Generalised Decision Level Ensemble Method for Classifying Multi-media Data

In recent decades, multimedia data have been commonly generated and used in various domains, such as in healthcare and social media due to their ability of capturing rich information. But as they are unstructured and separated, how to fuse and integrate multimedia datasets and then learn from them eectively have been a main challenge to machine learning. We present a novel generalised decision level ensemble method (GDLEM) that combines the multimedia datasets at decision level. After extracting features from each of multimedia datasets separately, the method trains models independently on each media dataset and then employs a generalised selection function to choose the appropriate models to construct a heterogeneous ensemble. The selection function is dened as a weighted combination of two criteria: the accuracy of individual models and the diversity among the models. The framework is tested on multimedia data and compared with other heterogeneous ensembles. The results show that the GDLEM is more exible and eective

Condensates and pressure of two-flavor chiral perturbation theory at nonzero isospin and temperature

We consider two-flavor chiral perturbation theory ($\chi$PT) at finite isospin chemical potential $\mu_I$ and finite temperature $T$. We calculate the effective potential and the quark and pion condensates as functions of $T$ and $\mu_I$ to next-to-leading order in the low-energy expansion in the presence of a pionic source. We map out the phase diagram in the $\mu_I$--$T$ plane. Numerically, we find that the transition to the pion-condensed phase is second order in the region of validity of $\chi$PT, which is in agreement with model calculations and lattice simulations. Finally, we calculate the pressure to two-loop order in the symmetric phase for nonzero $\mu_I$ and find that $\chi$PT seems to be converging very well.Comment: 11 pages and 6 figures, LaTeX; typos corrected, references adde

Assessing the Dissipative Capacity of Particle Impact Dampers Based on their Nonlinear Bandwidth Characteristics

The dissipative capacity as quantified by the nonlinear bandwidth measure of impulsively loaded primary structures (PSs) coupled to particle impact dampers (PIDs) is assessed. The considered PIDs are designed by initially placing different numbers of spherical, linearly viscoelastic granules at different 2D initial topologies and clearances. The strongly nonlinear and highly discontinuous dynamics of the PIDs are simulated via the discrete element method taking Hertzian interactions, slipping friction and granular rotations into account. The general definition of nonlinear bandwidth is used to evaluate the energy dissipation capacity of the integrated PS-PID systems. Moreover, the effect of the dynamics of the PIDs on the time-bandwidth product of these systems is studied, as a measure of their capacity to store or dissipate vibration energy. It is found that the initial topologies of the granules in the PID drastically affect the time-bandwidth product, which, depending on shock intensity, may break the classical limit of unity which holds for linear time-invariant dissipative resonators. The optimal PS-PID systems composed of multiple granules produce large nonlinear bandwidths, indicating strong dissipative capacity of broadband input energy by the PIDs. Additionally, in the optimal configurations, the time-bandwidth product, i.e., the measure of the frequency bandwidth of the input shock that is stored in the PS-PID system, in tandem with the amount of time it takes for the system to dissipate (1/e) of the initial energy, can be tuned either above or below unity by varying the applied shock intensity. The implications of these findings on the dissipative capacity of the system considered are discussed, showing that it can be predictively assessed so that PIDs can act as highly effective nonlinear energy sinks capable of rapid and efficient suppression of vibration induced by shocks

Decision level ensemble method for classifying multi-media data

In the digital era, the data, for a given analytical task, can be collected in different formats, such as text, images and audio etc. The data with multiple formats are called multimedia data. Integrating and fusing multimedia datasets has become a challenging task in machine learning and data mining. In this paper, we present heterogeneous ensemble method that combines multi-media datasets at the decision level. Our method consists of several components, including extracting the features from multimedia datasets that are not represented by features, modelling independently on each of multimedia datasets, selecting models based on their accuracy and diversity and building the ensemble at the decision level. Hence our method is called decision level ensemble method (DLEM). The method is tested on multimedia data and compared with other heterogeneous ensemble based methods. The results show that the DLEM outperformed these methods significantly

Renal primitive neuroectodermal tumor: does age at diagnosis impact outcomes?

