Investigating Persistence in the US Mutual Fund Market: A Mobility Approach

Performance persistence in the US mutual fund market is investigated, modeling risk-adjusted performance as a Markov Chain. This allows us to explore whether there is a higher probability for funds to remain in their initial ranking, compared to the probability that funds exhibit some kind of movement. We find some degree of inertia due to non-uniformity of transition probabilities across states. Our analysis allows also assesses the proximity of empirical transition matrices to two benchmark matrices, identifying the no-persistence/perfect immobility cases. We find that the observed transition matrices are closer to the no-persistence benchmark and also that performance persistence has decreased over time

Effect of Pt nanoparticle decoration on the H2 storage performance of plasma-derived nanoporous graphene

A nanoporous and large surface area (∼800 m2/g) graphene-based material was produced by plasma treatment of natural flake graphite and was subsequently surface decorated with platinum (Pt) nano-sized particles via thermal reduction of a Pt precursor (chloroplatinic acid). The carbon-metal nanocomposite showed a ∼2 wt% loading of well-dispersed Pt nanoparticles (<2 nm) across its porous graphene surface, while neither a significant surface chemistry alteration nor a pore structure degradation was observed due to the Pt decoration procedure. The presence of Pt seems to slightly promote the hydrogen sorption behavior at room temperature with respect to the pure graphene, thus implying the rise of “weak” chemisorption phenomena, including a potential hydrogen “spillover” effect. The findings of this experimental study provide insights for the development of novel graphene-based nanocomposites for hydrogen storage applications at ambient conditions

Speaker Verification based on extraction of Deep Features

In this paper we present an approach for speaker verification, based on the the extraction of deep features. More specifically, we propose a scheme that is based on a convolutional neural network. For audio representation we opt for spectrograms, i.e., images that result from the spectral content of voices. Our network is trained to extract visual features from these spectrograms. We demonstrate that our network is able to produce discriminative features for the problem at hand, and moreover, when transfer learning is used, few samples may be needed for accurate speaker verification

Inverse Design of ZIFs through Artificial Intelligence Methods

Artificial Intelligence (AI) benefits research on membrane separations by facilitating fast and accurate performance predictions of a given material. However, the potential of AI to work backwards, towards predicting/designing a finetuned material for a given separation, remains untapped. Recent works report the inverse design of functionalized materials, such as metal-organic frameworks (MOFs), but they are limited to targeted sorption properties, while diffusivity, D, which is the driving force in membrane-based separations, is omitted. Herein, we report a tool combining a biologically inspired evolutionary algorithm with machine learning to design fine-tuned Zeolitic-Imidazolate Frameworks (ZIFs), a sub-family of MOFs, for desired sets of diffusivities (Di, Di/Dj) values of any given mixture of species i and j. We moreover display the efficacy of our tool, by designing ZIFs that meet industrial performance criteria of permeability and selectivity, for CO2/CH4, O2/N2 and C3H6/C3H8 mixtures. We validate the designed ZIFs through appropriate simulations, confirming the suitability of the AI-suggested ZIF designs

Deep learned features for image retrieval

A novel approach for feature learning using deep learning is presented. More specifically, a Convolutional Neural Network that is trained using feature correspondences learns to map a given image patch to a descriptor. Therefore, descriptors are directly learned from examples instead of being hand-crafted. The proposed approach is evaluated in a challenging image retrieval dataset