary systems is not as well-established. In this paper, we propose a generalized k-th order dissipativity framework with respect to a supply rate which is a function of the k-th time-derivative of the system output. It is shown that positive realness and negative imaginaryness can be defined in this general framework in a unified manner. Then, necessary and sufficient conditions for first order dissipativity of nonlinear systems are obtained. These capture and are more general than the negative imaginary property. Moreover, the concept of exponentially negative imaginary systems for both linear and nonlinear systems is developed and the required conditions are obtained.
This paper aims to achieve scalable exact output synchronization for discrete-time homogeneous multi-agent systems in the presence of disturbances and measurement noise with known frequencies. The key contribution of ...
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Evaluating the performance of machine learning models under distribution shifts is challenging, especially when we only have unlabeled data from the shifted (target) domain, along with labeled data from the original (...
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The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low-temperature phase is expected to lack magnetic degrees of freedom, as ...
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The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low-temperature phase is expected to lack magnetic degrees of freedom, as vanadium atoms are thought to form singlets upon dimerization of the vanadium chains. Instead, we find a paramagnetic SSE response in VO2 films that grows as the temperature decreases below 50 K. The field and temperature-dependent SSE voltage is qualitatively consistent with a general model of paramagnetic SSE response and inconsistent with triplet spin transport. Quantitative estimates find a spin Seebeck coefficient comparable in magnitude to that observed in strongly magnetic materials. The microscopic nature of the magnetic excitations in VO2 requires further examination.
In this paper, we propose a programmable SoC-based hardware framework that can be used for developing a complete clinical portable ultrasound scanning (PUS) system for remote healthcare, point-of-care diagnostics and ...
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All-optical ultrasound is an emerging minimally invasive imaging technique widely used in biomedical applications, such as intravascular diagnostics and high-resolution photoacoustic endoscopy, requiring miniaturized ...
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ISBN:
(数字)9798350369250
ISBN:
(纸本)9798350369267
All-optical ultrasound is an emerging minimally invasive imaging technique widely used in biomedical applications, such as intravascular diagnostics and high-resolution photoacoustic endoscopy, requiring miniaturized probes with high sensitivity. In this context, Lab-on-fiber 3D micro-structures are becoming particularly attractive thanks to their small size and easy integration in minimally invasive endoscopic tools. Considering the advancements that the Lab-on-Fiber is continuously experiencing in terms of fabrication techniques, here we propose a comparison concerning design flexibility and performances of two different polymeric ultrasound sensors integrated on the fiber tip: a polymeric multilayer configuration and a membrane-based micro-structure. The former can be realized by means of the spin coating technique, while the latter with the Two-Photon-Polymerization technique. Both configurations have been designed and studied through Finite Element Method, evaluating their performances as ultrasound detectors in terms of sensitivity and bandwidth. Furthermore, their performances have been also compared to those obtained with a third configuration based on a plano-concave Fabry-Perot, which is currently the benchmark for this kind of sensor. Interestingly, the multilayer configuration introduces additional degrees of freedom in the design allowing for improving the optical behaviour, while the membrane configuration exhibits a resonant acoustic response that strongly enhances the sensitivity at high frequencies.
When learning multiple tasks in a sequence, deep neural networks tend to loose accuracy on tasks learned in the past while gaining accuracy on the current task. This phenomenon is called catastrophic forgetting. Memor...
When learning multiple tasks in a sequence, deep neural networks tend to loose accuracy on tasks learned in the past while gaining accuracy on the current task. This phenomenon is called catastrophic forgetting. Memory-based Class Incremental Learning (CIL) methods address this problem by re-learning exemplars retained in the memory from previous tasks. However, due to data imbalances between the training data for the current task and the limited exemplars from previous tasks, existing methods struggle to balance the accuracy across all seen tasks. Here, we propose to address data imbalance and in addition to a generic model to learn a set of task-specific parameters. In particular, we propose a novel methodology of Tangent Kernel for Incremental Learning (TKIL) that seeks an equilibrium between current and previous representations. Specifically, TKIL achieves such equilibrium by tuning different task-specific parameters for different tasks with a new Gradient Tangent Kernel (GTK) loss. Therefore, when representing previous tasks, task-specific models are not impacted by the samples of the current task and are able to retain learned representations. As a result, TKIL equally considers the contribution from all task models. The generalized parameters that TKIL obtains allow it to automatically identify which task is being considered and to adapt to it during inference. Extensive experiments on five CIL benchmark datasets with ten incremental learning settings show that TKIL outperforms existing state-of-the-art methods, e.g., achieving 9.4% boost on CIFAR-100 with 25 incremental stages.
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