Primitive neuroectodermal tumor (PNET) of the kidney is a rare and highly malignant neoplasm. The median age for renal PNET is 27 years but it can be seen also in a wide age range between 3 and 78 years. We performed a Medline search for the term renal PNET and identified 79 cases up till December of 2010. We report here a new case of renal PNET and a literature review for published data for evaluation of clinicopathological prognostic factors, with an emphasis on prognosis in two groups of adults and children-adolescents: 18 years of age or under and over 18 years

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

This paper introduces Hk-medoids, a modified version of the standard k-medoids algorithm. The modification extends the algorithm for the problem of clustering complex heterogeneous objects that are described by a diversity of data types, e.g. text, images, structured data and time series. We first proposed an intermediary fusion approach to calculate fused similarities between objects, SMF, taking into account the similarities between the component elements of the objects using appropriate similarity measures. The fused approach entails uncertainty for incomplete objects or for objects which have diverging distances according to the different component. Our implementation of Hk-medoids proposed here works with the fused distances and deals with the uncertainty in the fusion process. We experimentally evaluate the potential of our proposed algorithm using five datasets with different combinations of data types that define the objects. Our results show the feasibility of the our algorithm, and also they show a performance enhancement when comparing to the application of the original SMF approach in combination with a standard k-medoids that does not take uncertainty into account. In addition, from a theoretical point of view, our proposed algorithm has lower computation complexity than the popular PAM implementation

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges

Quark, pion and axial condensates in three-flavor finite isospin chiral perturbation theory

We calculate the light-quark condensate, the strange-quark condensate, the pion condensate, and the axial condensate in three-flavor chiral perturbation theory ($$\chi$$PT) in the presence of an isospin chemical potential at next-to-leading order at zero temperature. It is shown that the three-flavor $$\chi$$PT effective potential and condensates can be mapped onto two-flavor $$\chi$$PT ones by integrating out mesons with strange-quark content (kaons and eta), with renormalized couplings. We compare the results for the light-quark and pion condensates at finite pseudoscalar source with ($$2+1$$)-flavor lattice QCD, and we also compare the axial condensate at zero pseudoscalar and axial sources with lattice QCD data. We find that the light-quark, pion, and axial condensates are in very good agreement with lattice data. There is an overall improvement by including NLO effects

Exceeding the classical time-bandwidth product in nonlinear time-invariant systems

The classical “time-bandwidth” limit for linear time-invariant (LTI) devices in physics and engineering asserts that it is impossible to store broadband propagating waves (large Δ ω’s) for long times (large Δt’s). For standing (non-propagating) waves, i.e., vibrations, in particular, this limit takes on a simple form, ΔtΔω=1, where Δ ω is the bandwidth over which localization (energy storage) occurs, and Δ t is the storage time. This is related to a well-known result in dynamics, namely that one can achieve a high Q-factor (narrowband resonance) for low damping, or small Q-factor (broadband resonance) for high damping, but not simultaneously both. It thus remains a fundamental challenge in classical wave physics and vibration engineering to try to find ways to overcome this limit, not least because that would allow for storing broadband waves for long times, or achieving broadband resonance for low damping. Recent theoretical studies have suggested that such a feat might be possible in LTI terminated unidirectional waveguides or LTI topological “rainbow trapping” devices, although an experimental confirmation of either concept is still lacking. In this work, we consider a nonlinear but time-invariant mechanical system and demonstrate experimentally that its time-bandwidth product can exceed the classical time-bandwidth limit, thus achieving values both above and below unity, in an energy-tunable way. Our proposed structure consists of a single-degree-of-freedom nonlinear oscillator, rigidly coupled to a nondispersive waveguide. Upon developing the full theoretical framework for this class of nonlinear systems, we show how one may control the nonlinear flow of energy in the frequency domain, thereby managing to disproportionately decrease (increase) Δ t, the storage time in the resonator, as compared with an increase (decrease) of the system’s bandwidth Δ ω. Our results pave the way toward conceiving and harnessing hitherto unattainable broadband and simultaneously low-loss wave-storage devices, both linear and nonlinear, for a host of key applications in wave physics and engineering. © 2022, The Author(s), under exclusive licence to Springer Nature B.V